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Special Issue "Sensors for Agriculture"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 May 2016).

Special Issue Editor

Guest Editor
Prof. Dr. Simon X. Yang Website E-Mail
Advanced Robotics & Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Interests: electronic noses; smart sensors; sensor signal processing; multi-sensor fusion; sensor networks; robotics; intelligent systems; control systems; intelligent transportation; systems modeling and analysis

Special Issue Information

Dear Colleagues,

More and more sensors have been used in various aspects of agriculture. Precision and facility agriculture has been the trend in modern agriculture for high-quality products, high productivity, environmental sustainability and low cost. More and more diversified sensors have been used in various stages of agriculture for information acquisition, effective monitoring and decision-making. Various sensors are essential important for science-based approaches to smart and precision agriculture.

This Special Issue is devoted to new activities on sensor development, sensor signal processing, sensor based monitoring, and decision making in various stages of agriculture. The topics in this issue include, but not limited to, sensor design and development for information acquisition in agriculture, sensor signal processing in agriculture, sensor devices and multi-sensor fusion for agriculture, sensors based monitoring and decision making, modelling and analysis of agricultural sensor systems, sensor information processing and software development for agriculture, remote sensing and monitoring for agriculture, and machine vision and image processing in agriculture.

Prof. Dr. Simon X. Yang
Guest Editor

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Keywords

  • Agricultural sensors
  • Agriculture information acquisition
  • Agriculture sensor information processing
  • Sensors-based decision making in agriculture

Published Papers (45 papers)

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Open AccessArticle
Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System
Sensors 2017, 17(3), 447; https://doi.org/10.3390/s17030447 - 23 Feb 2017
Cited by 13
Abstract
Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation and other management processes still have great blindness. They usually use the same pattern [...] Read more.
Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation and other management processes still have great blindness. They usually use the same pattern and the same formula rather than considering the orchard terrain features, soil differences, species characteristics and the state of tree growth. With the help of the ZigBee technology, artificial intelligence and decision support technology, this paper has developed the research on the application technology of agricultural Internet of Things for real-time monitoring of citrus soil moisture and nutrients as well as the research on the integration of fertilization and irrigation decision support system. Some achievements were obtained including single-point multi-layer citrus soil temperature and humidity detection wireless sensor nodes and citrus precision fertilization and irrigation management decision support system. They were applied in citrus base in the Three Gorges Reservoir Area. The results showed that the system could help the grower to scientifically fertilize or irrigate, improve the precision operation level of citrus production, reduce the labor cost and reduce the pollution caused by chemical fertilizer. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Relative Estimation of Water Content for Flat-Type Inductive-Based Oil Palm Fruit Maturity Sensor
Sensors 2017, 17(1), 52; https://doi.org/10.3390/s17010052 - 28 Dec 2016
Cited by 2
Abstract
The paper aims to study the sensor that identifies the maturity of oil palm fruit bunches by using a flat-type inductive concept based on a resonant frequency technique. Conventionally, a human grader is used to inspect the ripeness of the oil palm fresh [...] Read more.
The paper aims to study the sensor that identifies the maturity of oil palm fruit bunches by using a flat-type inductive concept based on a resonant frequency technique. Conventionally, a human grader is used to inspect the ripeness of the oil palm fresh fruit bunch (FFB) which can be inconsistent and inaccurate. There are various new methods that are proposed with the intention to grade the ripeness of the oil palm FFB, but none has taken the inductive concept. In this study, the resonance frequency of the air coil is investigated. Samples of oil palm FFB are tested with frequencies ranging from 20 Hz to 10 MHz and the results obtained show a linear relationship between the graph of the resonance frequency (MHz) against time (Weeks). It is observed that the resonance frequencies obtained for Week 10 (pre-mature) and Week 18 (mature) are around 8.5 MHz and 9.8 MHz, respectively. These results are compared with the percentage of the moisture content. Hence, the inductive method of the oil palm fruit maturity sensor can be used to detect the change in water content for ripeness detection of the oil palm FFB. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Nondestructive In Situ Measurement Method for Kernel Moisture Content in Corn Ear
Sensors 2016, 16(12), 2196; https://doi.org/10.3390/s16122196 - 20 Dec 2016
Cited by 3
Abstract
Moisture content is an important factor in corn breeding and cultivation. A corn breed with low moisture at harvest is beneficial for mechanical operations, reduces drying and storage costs after harvesting and, thus, reduces energy consumption. Nondestructive measurement of kernel moisture in an [...] Read more.
Moisture content is an important factor in corn breeding and cultivation. A corn breed with low moisture at harvest is beneficial for mechanical operations, reduces drying and storage costs after harvesting and, thus, reduces energy consumption. Nondestructive measurement of kernel moisture in an intact corn ear allows us to select corn varieties with seeds that have high dehydration speeds in the mature period. We designed a sensor using a ring electrode pair for nondestructive measurement of the kernel moisture in a corn ear based on a high-frequency detection circuit. Through experiments using the effective scope of the electrodes’ electric field, we confirmed that the moisture in the corn cob has little effect on corn kernel moisture measurement. Before the sensor was applied in practice, we investigated temperature and conductivity effects on the output impedance. Results showed that the temperature was linearly related to the output impedance (both real and imaginary parts) of the measurement electrodes and the detection circuit’s output voltage. However, the conductivity has a non-monotonic dependence on the output impedance (both real and imaginary parts) of the measurement electrodes and the output voltage of the high-frequency detection circuit. Therefore, we reduced the effect of conductivity on the measurement results through measurement frequency selection. Corn moisture measurement results showed a quadric regression between corn ear moisture and the imaginary part of the output impedance, and there is also a quadric regression between corn kernel moisture and the high-frequency detection circuit output voltage at 100 MHz. In this study, two corn breeds were measured using our sensor and gave R2 values for the quadric regression equation of 0.7853 and 0.8496. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks
Sensors 2016, 16(11), 1934; https://doi.org/10.3390/s16111934 - 18 Nov 2016
Cited by 4
Abstract
With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important [...] Read more.
With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed. When the unknown node is inside positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is outside positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt
Sensors 2016, 16(11), 1884; https://doi.org/10.3390/s16111884 - 09 Nov 2016
Cited by 53
Abstract
Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental [...] Read more.
Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Multi-Domain SDN Survivability for Agricultural Wireless Sensor Networks
Sensors 2016, 16(11), 1861; https://doi.org/10.3390/s16111861 - 06 Nov 2016
Cited by 4
Abstract
Wireless sensor networks (WSNs) have been widely applied in agriculture field; meanwhile, the advent of multi-domain software-defined networks (SDNs) have improved the wireless resource utilization rate and strengthened network management. In recent times, multi-domain SDNs have been applied to agricultural sensor networks, namely [...] Read more.
