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Topical Collection "Sensors in Agriculture and Forestry"

Editor

Collection Editor
Prof. Dr. Gonzalo Pajares Martinsanz

Department Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain
Website | E-Mail
Phone: +34.1.3947546
Interests: computer vision; image processing; pattern recognition; 3D image reconstruction, spatio-temporal image change detection and track movement; fusion and registering from imaging sensors; superresolution from low-resolution image sensors

Topical Collection Information

Dear Colleagues,

Sensors and technologies involving sensors in agriculture and forestry play an important role today. In agriculture and silviculture, as a branch of forestry, the need for increasing the production and simultaneously the efforts for minimizing the environmental impact and for saving costs make the sensor systems the best allied tool. The use of sensors helps to exploit all available resources appropriately and to apply hazardous products moderately. When nutrients in the soil, humidity, solar radiation, density of weeds and all factors affecting the production are known, this gets better and the use of chemical products such as fertilizers, herbicides and other pollution products can be reduced considerably. These activities fall inside the emerging area known as Precision Agriculture. In forest management, which can be considered a branch of forestry, a lot of number of activities is oriented towards wood production or forest inventories with the aims of controlling parameters of interest such as diameter of trees, height, crown height, bark thickness and other variables, such as canopy, humidity, illumination, CO2 transformation, where the social acceptation is of interest.

The use of unmanned aerial or ground vehicles (UAVs and UGVs), equipped with a set of sensors, has experimented an important growing during the last years to carry out the tasks involved in the above processes and also for autonomous navigation on the specific agricultural and forestry environments. But also traditional crewed vehicles are sensorized conveniently with same purpose.

Additionally, during the post-production process, including transportation, storage, packing, selection, classification or distribution among others, the use of sensors is of vital importance for minimizing costs and negative environmental impact allowing saving energy or minimizing the application of chemical products.

A list of sensors covering the above topics, but are not limited, is the following: biological (including chemical and gas analyzers), water sensors, meteorological sensors, weed seekers, optical cameras, Light Detection and Ranging (LIDAR), photometric sensors, soil respiration or moisture, photosynthesis sensors, Leaf Area index (LAI) sensors, range finders, Dendrometers, hygrometers.

This Topical Collection covers the following topics related to the above sensors:

  • Sensors devices capabilities, materials and technologies
  • Applications and problems addressed
  • Sensors domain-oriented devices and methods used for processing the data sensed

Prof. Dr. Gonzalo Pajares Martinsanz
Collection Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs).

Keywords

  • sensors in agriculture and forestry
  • precision agriculture
  • sensors in agricultural and forestry production
  • storage and distribution
  • technologies
  • material and methods
  • sensors applications
  • processing of sensed data

Published Papers (16 papers)