Wireless sensor networks (WSNs) have been widely applied in agriculture field; meanwhile, the advent of multi-domain software-defined networks (SDNs) have improved the wireless resource utilization rate and strengthened network management. In recent times, multi-domain SDNs have been applied to agricultural sensor networks, namely multi-domain software-defined wireless sensor networks (SDWSNs). However, when the SDNs controlling agriculture networks suddenly become unavailable, whether intra-domain or inter-domain, sensor network communication is abnormal because of the loss of control. Moreover, there are controller and switch info-updating problems even if the controller becomes available again. To resolve these problems, this paper proposes a new approach based on an Open vSwitch extension for multi-domain SDWSNs, which can enhance agriculture network survivability and stability. We achieved this by designing a connection-state mechanism, a communication mechanism on both L2 and L3, and an info-updating mechanism based on Open vSwitch. The experimental results show that, whether it is agricultural inter-domain or intra-domain during the controller failure period, the sensor switches can enter failure recovery mode as soon as possible so that the sensor network keeps a stable throughput, a short failure recovery time below 300 ms, and low packet loss. Further, the domain can smoothly control the domain network again once the controller becomes available. This approach based on an Open vSwitch extension can enhance the survivability and stability of multi-domain SDWSNs in precision agriculture. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging
Sensors 2016, 16(11), 1753; https://doi.org/10.3390/s16111753 - 27 Oct 2016
Cited by 4
Abstract
The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its [...] Read more.
The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
A Portable Farmland Information Collection System with Multiple Sensors
Sensors 2016, 16(10), 1762; https://doi.org/10.3390/s16101762 - 22 Oct 2016
Cited by 4
Abstract
Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture—efficient use of agricultural resources, and improving the crop yields [...] Read more.
Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture—efficient use of agricultural resources, and improving the crop yields and quality—some necessary field information in crop growth environment needs to be collected and monitored. In this paper, a farmland information collection system is developed, which includes a portable farmland information collection device based on STM32 (a 32-bit comprehensive range of microcontrollers based on ARM Crotex-M3), a remote server and a mobile phone APP. The device realizes the function of portable and mobile collecting of multiple parameters farmland information, such as chlorophyll content of crop leaves, air temperature, air humidity, and light intensity. UM220-III (Unicore Communication Inc., Beijing, China) is used to realize the positioning based on BDS/GPS (BeiDou Navigation Satellite System, BDS/Global Positioning System, GPS) dual-mode navigation and positioning system, and the CDMA (Code Division Multiple Access, CDMA) wireless communication module is adopted to realize the real-time remote transmission. The portable multi-function farmland information collection system is real-time, accurate, and easy to use to collect farmland information and multiple information parameters of crops. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Testing the Suitability of a Terrestrial 2D LiDAR Scanner for Canopy Characterization of Greenhouse Tomato Crops
Sensors 2016, 16(9), 1435; https://doi.org/10.3390/s16091435 - 06 Sep 2016
Cited by 10
Abstract
Canopy characterization is essential for pesticide dosage adjustment according to vegetation volume and density. It is especially important for fresh exportable vegetables like greenhouse tomatoes. These plants are thin and tall and are planted in pairs, which makes their characterization with electronic methods [...] Read more.
Canopy characterization is essential for pesticide dosage adjustment according to vegetation volume and density. It is especially important for fresh exportable vegetables like greenhouse tomatoes. These plants are thin and tall and are planted in pairs, which makes their characterization with electronic methods difficult. Therefore, the accuracy of the terrestrial 2D LiDAR sensor is evaluated for determining canopy parameters related to volume and density and established useful correlations between manual and electronic parameters for leaf area estimation. Experiments were performed in three commercial tomato greenhouses with a paired plantation system. In the electronic characterization, a LiDAR sensor scanned the plant pairs from both sides. The canopy height, canopy width, canopy volume, and leaf area were obtained. From these, other important parameters were calculated, like the tree row volume, leaf wall area, leaf area index, and leaf area density. Manual measurements were found to overestimate the parameters compared with the LiDAR sensor. The canopy volume estimated with the scanner was found to be reliable for estimating the canopy height, volume, and density. Moreover, the LiDAR scanner could assess the high variability in canopy density along rows and hence is an important tool for generating canopy maps. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
An H2S Sensor Based on Electrochemistry for Chicken Coops
Sensors 2016, 16(9), 1398; https://doi.org/10.3390/s16091398 - 31 Aug 2016
Cited by 4
Abstract
The recent modernization of the livestock industry lags behind the scale of the livestock industry, particularly in indoor environmental monitoring. In particular, the H2S gas concentration in chicken coops affects the growth and reproductive capacity of the chickens and threatens their [...] Read more.
The recent modernization of the livestock industry lags behind the scale of the livestock industry, particularly in indoor environmental monitoring. In particular, the H2S gas concentration in chicken coops affects the growth and reproductive capacity of the chickens and threatens their health. Therefore, the research and development of a low-cost, environmentally friendly sensor that can achieve on-line monitoring of H2S gas has a notably important practical significance. This paper reports the design of an H2S gas sensor, with selection of an electrochemical probe with high accuracy and wide measurement range using the relatively mature technology of electrochemical sensors. Although the probe of the sensor is the main factor that affects the sensor accuracy, the probe must be combined with a specifically designed signal condition circuit that can overcome the lack of an electrode to satisfy the requirements for the interconnection and matching between the output signal and the test instrument. Because the output current of the electrochemical electrode is small and likely to be disturbed by noise, we designed signal-conditioning modules. Through the signal-conditioning circuit, the output signal of the current electrode can be converted into a voltage and amplified. In addition, we designed a power control module because a bias voltage is necessary for the electrode. Finally, after the calibration experiment, the accurate concentration of H2S gas can be measured. Based on the experimental analysis, the sensor shows good linearity and selectivity, comparatively high sensitivity, perfect stability and an extremely long operating life of up to two years. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis
Sensors 2016, 16(9), 1375; https://doi.org/10.3390/s16091375 - 27 Aug 2016
Cited by 13
Abstract
As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence [...] Read more.
As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest biomass so that the appropriate online adjustments to parameters can be made in time to ensure process optimization and product quality. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Colour-Based Binary Discrimination of Scarified Quercus robur Acorns under Varying Illumination
Sensors 2016, 16(8), 1319; https://doi.org/10.3390/s16081319 - 18 Aug 2016
Cited by 7
Abstract
Efforts to predict the germination ability of acorns using their shape, length, diameter and density are reported in the literature. These methods, however, are not efficient enough. As such, a visual assessment of the viability of seeds based on the appearance of cross-sections [...] Read more.
Efforts to predict the germination ability of acorns using their shape, length, diameter and density are reported in the literature. These methods, however, are not efficient enough. As such, a visual assessment of the viability of seeds based on the appearance of cross-sections of seeds following their scarification is used. This procedure is more robust but demands significant effort from experienced employees over a short period of time. In this article an automated method of acorn scarification and assessment has been announced. This type of automation requires the specific setup of a machine vision system and application of image processing algorithms for evaluation of sections of seeds in order to predict their viability. In the stage of the analysis of pathological changes, it is important to point out image features that enable efficient classification of seeds in respect of viability. The article shows the results of the binary separation of seeds into two fractions (healthy or spoiled) using average components of regular red-green-blue and perception-based hue-saturation-value colour space. Analysis of accuracy of discrimination was performed on sections of 400 scarified acorns acquired using two various setups: machine vision camera under uncontrolled varying illumination and commodity high-resolution camera under controlled illumination. The accuracy of automatic classification has been compared with predictions completed by experienced professionals. It has been shown that both automatic and manual methods reach an accuracy level of 84%, assuming that the images of the sections are properly normalised. The achieved recognition ratio was higher when referenced to predictions provided by professionals. Results of discrimination by means of Bayes classifier have been also presented as a reference. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Low-Cost Soil Moisture Profile Probe Using Thin-Film Capacitors and a Capacitive Touch Sensor
Sensors 2016, 16(8), 1292; https://doi.org/10.3390/s16081292 - 15 Aug 2016
Cited by 10
Abstract
Soil moisture is an important property for agriculture, but currently commercialized soil moisture sensors are too expensive for many farmers. The objective of this study is to develop a low-cost soil moisture sensor using capacitors on a film substrate and a capacitive touch [...] Read more.
Soil moisture is an important property for agriculture, but currently commercialized soil moisture sensors are too expensive for many farmers. The objective of this study is to develop a low-cost soil moisture sensor using capacitors on a film substrate and a capacitive touch integrated circuit. The performance of the sensor was evaluated in two field experiments: a grape field and a mizuna greenhouse field. The developed sensor captured dynamic changes in soil moisture at 10, 20, and 30 cm depth, with a period of 10–14 days required after sensor installation for the contact between capacitors and soil to settle down. The measured soil moisture showed the influence of individual sensor differences, and the influence masked minor differences of less than 0.05 m3·m−3 in the soil moisture at different locations. However, the developed sensor could detect large differences of more than 0.05 m3·m−3, as well as the different magnitude of changes, in soil moisture. The price of the developed sensor was reduced to 300 U.S. dollars and can be reduced even more by further improvements suggested in this study and by mass production. Therefore, the developed sensor will be made more affordable to farmers as it requires low financial investment, and it can be utilized for decision-making in irrigation. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Optical Inspection and Morphological Analysis of Diospyros kaki Plant Leaves for the Detection of Circular Leaf Spot Disease
Sensors 2016, 16(8), 1282; https://doi.org/10.3390/s16081282 - 12 Aug 2016
Cited by 13
Abstract
The feasibility of using the bio-photonic imaging technique to assess symptoms of circular leaf spot (CLS) disease in Diospyros kaki (persimmon) leaf samples was investigated. Leaf samples were selected from persimmon plantations and were categorized into three groups: healthy leaf samples, infected leaf [...] Read more.
The feasibility of using the bio-photonic imaging technique to assess symptoms of circular leaf spot (CLS) disease in Diospyros kaki (persimmon) leaf samples was investigated. Leaf samples were selected from persimmon plantations and were categorized into three groups: healthy leaf samples, infected leaf samples, and healthy-looking leaf samples from infected trees. Visually non-identifiable reduction of the palisade parenchyma cell layer thickness is the main initial symptom, which occurs at the initial stage of the disease. Therefore, we established a non-destructive bio-photonic inspection method using a 1310 nm swept source optical coherence tomography (SS-OCT) system. These results confirm that this method is able to identify morphological differences between healthy leaves from infected trees and leaves from healthy and infected trees. In addition, this method has the potential to generate significant cost savings and good control of CLS disease in persimmon fields. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
A Miniaturized On-Chip Colorimeter for Detecting NPK Elements
Sensors 2016, 16(8), 1234; https://doi.org/10.3390/s16081234 - 04 Aug 2016
Cited by 3
Abstract
Recently, precision agriculture has become a globally attractive topic. As one of the most important factors, the soil nutrients play an important role in estimating the development of precision agriculture. Detecting the content of nitrogen, phosphorus and potassium (NPK) elements more efficiently is [...] Read more.
Recently, precision agriculture has become a globally attractive topic. As one of the most important factors, the soil nutrients play an important role in estimating the development of precision agriculture. Detecting the content of nitrogen, phosphorus and potassium (NPK) elements more efficiently is one of the key issues. In this paper, a novel chip-level colorimeter was fabricated to detect the NPK elements for the first time. A light source–microchannel photodetector in a sandwich structure was designed to realize on-chip detection. Compared with a commercial colorimeter, all key parts are based on MEMS (Micro-Electro-Mechanical System) technology so that the volume of this on-chip colorimeter can be minimized. Besides, less error and high precision are achieved. The cost of this colorimeter is two orders of magnitude less than that of a commercial one. All these advantages enable a low-cost and high-precision sensing operation in a monitoring network. The colorimeter developed herein has bright prospects for environmental and biological applications. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Practical Application of Electrochemical Nitrate Sensor under Laboratory and Forest Nursery Conditions
Sensors 2016, 16(8), 1190; https://doi.org/10.3390/s16081190 - 28 Jul 2016
Cited by 1
Abstract
The reduction of nitrate leaching to ensure greater protection of groundwater quality has become a global issue. The development of new technologies for more accurate dosing of nitrates helps optimize fertilization programs. This paper presents the practical application of a newly developed electrochemical [...] Read more.
The reduction of nitrate leaching to ensure greater protection of groundwater quality has become a global issue. The development of new technologies for more accurate dosing of nitrates helps optimize fertilization programs. This paper presents the practical application of a newly developed electrochemical sensor designed for in situ quantification of nitrate. To our knowledge, this paper is the first to report the use of electrochemical impedance to determine nitrate concentrations in growing media under forest nursery conditions. Using impedance measurements, the sensor has been tested in laboratory and compared to colorimetric measurements of the nitrate. The developed sensor has been used in water-saturated growing medium and showed good correlation to certified methods, even in samples obtained over a multi-ion fertilisation season. A linear and significant relationship was observed between the resistance and the concentration of nitrates (R2 = 0.972), for a range of concentrations of nitrates. We also observed stability of the sensor after exposure of one month to the real environmental conditions of the forest nursery. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Preservation Mechanism of Chitosan-Based Coating with Cinnamon Oil for Fruits Storage Based on Sensor Data
Sensors 2016, 16(7), 1111; https://doi.org/10.3390/s16071111 - 18 Jul 2016
Cited by 10
Abstract
The chitosan-based coating with antimicrobial agent has been developed recently to control the decay of fruits. However, its fresh keeping and antimicrobial mechanism is still not very clear. The preservation mechanism of chitosan coating with cinnamon oil for fruits storage is investigated in [...] Read more.