2017

Jump to: 2016, 2015, 2014

Open AccessArticle Development of a Stereovision-Based Technique to Measure the Spread Patterns of Granular Fertilizer Spreaders
Sensors 2017, 17(6), 1396; doi:10.3390/s17061396
Received: 10 May 2017 / Revised: 8 June 2017 / Accepted: 13 June 2017 / Published: 15 June 2017
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Abstract
Centrifugal fertilizer spreaders are by far the most commonly used granular fertilizer spreader type in Europe. Their spread pattern however is error-prone, potentially leading to an undesired distribution of particles in the field and losses out of the field, which is often caused
[...] Read more.
Centrifugal fertilizer spreaders are by far the most commonly used granular fertilizer spreader type in Europe. Their spread pattern however is error-prone, potentially leading to an undesired distribution of particles in the field and losses out of the field, which is often caused by poor calibration of the spreader for the specific fertilizer used. Due to the large environmental impact of fertilizer use, it is important to optimize the spreading process and minimize these errors. Spreader calibrations can be performed by using collection trays to determine the (field) spread pattern, but this is very time-consuming and expensive for the farmer and hence not common practice. Therefore, we developed an innovative multi-camera system to predict the spread pattern in a fast and accurate way, independent of the spreader configuration. Using high-speed stereovision, ejection parameters of particles leaving the spreader vanes were determined relative to a coordinate system associated with the spreader. The landing positions and subsequent spread patterns were determined using a ballistic model incorporating the effect of tractor motion and wind. Experiments were conducted with a commercial spreader and showed a high repeatability. The results were transformed to one spatial dimension to enable comparison with transverse spread patterns determined in the field and showed similar results. Full article
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Open AccessErratum Erratum: Kim, K.-P.; Singh, A.K.; Bai, X.; Leprun, L.; Bhunia, A.K. Novel PCR Assays Complement Laser Biosensor-Based Method and Facilitate Listeria Species Detection from Food. Sensors 2015, 15, 22672–22691
Sensors 2017, 17(5), 945; doi:10.3390/s17050945
Received: 11 April 2017 / Accepted: 12 April 2017 / Published: 25 April 2017
PDF Full-text (157 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract The authors wish to correct the oligonucleotide sequence of primer E-LAP-F1 and LIS-R1 in Table 1in their paper published in Sensors [1], doi:10.3390/s150922672, http://www.mdpi.com/1424-8220/15/9/22672. The following table should be used.[...] Full article
Open AccessArticle Effective Calibration of Low-Cost Soil Water Content Sensors
Sensors 2017, 17(1), 208; doi:10.3390/s17010208
Received: 27 November 2016 / Revised: 12 January 2017 / Accepted: 16 January 2017 / Published: 21 January 2017
Cited by 1 | PDF Full-text (2359 KB) | HTML Full-text | XML Full-text
Abstract
Soil water content is a key variable for understanding and modelling ecohydrological processes. Low-cost electromagnetic sensors are increasingly being used to characterize the spatio-temporal dynamics of soil water content, despite the reduced accuracy of such sensors as compared to reference electromagnetic soil water
[...] Read more.
Soil water content is a key variable for understanding and modelling ecohydrological processes. Low-cost electromagnetic sensors are increasingly being used to characterize the spatio-temporal dynamics of soil water content, despite the reduced accuracy of such sensors as compared to reference electromagnetic soil water content sensing methods such as time domain reflectometry. Here, we present an effective calibration method to improve the measurement accuracy of low-cost soil water content sensors taking the recently developed SMT100 sensor (Truebner GmbH, Neustadt, Germany) as an example. We calibrated the sensor output of more than 700 SMT100 sensors to permittivity using a standard procedure based on five reference media with a known apparent dielectric permittivity (1 < Ka < 34.8). Our results showed that a sensor-specific calibration improved the accuracy of the calibration compared to single “universal” calibration. The associated additional effort in calibrating each sensor individually is relaxed by a dedicated calibration setup that enables the calibration of large numbers of sensors in limited time while minimizing errors in the calibration process. Full article
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Open AccessArticle Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents
Sensors 2017, 17(1), 142; doi:10.3390/s17010142
Received: 6 November 2016 / Revised: 27 December 2016 / Accepted: 9 January 2017 / Published: 13 January 2017
PDF Full-text (4959 KB) | HTML Full-text | XML Full-text
Abstract
The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results
[...] Read more.
The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions. Full article
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Open AccessArticle Automated Surveillance of Fruit Flies
Sensors 2017, 17(1), 110; doi:10.3390/s17010110
Received: 8 November 2016 / Revised: 2 January 2017 / Accepted: 5 January 2017 / Published: 8 January 2017
PDF Full-text (2764 KB) | HTML Full-text | XML Full-text
Abstract
Insects of the Diptera order of the Tephritidae family cause costly, annual crop losses worldwide. Monitoring traps are important components of integrated pest management programs used against fruit flies. Here we report the modification of typical, low-cost plastic traps for fruit flies by
[...] Read more.
Insects of the Diptera order of the Tephritidae family cause costly, annual crop losses worldwide. Monitoring traps are important components of integrated pest management programs used against fruit flies. Here we report the modification of typical, low-cost plastic traps for fruit flies by adding the necessary optoelectronic sensors to monitor the entrance of the trap in order to detect, time-stamp, GPS tag, and identify the species of incoming insects from the optoacoustic spectrum analysis of their wingbeat. We propose that the incorporation of automated streaming of insect counts, environmental parameters and GPS coordinates into informative visualization of collective behavior will finally enable better decision making across spatial and temporal scales, as well as administrative levels. The device presented is at product level of maturity as it has solved many pending issues presented in a previously reported study. Full article
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2016