The chitosan-based coating with antimicrobial agent has been developed recently to control the decay of fruits. However, its fresh keeping and antimicrobial mechanism is still not very clear. The preservation mechanism of chitosan coating with cinnamon oil for fruits storage is investigated in this paper. Results in the atomic force microscopy sensor images show that many micropores exist in the chitosan coating film. The roughness of coating film is affected by the concentration of chitosan. The antifungal activity of cinnamon oil should be mainly due to its main consistent trans-cinnamaldehyde, which is proportional to the trans-cinnamaldehyde concentration and improves with increasing the attachment time of oil. The exosmosis ratios of Penicillium citrinum and Aspergillus flavus could be enhanced by increasing the concentration of cinnamon oil. Morphological observation indicates that, compared to the normal cell, the wizened mycelium of A. flavus is observed around the inhibition zone, and the growth of spores is also inhibited. Moreover, the analysis of gas sensors indicate that the chitosan-oil coating could decrease the level of O2 and increase the level of CO2 in the package of cherry fruits, which also control the fruit decay. These results indicate that its preservation mechanism might be partly due to the micropores structure of coating film as a barrier for gas and a carrier for oil, and partly due to the activity of cinnamon oil on the cell disruption. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Ultrasonic Sensing of Plant Water Needs for Agriculture
Sensors 2016, 16(7), 1089; https://doi.org/10.3390/s16071089 - 14 Jul 2016
Cited by 2
Abstract
Fresh water is a key natural resource for food production, sanitation and industrial uses and has a high environmental value. The largest water use worldwide (~70%) corresponds to irrigation in agriculture, where use of water is becoming essential to maintain productivity. Efficient irrigation [...] Read more.
Fresh water is a key natural resource for food production, sanitation and industrial uses and has a high environmental value. The largest water use worldwide (~70%) corresponds to irrigation in agriculture, where use of water is becoming essential to maintain productivity. Efficient irrigation control largely depends on having access to reliable information about the actual plant water needs. Therefore, fast, portable and non-invasive sensing techniques able to measure water requirements directly on the plant are essential to face the huge challenge posed by the extensive water use in agriculture, the increasing water shortage and the impact of climate change. Non-contact resonant ultrasonic spectroscopy (NC-RUS) in the frequency range 0.1–1.2 MHz has revealed as an efficient and powerful non-destructive, non-invasive and in vivo sensing technique for leaves of different plant species. In particular, NC-RUS allows determining surface mass, thickness and elastic modulus of the leaves. Hence, valuable information can be obtained about water content and turgor pressure. This work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure. A sensing prototype is proposed, described and, as application example, used to study two different species: Vitis vinifera and Coffea arabica, whose leaves present thickness resonances in two different frequency bands (400–900 kHz and 200–400 kHz, respectively), These species are representative of two different climates and are related to two high-added value agricultural products where efficient irrigation management can be critical. Moreover, the technique can also be applied to other species and similar results can be obtained. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning
Sensors 2016, 16(7), 1044; https://doi.org/10.3390/s16071044 - 07 Jul 2016
Cited by 6
Abstract
Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this [...] Read more.
Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this study, we develop a method for internode length estimation using image processing technology. The proposed method consists of three steps: node detection, node order estimation, and internode length estimation. This method has two main advantages: (i) as it uses machine learning approaches for node detection, it does not require adjustment of threshold values even though seedlings are imaged under varying timings and lighting conditions with complex backgrounds; and (ii) as it uses affinity propagation for node order estimation, it can be applied to seedlings with different numbers of nodes without prior provision of the node number as a parameter. Our node detection results show that the proposed method can detect 72% of the 358 nodes in time-series imaging of three seedlings (recall = 0.72, precision = 0.78). In particular, the application of a general object recognition approach, Bag of Visual Words (BoVWs), enabled the elimination of many false positives on leaves occurring in the image segmentation based on pixel color, significantly improving the precision. The internode length estimation results had a relative error of below 15.4%. These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Developing a Penetrometer-Based Mapping System for Visualizing Silage Bulk Density from the Bunker Silo Face
Sensors 2016, 16(7), 1038; https://doi.org/10.3390/s16071038 - 05 Jul 2016
Cited by 2
Abstract
For silage production, high bulk density (BD) is critical to minimize aerobic deterioration facilitated by oxygen intrusion. To precisely assess packing quality for bunker silos, there is a desire to visualize the BD distribution within the silage. In this study, a penetrometer-based mapping [...] Read more.
For silage production, high bulk density (BD) is critical to minimize aerobic deterioration facilitated by oxygen intrusion. To precisely assess packing quality for bunker silos, there is a desire to visualize the BD distribution within the silage. In this study, a penetrometer-based mapping system was developed. The data processing included filtering of the penetration friction component (PFC) out of the penetration resistance (PR), transfer of the corrected penetration resistance (PRc) to BD, incorporation of Kriged interpolation for data expansion and map generation. The experiment was conducted in a maize bunker silo (width: 8 m, middle height: 3 m). The BD distributions near the bunker silo face were represented using two map groups, one related to horizontal- and the other to vertical-density distribution patterns. We also presented a comparison between the map-based BD results and core sampling data. Agreement between the two measurement approaches (RMSE = 19.175 kg·m−3) demonstrates that the developed penetrometer mapping system may be beneficial for rapid assessment of aerobic deterioration potential in bunker silos. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera
Sensors 2016, 16(7), 1012; https://doi.org/10.3390/s16071012 - 30 Jun 2016
Cited by 9
Abstract
This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and [...] Read more.
This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and projected area. A stepwise multiple linear regression method is applied to select significant variables for predicting minimum diameter of the equatorial section and volume and to establish corresponding estimation models. Results show that length, maximum diameter of the equatorial section and weight are selected to predict the minimum diameter of the equatorial section, with the coefficient of determination of only 0.82 when compared to manual measurements. Weight and length are then selected to estimate the volume, which is in good agreement with the measured one with the coefficient of determination of 0.98. Fruit classification based on the estimated minimum diameter of the equatorial section achieves a low success rate of 84.6%, which is significantly improved using a linear combination of the length/maximum diameter of the equatorial section and projected area/length ratios, reaching 98.3%. Thus, it is possible for Chinese kiwifruit sorting lines to reach international standards of grading kiwifruit on fruit shape classification by adding a single camera. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning
Sensors 2016, 16(7), 998; https://doi.org/10.3390/s16070998 - 29 Jun 2016
Cited by 7
Abstract
The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from [...] Read more.