Jump to: 2017, 2015, 2014

Open AccessArticle Simultaneous Moisture Content and Mass Flow Measurements in Wood Chip Flows Using Coupled Dielectric and Impact Sensors
Sensors 2017, 17(1), 20; doi:10.3390/s17010020
Received: 30 October 2016 / Revised: 16 December 2016 / Accepted: 20 December 2016 / Published: 23 December 2016
PDF Full-text (5697 KB) | HTML Full-text | XML Full-text
Abstract
An 8-electrode capacitance tomography (ECT) sensor was built and used to measure moisture content (MC) and mass flow of pine chip flows. The device was capable of directly measuring total water quantity in a sample but was sensitive to both dry matter and
[...] Read more.
An 8-electrode capacitance tomography (ECT) sensor was built and used to measure moisture content (MC) and mass flow of pine chip flows. The device was capable of directly measuring total water quantity in a sample but was sensitive to both dry matter and moisture, and therefore required a second measurement of mass flow to calculate MC. Two means of calculating the mass flow were used: the first being an impact sensor to measure total mass flow, and the second a volumetric approach based on measuring total area occupied by wood in images generated using the capacitance sensor’s tomographic mode. Tests were made on 109 groups of wood chips ranging in moisture content from 14% to 120% (dry basis) and wet weight of 280 to 1100 g. Sixty groups were randomly selected as a calibration set, and the remaining were used for validation of the sensor’s performance. For the combined capacitance/force transducer system, root mean square errors of prediction (RMSEP) for wet mass flow and moisture content were 13.42% and 16.61%, respectively. RMSEP using the combined volumetric mass flow/capacitance sensor for dry mass flow and moisture content were 22.89% and 24.16%, respectively. Either of the approaches was concluded to be feasible for prediction of moisture content in pine chip flows, but combining the impact and capacitance sensors was easier to implement. In situations where flows could not be impeded, however, the tomographic approach would likely be more useful. Full article
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Open AccessArticle Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions
Sensors 2016, 16(12), 2136; doi:10.3390/s16122136
Received: 31 October 2016 / Revised: 8 December 2016 / Accepted: 8 December 2016 / Published: 15 December 2016
Cited by 1 | PDF Full-text (17533 KB) | HTML Full-text | XML Full-text
Abstract
In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and
[...] Read more.
In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter. Full article
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Open AccessArticle Performance Evaluation of Proximal Sensors for Soil Assessment in Smallholder Farms in Embu County, Kenya
Sensors 2016, 16(11), 1950; doi:10.3390/s16111950
Received: 29 August 2016 / Revised: 1 November 2016 / Accepted: 8 November 2016 / Published: 19 November 2016
PDF Full-text (9242 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Four proximal soil sensors were tested at four smallholder farms in Embu County, Kenya: a portable X-ray fluorescence sensor (PXRF), a mobile phone application for soil color determination by photography, a dual-depth electromagnetic induction (EMI) sensor, and a LED-based soil optical reflectance sensor.
[...] Read more.
Four proximal soil sensors were tested at four smallholder farms in Embu County, Kenya: a portable X-ray fluorescence sensor (PXRF), a mobile phone application for soil color determination by photography, a dual-depth electromagnetic induction (EMI) sensor, and a LED-based soil optical reflectance sensor. Measurements were made at 32–43 locations at each site. Topsoil samples were analyzed for plant-available nutrients (N, P, K, Mg, Ca, S, B, Mn, Zn, Cu, and Fe), pH, total nitrogen (TN) and total carbon (TC), soil texture, cation exchange capacity (CEC), and exchangeable aluminum (Al). Multivariate prediction models of each of the lab-analyzed soil properties were parameterized for 576 sensor-variable combinations. Prediction models for K, N, Ca and S, B, Zn, Mn, Fe, TC, Al, and CEC met the setup criteria for functional, robust, and accurate models. The PXRF sensor was the sensor most often included in successful models. We concluded that the combination of a PXRF and a portable soil reflectance sensor is a promising combination of handheld soil sensors for the development of in situ soil assessments as a field-based alternative or complement to laboratory measurements. Full article
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Open AccessArticle Laboratory Performance of Five Selected Soil Moisture Sensors Applying Factory and Own Calibration Equations for Two Soil Media of Different Bulk Density and Salinity Levels
Sensors 2016, 16(11), 1912; doi:10.3390/s16111912
Received: 1 September 2016 / Revised: 2 November 2016 / Accepted: 5 November 2016 / Published: 15 November 2016
Cited by 1 | PDF Full-text (13173 KB) | HTML Full-text | XML Full-text
Abstract
Non-destructive soil water content determination is a fundamental component for many agricultural and environmental applications. The accuracy and costs of the sensors define the measurement scheme and the ability to fit the natural heterogeneous conditions. The aim of this study was to evaluate
[...] Read more.
Non-destructive soil water content determination is a fundamental component for many agricultural and environmental applications. The accuracy and costs of the sensors define the measurement scheme and the ability to fit the natural heterogeneous conditions. The aim of this study was to evaluate five commercially available and relatively cheap sensors usually grouped with impedance and FDR sensors. ThetaProbe ML2x (impedance) and ECH2O EC-10, ECH2O EC-20, ECH2O EC-5, and ECH2O TE (all FDR) were tested on silica sand and loess of defined characteristics under controlled laboratory conditions. The calibrations were carried out in nine consecutive soil water contents from dry to saturated conditions (pure water and saline water). The gravimetric method was used as a reference method for the statistical evaluation (ANOVA with significance level 0.05). Generally, the results showed that our own calibrations led to more accurate soil moisture estimates. Variance component analysis arranged the factors contributing to the total variation as follows: calibration (contributed 42%), sensor type (contributed 29%), material (contributed 18%), and dry bulk density (contributed 11%). All the tested sensors performed very well within the whole range of water content, especially the sensors ECH2O EC-5 and ECH2O TE, which also performed surprisingly well in saline conditions. Full article
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Open AccessArticle Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network
Sensors 2016, 16(8), 1228; doi:10.3390/s16081228
Received: 19 June 2016 / Revised: 27 July 2016 / Accepted: 28 July 2016 / Published: 4 August 2016
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Abstract
Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network
[...] Read more.
Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. Full article
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2015