The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images’ spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Verification of Geometric Model-Based Plant Phenotyping Methods for Studies of Xerophytic Plants
Sensors 2016, 16(7), 924; https://doi.org/10.3390/s16070924 - 27 Jun 2016
Cited by 1
Abstract
This paper presents the results of verification of certain non-contact measurement methods of plant scanning to estimate morphological parameters such as length, width, area, volume of leaves and/or stems on the basis of computer models. The best results in reproducing the shape of [...] Read more.
This paper presents the results of verification of certain non-contact measurement methods of plant scanning to estimate morphological parameters such as length, width, area, volume of leaves and/or stems on the basis of computer models. The best results in reproducing the shape of scanned objects up to 50 cm in height were obtained with the structured-light DAVID Laserscanner. The optimal triangle mesh resolution for scanned surfaces was determined with the measurement error taken into account. The research suggests that measuring morphological parameters from computer models can supplement or even replace phenotyping with classic methods. Calculating precise values of area and volume makes determination of the S/V (surface/volume) ratio for cacti and other succulents possible, whereas for classic methods the result is an approximation only. In addition, the possibility of scanning and measuring plant species which differ in morphology was investigated. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Field Measurements and Numerical Simulations of Temperature and Moisture in Highway Engineering Using a Frequency Domain Reflectometry Sensor
Sensors 2016, 16(6), 857; https://doi.org/10.3390/s16060857 - 10 Jun 2016
Cited by 5
Abstract
This paper presents a systematic pioneering study on the use of agricultural-purpose frequency domain reflectometry (FDR) sensors to monitor temperature and moisture of a subgrade in highway extension and reconstruction engineering. The principle of agricultural-purpose FDR sensors and the process for embedding this [...] Read more.
This paper presents a systematic pioneering study on the use of agricultural-purpose frequency domain reflectometry (FDR) sensors to monitor temperature and moisture of a subgrade in highway extension and reconstruction engineering. The principle of agricultural-purpose FDR sensors and the process for embedding this kind of sensors for subgrade engineering purposes are introduced. Based on field measured weather data, a numerical analysis model for temperature and moisture content in the subgrade’s soil is built. Comparisons of the temperature and moisture data obtained from numerical simulation and FDR-based measurements are conducted. The results show that: (1) the embedding method and process, data acquisition, and remote transmission presented are reasonable; (2) the temperature and moisture changes are coordinated with the atmospheric environment and they are also in close agreement with numerical calculations; (3) the change laws of both are consistent at positions where the subgrade is compacted uniformly. These results suggest that the data measured by the agricultural-purpose FDR sensors are reliable. The findings of this paper enable a new and effective real-time monitoring method for a subgrade’s temperature and moisture changes, and thus broaden the application of agricultural-purpose FDR sensors. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Olive Crown Porosity Measurement Based on Radiation Transmittance: An Assessment of Pruning Effect
Sensors 2016, 16(5), 723; https://doi.org/10.3390/s16050723 - 19 May 2016
Cited by 7
Abstract
Crown porosity influences radiation interception, air movement through the fruit orchard, spray penetration, and harvesting operation in fruit crops. The aim of the present study was to develop an accurate and reliable methodology based on transmitted radiation measurements to assess the porosity of [...] Read more.
Crown porosity influences radiation interception, air movement through the fruit orchard, spray penetration, and harvesting operation in fruit crops. The aim of the present study was to develop an accurate and reliable methodology based on transmitted radiation measurements to assess the porosity of traditional olive trees under different pruning treatments. Transmitted radiation was employed as an indirect method to measure crown porosity in two olive orchards of the Picual and Hojiblanca cultivars. Additionally, three different pruning treatments were considered to determine if the pruning system influences crown porosity. This study evaluated the accuracy and repeatability of four algorithms in measuring crown porosity under different solar zenith angles. From a 14° to 30° solar zenith angle, the selected algorithm produced an absolute error of less than 5% and a repeatability higher than 0.9. The described method and selected algorithm proved satisfactory in field results, making it possible to measure crown porosity at different solar zenith angles. However, pruning fresh weight did not show any relationship with crown porosity due to the great differences between removed branches. A robust and accurate algorithm was selected for crown porosity measurements in traditional olive trees, making it possible to discern between different pruning treatments. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Wind Tunnel Analysis of the Airflow through Insect-Proof Screens and Comparison of Their Effect When Installed in a Mediterranean Greenhouse
Sensors 2016, 16(5), 690; https://doi.org/10.3390/s16050690 - 12 May 2016
Cited by 6
Abstract
The present work studies the effect of three insect-proof screens with different geometrical and aerodynamic characteristics on the air velocity and temperature inside a Mediterranean multi-span greenhouse with three roof vents and without crops, divided into two independent sectors. First, the insect-proof screens [...] Read more.
The present work studies the effect of three insect-proof screens with different geometrical and aerodynamic characteristics on the air velocity and temperature inside a Mediterranean multi-span greenhouse with three roof vents and without crops, divided into two independent sectors. First, the insect-proof screens were characterised geometrically by analysing digital images and testing in a low velocity wind tunnel. The wind tunnel tests gave screen discharge coefficient values of Cd,φ of 0.207 for screen 1 (10 × 20 threads·cm−2; porosity φ = 35.0%), 0.151 for screen 2 (13 × 30 threads·cm−2; φ = 26.3%) and 0.325 for screen 3 (10 × 20 threads·cm−2; porosity φ = 36.0%), at an air velocity of 0.25 m·s−1. Secondly, when screens were installed in the greenhouse, we observed a statistical proportionality between the discharge coefficient at the openings and the air velocity ui measured in the centre of the greenhouse, ui = 0.856 Cd + 0.062 (R2 = 0.68 and p-value = 0.012). The inside-outside temperature difference ΔTio diminishes when the inside velocity increases following the statistically significant relationship ΔTio = (−135.85 + 57.88/ui)0.5 (R2 = 0.85 and p-value = 0.0011). Different thread diameters and tension affects the screen thickness, and means that similar porosities may well be associated with very different aerodynamic characteristics. Screens must be characterised by a theoretical function Cd,φ = [(2/Kpρ)·(1/us) + (2eY/Kp0.5)]−0.5 that relates the discharge coefficient of the screen Cd,φ with the air velocity us. This relationship depends on the three parameters that define the aerodynamic behaviour of porous medium: permeability Kp, inertial factor Y and screen thickness e (and on air temperature that determine its density ρ and viscosity μ). However, for a determined temperature of air, the pressure drop-velocity relationship can be characterised only with two parameters: ΔP = aus2 + bus. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves
Sensors 2016, 16(5), 676; https://doi.org/10.3390/s16050676 - 11 May 2016
Cited by 8
Abstract
This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths [...] Read more.
This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Determination of the Optimum Harvest Window for Apples Using the Non-Destructive Biospeckle Method
Sensors 2016, 16(5), 661; https://doi.org/10.3390/s16050661 - 10 May 2016
Cited by 14
Abstract
Determination of the optimum harvest window plays a key role in the agro-food chain as the quality of fruit depends on the right harvesting time and appropriate storage conditions during the postharvest period. Usually, indices based on destructive measurements are used for this [...] Read more.
Determination of the optimum harvest window plays a key role in the agro-food chain as the quality of fruit depends on the right harvesting time and appropriate storage conditions during the postharvest period. Usually, indices based on destructive measurements are used for this purpose, like the De Jager Index (PFW-1), FARS index and the most popular Streif Index. In this study, we proposed a biospeckle method for the evaluation of the optimum harvest window (OHW) of the “Ligol” and “Szampion” apple cultivars. The experiment involved eight different maturity stages, of which four were followed by long cold storage and shelf life to assist the determination of the optimum harvest window. The biospeckle activity was studied in relation to standard quality attributes (firmness, acidity, starch, soluble solids content, Streif Index) and physiological parameters (respiration and ethylene emission) of both apple cultivars. Changes of biospeckle activity (BA) over time showed moderate relationships with biochemical changes during apple maturation and ripening. The harvest date suggested by the Streif Index and postharvest quality indicators matched with characteristic decrease in BA. The ability of biospeckle method to characterize the biological state of apples was confirmed by significant correlations of BA with firmness, starch index, total soluble solids and Streif Index, as well as good match with changes in carbon dioxide and ethylene emission. However, it should be noted that correlations between variables changing over time are not as meaningful as independent observations. Also, it is a well-known property of the Pearson’s correlation that its value is highly susceptible to outlier data. Due to its non-selective nature the BA reflected only the current biological state of the fruit and could be affected by many other factors. The investigations showed that the optimum harvest window for apples was indicated by the characteristic drop of BA during pre-harvest development. Despite this, at the current state of development the BA method cannot be used as an indicator alone. Due to rather poor results for prediction in OHW the BA measurements should be supported by other destructive methods to compensate its low selectivity. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
A Reliable Wireless Control System for Tomato Hydroponics
Sensors 2016, 16(5), 644; https://doi.org/10.3390/s16050644 - 05 May 2016
Cited by 18
Abstract
Agricultural systems using advanced information and communication (ICT) technology can produce high-quality crops in a stable environment while decreasing the need for manual labor. The system collects a wide variety of environmental data and provides the precise cultivation control needed to produce high [...] Read more.
Agricultural systems using advanced information and communication (ICT) technology can produce high-quality crops in a stable environment while decreasing the need for manual labor. The system collects a wide variety of environmental data and provides the precise cultivation control needed to produce high value-added crops; however, there are the problems of packet transmission errors in wireless sensor networks or system failure due to having the equipment in a hot and humid environment. In this paper, we propose a reliable wireless control system for hydroponic tomato cultivation using the 400 MHz wireless band and the IEEE 802.15.6 standard. The 400 MHz band, which is lower than the 2.4 GHz band, has good obstacle diffraction, and zero-data-loss communication is realized using the guaranteed time-slot method supported by the IEEE 802.15.6 standard. In addition, this system has fault tolerance and a self-healing function to recover from faults such as packet transmission failures due to deterioration of the wireless communication quality. In our basic experiments, the 400 MHz band wireless communication was not affected by the plants’ growth, and the packet error rate was less than that of the 2.4 GHz band. In summary, we achieved a real-time hydroponic liquid supply control with no data loss by applying a 400 MHz band WSN to hydroponic tomato cultivation. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Automatic Recognition of Aggressive Behavior in Pigs Using a Kinect Depth Sensor
Sensors 2016, 16(5), 631; https://doi.org/10.3390/s16050631 - 02 May 2016
Cited by 29
Abstract
Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by [...] Read more.
Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by extracting activity features from the Kinect depth information obtained in a pigsty. The detection and classification module, which employs two binary-classifier support vector machines in a hierarchical manner, detects aggressive activity, and classifies it into aggressive sub-types such as head-to-head (or body) knocking and chasing. Our experimental results showed that this method is effective for detecting aggressive pig behaviors in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (detection and classification accuracies over 95.7% and 90.2%, respectively), either as a standalone solution or to complement existing methods. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping
Sensors 2016, 16(5), 594; https://doi.org/10.3390/s16050594 - 26 Apr 2016
Cited by 10
Abstract
Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total [...] Read more.
Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine
Sensors 2016, 16(4), 585; https://doi.org/10.3390/s16040585 - 23 Apr 2016
Cited by 19
Abstract
Leaf area index (LAI) and plant area index (PAI) are common and important biophysical parameters used to estimate agronomical variables such as canopy growth, light interception and water requirements of plants and trees. LAI can be either measured directly using destructive methods or [...] Read more.
Leaf area index (LAI) and plant area index (PAI) are common and important biophysical parameters used to estimate agronomical variables such as canopy growth, light interception and water requirements of plants and trees. LAI can be either measured directly using destructive methods or indirectly using dedicated and expensive instrumentation, both of which require a high level of know-how to operate equipment, handle data and interpret results. Recently, a novel smartphone and tablet PC application, VitiCanopy, has been developed by a group of researchers from the University of Adelaide and the University of Melbourne, to estimate grapevine canopy size (LAI and PAI), canopy porosity, canopy cover and clumping index. VitiCanopy uses the front in-built camera and GPS capabilities of smartphones and tablet PCs to automatically implement image analysis algorithms on upward-looking digital images of canopies and calculates relevant canopy architecture parameters. Results from the use of VitiCanopy on grapevines correlated well with traditional methods to measure/estimate LAI and PAI. Like other indirect methods, VitiCanopy does not distinguish between leaf and non-leaf material but it was demonstrated that the non-leaf material could be extracted from the results, if needed, to increase accuracy. VitiCanopy is an accurate, user-friendly and free alternative to current techniques used by scientists and viticultural practitioners to assess the dynamics of LAI, PAI and canopy architecture in vineyards, and has the potential to be adapted for use on other plants. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Measurement of Spray Drift with a Specifically Designed Lidar System
Sensors 2016, 16(4), 499; https://doi.org/10.3390/s16040499 - 08 Apr 2016
Cited by 6
Abstract
Field measurements of spray drift are usually carried out by passive collectors and tracers. However, these methods are labour- and time-intensive and only provide point- and time-integrated measurements. Unlike these methods, the light detection and ranging (lidar) technique allows real-time measurements, obtaining information [...] Read more.