Jump to: 2017, 2016, 2014

Open AccessArticle Impedance of the Grape Berry Cuticle as a Novel Phenotypic Trait to Estimate Resistance to Botrytis Cinerea
Sensors 2015, 15(6), 12498-12512; doi:10.3390/s150612498
Received: 23 February 2015 / Revised: 19 May 2015 / Accepted: 20 May 2015 / Published: 27 May 2015
Cited by 2 | PDF Full-text (2666 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality. Resistance donors of genetic loci to increase B. cinerea resistance are widely
[...] Read more.
Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality. Resistance donors of genetic loci to increase B. cinerea resistance are widely unknown. Promising traits of resistance are represented by physical features like the thickness and permeability of the grape berry cuticle. Sensor-based phenotyping methods or genetic markers are rare for such traits. In the present study, the simple-to-handle I-sensor was developed. The sensor enables the fast and reliable measurement of electrical impedance of the grape berry cuticles and its epicuticular waxes (CW). Statistical experiments revealed highly significant correlations between relative impedance of CW and the resistance of grapevines to B. cinerea. Thus, the relative impedance Zrel of CW was identified as the most important phenotypic factor with regard to the prediction of grapevine resistance to B. cinerea. An ordinal logistic regression analysis revealed a R2McFadden of 0.37 and confirmed the application of Zrel of CW for the prediction of bunch infection and in this way as novel phenotyping trait. Applying the I-sensor, a preliminary QTL region was identified indicating that the novel phenotypic trait is as well a valuable tool for genetic analyses. Full article
Open AccessArticle Georeferenced Scanning System to Estimate the Leaf Wall Area in Tree Crops
Sensors 2015, 15(4), 8382-8405; doi:10.3390/s150408382
Received: 20 November 2014 / Accepted: 2 April 2015 / Published: 10 April 2015
Cited by 2 | PDF Full-text (14042 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the use of a terrestrial light detection and ranging (LiDAR) system to scan the vegetation of tree crops to estimate the so-called pixelated leaf wall area (PLWA). Scanning rows laterally and considering only the half-canopy vegetation to the line of
[...] Read more.
This paper presents the use of a terrestrial light detection and ranging (LiDAR) system to scan the vegetation of tree crops to estimate the so-called pixelated leaf wall area (PLWA). Scanning rows laterally and considering only the half-canopy vegetation to the line of the trunks, PLWA refers to the vertical projected area without gaps detected by LiDAR. As defined, PLWA may be different depending on the side from which the LiDAR is applied. The system is completed by a real-time kinematic global positioning system (RTK-GPS) sensor and an inertial measurement unit (IMU) sensor for positioning. At the end, a total leaf wall area (LWA) is computed and assigned to the X, Y position of each vertical scan. The final value of the area depends on the distance between two consecutive scans (or horizontal resolution), as well as the number of intercepted points within each scan, since PLWA is only computed when the laser beam detects vegetation. To verify system performance, tests were conducted related to the georeferencing task and synchronization problems between GPS time and central processing unit (CPU) time. Despite this, the overall accuracy of the system is generally acceptable. The Leaf Area Index (LAI) can then be estimated using PLWA as an explanatory variable in appropriate linear regression models. Full article
Open AccessArticle Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution
Sensors 2015, 15(3), 5609-5626; doi:10.3390/s150305609
Received: 7 January 2015 / Revised: 25 February 2015 / Accepted: 27 February 2015 / Published: 6 March 2015
Cited by 11 | PDF Full-text (5992 KB) | HTML Full-text | XML Full-text
Abstract
In order to optimize the application of herbicides in weed-crop systems, accurate and timely weed maps of the crop-field are required. In this context, this investigation quantified the efficacy and limitations of remote images collected with an unmanned aerial vehicle (UAV) for early
[...] Read more.
In order to optimize the application of herbicides in weed-crop systems, accurate and timely weed maps of the crop-field are required. In this context, this investigation quantified the efficacy and limitations of remote images collected with an unmanned aerial vehicle (UAV) for early detection of weed seedlings. The ability to discriminate weeds was significantly affected by the imagery spectral (type of camera), spatial (flight altitude) and temporal (the date of the study) resolutions. The colour-infrared images captured at 40 m and 50 days after sowing (date 2), when plants had 5–6 true leaves, had the highest weed detection accuracy (up to 91%). At this flight altitude, the images captured before date 2 had slightly better results than the images captured later. However, this trend changed in the visible-light images captured at 60 m and higher, which had notably better results on date 3 (57 days after sowing) because of the larger size of the weed plants. Our results showed the requirements on spectral and spatial resolutions needed to generate a suitable weed map early in the growing season, as well as the best moment for the UAV image acquisition, with the ultimate objective of applying site-specific weed management operations. Full article