Field measurements of spray drift are usually carried out by passive collectors and tracers. However, these methods are labour- and time-intensive and only provide point- and time-integrated measurements. Unlike these methods, the light detection and ranging (lidar) technique allows real-time measurements, obtaining information with temporal and spatial resolution. Recently, the authors have developed the first eye-safe lidar system specifically designed for spray drift monitoring. This prototype is based on a 1534 nm erbium-doped glass laser and an 80 mm diameter telescope, has scanning capability, and is easily transportable. This paper presents the results of the first experimental campaign carried out with this instrument. High coefficients of determination (R2 > 0.85) were observed by comparing lidar measurements of the spray drift with those obtained by horizontal collectors. Furthermore, the lidar system allowed an assessment of the drift reduction potential (DRP) when comparing low-drift nozzles with standard ones, resulting in a DRP of 57% (preliminary result) for the tested nozzles. The lidar system was also used for monitoring the evolution of the spray flux over the canopy and to generate 2-D images of these plumes. The developed instrument is an advantageous alternative to passive collectors and opens the possibility of new methods for field measurement of spray drift. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Non-Invasive Examination of Plant Surfaces by Opto-Electronic Means—Using Russet as a Prime Example
Sensors 2016, 16(4), 452; https://doi.org/10.3390/s16040452 - 29 Mar 2016
Cited by 4
Abstract
(1) Background: Many disorders and diseases of agricultural produce change the physical features of surfaces of plant organs; in terms of russet, e.g., of apple or pear, affected fruit peel becomes rough and brown in color, which is associated with changes in light [...] Read more.
(1) Background: Many disorders and diseases of agricultural produce change the physical features of surfaces of plant organs; in terms of russet, e.g., of apple or pear, affected fruit peel becomes rough and brown in color, which is associated with changes in light reflection; (2) Objective and Methods: The objective of the present project was an interdisciplinary approach between horticultural science and engineering to examine two new innovative technologies as to their suitability for the non-destructive determination of surfaces of plant organs, using russet as an example, and (a) an industrial luster sensor (type CZ-H72, Keyence, Japan) and (b) a new type of a three-dimensional (3D) color microscope (VHX 5000); (3) Results: In the case of russet, i.e., suberinization of the fruit peel, peel roughness increased by ca. 2.5-fold from ca. 20 µm to ca. 50 µm on affected fruit sections when viewed at 200× magnification. Russeted peel showed significantly reduced luster, with smaller variation than russet-devoid peel with larger variation; (4) Conclusion: These results indicate that both sensors are suitable for biological material and their use for non-contact, non-invasive detection of surface disorders on agricultural produce such as russet may be a very powerful tool for many applications in agriculture and beyond in the future. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors
Sensors 2016, 16(4), 437; https://doi.org/10.3390/s16040437 - 25 Mar 2016
Cited by 10
Abstract
The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is [...] Read more.
The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
An Inexpensive, Stable, and Accurate Relative Humidity Measurement Method for Challenging Environments
Sensors 2016, 16(3), 398; https://doi.org/10.3390/s16030398 - 18 Mar 2016
Cited by 2
Abstract
In this research, an improved psychrometer is developed to solve practical issues arising in the relative humidity measurement of challenging drying environments for meat manufacturing in agricultural and agri-food industries. The design in this research focused on the structure of the improved psychrometer, [...] Read more.
In this research, an improved psychrometer is developed to solve practical issues arising in the relative humidity measurement of challenging drying environments for meat manufacturing in agricultural and agri-food industries. The design in this research focused on the structure of the improved psychrometer, signal conversion, and calculation methods. The experimental results showed the effect of varying psychrometer structure on relative humidity measurement accuracy. An industrial application to dry-cured meat products demonstrated the effective performance of the improved psychrometer being used as a relative humidity measurement sensor in meat-drying rooms. In a drying environment for meat manufacturing, the achieved measurement accuracy for relative humidity using the improved psychrometer was ±0.6%. The system test results showed that the improved psychrometer can provide reliable and long-term stable relative humidity measurements with high accuracy in the drying system of meat products. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model
Sensors 2016, 16(3), 340; https://doi.org/10.3390/s16030340 - 07 Mar 2016
Cited by 6
Abstract
Accurate monitoring of heavy metal stress in crops is of great importance to assure agricultural productivity and food security, and remote sensing is an effective tool to address this problem. However, given that Earth observation instruments provide data at multiple scales, the choice [...] Read more.
Accurate monitoring of heavy metal stress in crops is of great importance to assure agricultural productivity and food security, and remote sensing is an effective tool to address this problem. However, given that Earth observation instruments provide data at multiple scales, the choice of scale for use in such monitoring is challenging. This study focused on identifying the characteristic scale for effectively monitoring heavy metal stress in rice using the dry weight of roots (WRT) as the representative characteristic, which was obtained by assimilation of GF-1 data with the World Food Studies (WOFOST) model. We explored and quantified the effect of the important state variable LAI (leaf area index) at various spatial scales on the simulated rice WRT to find the critical scale for heavy metal stress monitoring using the statistical characteristics. Furthermore, a ratio analysis based on the varied heavy metal stress levels was conducted to identify the characteristic scale. Results indicated that the critical threshold for investigating the rice WRT in monitoring studies of heavy metal stress was larger than 64 m but smaller than 256 m. This finding represents a useful guideline for choosing the most appropriate imagery. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Suppression of Strong Background Interference on E-Nose Sensors in an Open Country Environment
Sensors 2016, 16(2), 233; https://doi.org/10.3390/s16020233 - 16 Feb 2016
Cited by 6
Abstract
The feature extraction technique for an electronic nose (e-nose) applied in tobacco smell detection in an open country/outdoor environment with periodic background strong interference is studied in this paper. Principal component analysis (PCA), Independent component analysis (ICA), re-filtering and a priori knowledge are [...] Read more.
The feature extraction technique for an electronic nose (e-nose) applied in tobacco smell detection in an open country/outdoor environment with periodic background strong interference is studied in this paper. Principal component analysis (PCA), Independent component analysis (ICA), re-filtering and a priori knowledge are combined to separate and suppress background interference on the e-nose. By the coefficient of multiple correlation (CMC), it can be verified that a better separation of environmental temperature, humidity, and atmospheric pressure variation related background interference factors can be obtained with ICA. By re-filtering according to the on-site interference characteristics a composite smell curve was obtained which is more related to true smell information based on the tobacco curer’s experience. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Spray Droplet Characterization from a Single Nozzle by High Speed Image Analysis Using an In-Focus Droplet Criterion
Sensors 2016, 16(2), 218; https://doi.org/10.3390/s16020218 - 06 Feb 2016
Cited by 7
Abstract
Accurate spray characterization helps to better understand the pesticide spray application process. The goal of this research was to present the proof of principle of a droplet size and velocity measuring technique for different types of hydraulic spray nozzles using a high speed [...] Read more.