Open AccessArticle Structure Optimization of a Grain Impact Piezoelectric Sensor and Its Application for Monitoring Separation Losses on Tangential-Axial Combine Harvesters
Sensors 2015, 15(1), 1496-1517; doi:10.3390/s150101496
Received: 6 November 2014 / Accepted: 30 December 2014 / Published: 14 January 2015
PDF Full-text (3758 KB) | HTML Full-text | XML Full-text
Abstract
Grain separation losses is a key parameter to weigh the performance of combine harvesters, and also a dominant factor for automatically adjusting their major working parameters. The traditional separation losses monitoring method mainly rely on manual efforts, which require a high labor intensity.
[...] Read more.
Grain separation losses is a key parameter to weigh the performance of combine harvesters, and also a dominant factor for automatically adjusting their major working parameters. The traditional separation losses monitoring method mainly rely on manual efforts, which require a high labor intensity. With recent advancements in sensor technology, electronics and computational processing power, this paper presents an indirect method for monitoring grain separation losses in tangential-axial combine harvesters in real-time. Firstly, we developed a mathematical monitoring model based on detailed comparative data analysis of different feeding quantities. Then, we developed a grain impact piezoelectric sensor utilizing a YT-5 piezoelectric ceramic as the sensing element, and a signal process circuit designed according to differences in voltage amplitude and rise time of collision signals. To improve the sensor performance, theoretical analysis was performed from a structural vibration point of view, and the optimal sensor structural has been selected. Grain collide experiments have shown that the sensor performance was greatly improved. Finally, we installed the sensor on a tangential-longitudinal axial combine harvester, and grain separation losses monitoring experiments were carried out in North China, which results have shown that the monitoring method was feasible, and the biggest measurement relative error was 4.63% when harvesting rice. Full article
Open AccessArticle A Customized Metal Oxide Semiconductor-Based Gas Sensor Array for Onion Quality Evaluation: System Development and Characterization
Sensors 2015, 15(1), 1252-1273; doi:10.3390/s150101252
Received: 3 November 2014 / Accepted: 4 January 2015 / Published: 12 January 2015
Cited by 10 | PDF Full-text (4086 KB) | HTML Full-text | XML Full-text
Abstract
A gas sensor array, consisting of seven Metal Oxide Semiconductor (MOS) sensors that are sensitive to a wide range of organic volatile compounds was developed to detect rotten onions during storage. These MOS sensors were enclosed in a specially designed Teflon chamber equipped
[...] Read more.
A gas sensor array, consisting of seven Metal Oxide Semiconductor (MOS) sensors that are sensitive to a wide range of organic volatile compounds was developed to detect rotten onions during storage. These MOS sensors were enclosed in a specially designed Teflon chamber equipped with a gas delivery system to pump volatiles from the onion samples into the chamber. The electronic circuit mainly comprised a microcontroller, non-volatile memory chip, and trickle-charge real time clock chip, serial communication chip, and parallel LCD panel. User preferences are communicated with the on-board microcontroller through a graphical user interface developed using LabVIEW. The developed gas sensor array was characterized and the discrimination potential was tested by exposing it to three different concentrations of acetone (ketone), acetonitrile (nitrile), ethyl acetate (ester), and ethanol (alcohol). The gas sensor array could differentiate the four chemicals of same concentrations and different concentrations within the chemical with significant difference. Experiment results also showed that the system was able to discriminate two concentrations (196 and 1964 ppm) of methlypropyl sulfide and two concentrations (145 and 1452 ppm) of 2-nonanone, two key volatile compounds emitted by rotten onions. As a proof of concept, the gas sensor array was able to achieve 89% correct classification of sour skin infected onions. The customized low-cost gas sensor array could be a useful tool to detect onion postharvest diseases in storage. Full article

2014

Jump to: 2017, 2016, 2015

Open AccessReview A Review of Imaging Techniques for Plant Phenotyping
Sensors 2014, 14(11), 20078-20111; doi:10.3390/s141120078
Received: 8 August 2014 / Revised: 9 October 2014 / Accepted: 10 October 2014 / Published: 24 October 2014
Cited by 82 | PDF Full-text (1188 KB) | HTML Full-text | XML Full-text
Abstract
Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a
[...] Read more.
Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review. Full article

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