Accurate spray characterization helps to better understand the pesticide spray application process. The goal of this research was to present the proof of principle of a droplet size and velocity measuring technique for different types of hydraulic spray nozzles using a high speed backlight image acquisition and analysis system. As only part of the drops of an agricultural spray can be in focus at any given moment, an in-focus criterion based on the gray level gradient was proposed to decide whether a given droplet is in focus or not. In a first experiment, differently sized droplets were generated with a piezoelectric generator and studied to establish the relationship between size and in-focus characteristics. In a second experiment, it was demonstrated that droplet sizes and velocities from a real sprayer could be measured reliably in a non-intrusive way using the newly developed image acquisition set-up and image processing. Measured droplet sizes ranged from 24 μm to 543 μm, depending on the nozzle type and size. Droplet velocities ranged from around 0.5 m/s to 12 m/s. The droplet size and velocity results were compared and related well with the results obtained with a Phase Doppler Particle Analyzer (PDPA). Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Mapping Vineyard Leaf Area Using Mobile Terrestrial Laser Scanners: Should Rows be Scanned On-the-Go or Discontinuously Sampled?
Sensors 2016, 16(1), 119; https://doi.org/10.3390/s16010119 - 19 Jan 2016
Cited by 8
Abstract
The leaf area index (LAI) is defined as the one-side leaf area per unit ground area, and is probably the most widely used index to characterize grapevine vigor. However, LAI varies spatially within vineyard plots. Mapping and quantifying this variability is very important [...] Read more.
The leaf area index (LAI) is defined as the one-side leaf area per unit ground area, and is probably the most widely used index to characterize grapevine vigor. However, LAI varies spatially within vineyard plots. Mapping and quantifying this variability is very important for improving management decisions and agricultural practices. In this study, a mobile terrestrial laser scanner (MTLS) was used to map the LAI of a vineyard, and then to examine how different scanning methods (on-the-go or discontinuous systematic sampling) may affect the reliability of the resulting raster maps. The use of the MTLS allows calculating the enveloping vegetative area of the canopy, which is the sum of the leaf wall areas for both sides of the row (excluding gaps) and the projected upper area. Obtaining the enveloping areas requires scanning from both sides one meter length section along the row at each systematic sampling point. By converting the enveloping areas into LAI values, a raster map of the latter can be obtained by spatial interpolation (kriging). However, the user can opt for scanning on-the-go in a continuous way and compute 1-m LAI values along the rows, or instead, perform the scanning at discontinuous systematic sampling within the plot. An analysis of correlation between maps indicated that MTLS can be used discontinuously in specific sampling sections separated by up to 15 m along the rows. This capability significantly reduces the amount of data to be acquired at field level, the data storage capacity and the processing power of computers. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
Sensors 2016, 16(1), 108; https://doi.org/10.3390/s16010108 - 19 Jan 2016
Cited by 10
Abstract
Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, [...] Read more.
Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor
Sensors 2016, 16(1), 89; https://doi.org/10.3390/s16010089 - 11 Jan 2016
Cited by 8
Abstract
Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to [...] Read more.
Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle
Technological Advancement in Tower-Based Canopy Reflectance Monitoring: The AMSPEC-III System
Sensors 2015, 15(12), 32020-32030; https://doi.org/10.3390/s151229906 - 19 Dec 2015
Cited by 7
Abstract
Understanding plant photosynthesis, or Gross Primary Production (GPP), is a crucial aspect of quantifying the terrestrial carbon cycle. Remote sensing approaches, in particular multi-angular spectroscopy, have proven successful for studying relationships between canopy-reflectance and plant-physiology processes, thus providing a mechanism to scale up. [...] Read more.
Understanding plant photosynthesis, or Gross Primary Production (GPP), is a crucial aspect of quantifying the terrestrial carbon cycle. Remote sensing approaches, in particular multi-angular spectroscopy, have proven successful for studying relationships between canopy-reflectance and plant-physiology processes, thus providing a mechanism to scale up. However, many different instrumentation designs exist and few cross-comparisons have been undertaken. This paper discusses the design evolution of the Automated Multiangular SPectro-radiometer for Estimation of Canopy reflectance (AMSPEC) series of instruments. Specifically, we assess the performance of the PP-Systems Unispec-DC and Ocean Optics JAZ-COMBO spectro-radiometers installed on an updated, tower-based AMSPEC-III system. We demonstrate the interoperability of these spectro-radiometers, and the results obtained suggest that JAZ-COMBO can successfully be used to substitute more expensive measurement units for detecting and investigating photosynthesis and canopy spectra. We demonstrate close correlations between JAZ-COMBO and Unispec-DC measured canopy radiance (0.75 ≤ R2 ≤ 0.85) and solar irradiance (0.95 ≤ R2 ≤ 0.96) over a three month time span. We also demonstrate close agreement between the bi-directional distribution functions obtained from each instrument. We conclude that cost effective alternatives may allow a network of AMSPEC-III systems to simultaneously monitor various vegetation types in different ecosystems. This will allow to scale and improve our understanding of the interactions between vegetation physiology and spectral characteristics, calibrate broad-scale observations to stand-level measurements, and ultimately lead to improved understanding of changing vegetation spectral features from satellite. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Review

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Open AccessReview
3-D Imaging Systems for Agricultural Applications—A Review
Sensors 2016, 16(5), 618; https://doi.org/10.3390/s16050618 - 29 Apr 2016
Cited by 47 | Correction
Abstract
Efficiency increase of resources through automation of agriculture requires more information about the production process, as well as process and machinery status. Sensors are necessary for monitoring the status and condition of production by recognizing the surrounding structures such as objects, field structures, [...] Read more.
Efficiency increase of resources through automation of agriculture requires more information about the production process, as well as process and machinery status. Sensors are necessary for monitoring the status and condition of production by recognizing the surrounding structures such as objects, field structures, natural or artificial markers, and obstacles. Currently, three dimensional (3-D) sensors are economically affordable and technologically advanced to a great extent, so a breakthrough is already possible if enough research projects are commercialized. The aim of this review paper is to investigate the state-of-the-art of 3-D vision systems in agriculture, and the role and value that only 3-D data can have to provide information about environmental structures based on the recent progress in optical 3-D sensors. The structure of this research consists of an overview of the different optical 3-D vision techniques, based on the basic principles. Afterwards, their application in agriculture are reviewed. The main focus lays on vehicle navigation, and crop and animal husbandry. The depth dimension brought by 3-D sensors provides key information that greatly facilitates the implementation of automation and robotics in agriculture. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Other

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Open AccessCorrection
Correction: Vázquez-Arellano, M., et al. 3-D Imaging Systems for Agricultural Applications—A Review. Sensors 2016, 16, 618
Sensors 2016, 16(7), 1039; https://doi.org/10.3390/s16071039 - 05 Jul 2016
Abstract
The authors wish to make the following corrections to Table 1 of the title paper [1]: the working environment of the PlantEye platform should be changed from “Greenhouse” to “Open field, Greenhouse” and the shadowing device of the Scanalyzer platform should be changed [...] Read more.
The authors wish to make the following corrections to Table 1 of the title paper [1]: the working environment of the PlantEye platform should be changed from “Greenhouse” to “Open field, Greenhouse” and the shadowing device of the Scanalyzer platform should be changed from “‘√” to “x”. [...] Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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