Special Issue "Precision Agriculture for Sustainability"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (1 August 2020).

Special Issue Editor

Dr. Manuel Pérez-Ruiz
Website
Guest Editor
Department of Aerospace Engineering and Fluids Mechanics, Escuela Técnica Superior de Ingeniería Agronómica (ETSIA), Universidad de Sevilla, 41013 Sevilla, Spain
Interests: remote sensing; precision agriculture; agriculture machinery; advances in crop protection

Special Issue Information

Dear Colleagues,

There is general consensus among the scientific community that the challenge of feeding a growing population in a resource-limited world must be addressed by developing new set of tools applicable to agriculture (GNSS technology, sensors, UAVs, software, actuator and controllers). Smart farming can make agriculture more profitable for the farmer. Decreasing resource inputs will save the farmer money and labor, and increased reliability of spatially explicit data will reduce risks. Please share your success stories from research in precision agriculture around the world in this Special Issue. Submissions on the following topics (but not limited to) are invited: 1) hyperspectral imagery for mapping plant health and crop yield; 2) agricultural robots; 3) farm management information systems; 4) automated irrigation scheduling; 5) crop stress monitoring; 6) high-throughput plant phenotyping; 7) agricultural machine learning; 8) precision for weed control; 9) sensors and drones in agriculture; and 10) economic analysis of the efficiency and sustainability

Dr. Manuel Pérez-Ruiz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Variable rate application (VRA)
  • Data fusion
  • Drones for agriculture
  • Soil mapping
  • Advances in crop protection
  • Decision support system (DSS)
  • Precision irrigation
  • Precision phenotyping.

Published Papers (15 papers)

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Open AccessArticle
Evaluation of In-Season Management Zones from High-Resolution Soil and Plant Sensors
Agronomy 2020, 10(8), 1124; https://doi.org/10.3390/agronomy10081124 - 03 Aug 2020
Abstract
The adoption of precision agriculture has the potential to increase the environmental sustainability of cropping systems as well as farmers’ income. Farmers in transition to precision agriculture need low-input and effective protocols to delineate homogenous management zones to optimize their actions without past [...] Read more.
The adoption of precision agriculture has the potential to increase the environmental sustainability of cropping systems as well as farmers’ income. Farmers in transition to precision agriculture need low-input and effective protocols to delineate homogenous management zones to optimize their actions without past knowledge e.g., yield maps. Different approaches have been developed so far, based on the analysis of the within-field variability in crop and soil properties, but procedures were rarely suited for operational conditions. We identified here a low-inputs protocol to map management zones from soil electrical conductivity and/or crop vegetation indices, using a winter wheat field in northern Italy as a pilot case. The reliability of the alternative data sources was evaluated at three crop development stages using a yield map as reference. Red-edge and NIR (NDRE) bands were the most reliable data sources for management zones identification, with 62%, 68%, and 74% of correct classifications at early tillering, stem elongation, and late booting, respectively. Our work identifies a minimum dataset for accurate management zones’ definition and highlights that in-season monitoring based on the red-edge band was able to reliably identify management zones already at early tillering, despite minor differences in crop growth. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Using YOLOv3 Algorithm with Pre- and Post-Processing for Apple Detection in Fruit-Harvesting Robot
Agronomy 2020, 10(7), 1016; https://doi.org/10.3390/agronomy10071016 - 14 Jul 2020
Abstract
A machine vision system for detecting apples in orchards was developed. The system was designed to be used in harvesting robots and is based on a YOLOv3 algorithm with special pre- and post-processing. The proposed pre- and post-processing techniques made it possible to [...] Read more.
A machine vision system for detecting apples in orchards was developed. The system was designed to be used in harvesting robots and is based on a YOLOv3 algorithm with special pre- and post-processing. The proposed pre- and post-processing techniques made it possible to adapt the YOLOv3 algorithm to be used in an apple-harvesting robot machine vision system, providing an average apple detection time of 19 ms with a share of objects being mistaken for apples at 7.8% and a share of unrecognized apples at 9.2%. Both the average detection time and error rates are less than in all known similar systems. The system can operate not only in apple-harvesting robots but also in orange-harvesting robots. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Smart Palm: An IoT Framework for Red Palm Weevil Early Detection
Agronomy 2020, 10(7), 987; https://doi.org/10.3390/agronomy10070987 - 09 Jul 2020
Cited by 1
Abstract
Smart agriculture is an evolving trend in the agriculture industry, where sensors are embedded into plants to collect vital data and help in decision-making to ensure a higher quality of crops and prevent pests, disease, and other possible threats. One of the most [...] Read more.
Smart agriculture is an evolving trend in the agriculture industry, where sensors are embedded into plants to collect vital data and help in decision-making to ensure a higher quality of crops and prevent pests, disease, and other possible threats. One of the most critical pests of palms is the red palm weevil, which is an insect that causes much damage to palm trees and can devastate vast areas of palm trees. The most challenging problem is that the effect of the weevil is not visible by humans until the palm reaches an advanced infestation state. For this reason, there is a pressing need to use advanced technology for early detection and prevention of infestation propagation. In this project, we have developed an IoT-based smart palm monitoring prototype as a proof-of-concept that (1) allows monitoring palms remotely using smart agriculture sensors, (2) contribute to the early detection of red palm weevil infestation. Users can use web/mobile applications to interact with their palm farms and help them in getting early detection of possible infestations. We used an industrial-level IoT platform to interface between the sensor layer and the user layer. Moreover, we have collected data using accelerometer sensors, and we applied signal processing and statistical techniques to analyze collected data and determine a fingerprint of the infestation. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Positioning Accuracy Comparison of GNSS Receivers Used for Mapping and Guidance of Agricultural Machines
Agronomy 2020, 10(7), 924; https://doi.org/10.3390/agronomy10070924 - 27 Jun 2020
Abstract
Global Navigation Satellite Systems (GNSS) allow the determination of the 3D position of a point on the Earth’s surface by measuring the distance from the receiver antenna to the orbital position of at least four satellites. Selecting and buying a GNSS receiver, depending [...] Read more.
Global Navigation Satellite Systems (GNSS) allow the determination of the 3D position of a point on the Earth’s surface by measuring the distance from the receiver antenna to the orbital position of at least four satellites. Selecting and buying a GNSS receiver, depending on farm needs, is the first step for implementing precision agriculture. The aim of this work is to compare the positioning accuracy of four GNSS receivers, different for technical features and working modes: L1/L2 frequency survey-grade Real-Time Kinematic (RTK)-capable Stonex S7-G (S7); L1 frequency RTK-capable Stonex S5 (S5); L1 frequency Thales MobileMapper Pro (TMMP); low-cost L1 frequency Quanum GPS Logger V2 (QLV2). In order to evaluate the positioning accuracy of these receivers, i.e., the distance of the determined points from a reference trajectory, different tests, distinguished by the use or not of Real-Time Kinematic (RTK) differential correction data and/or an external antenna, were carried out. The results show that all satellite receivers tested carried out with the external antenna had an improvement in positioning accuracy. The Thales MobileMapper Pro satellite receiver showed the worst positioning accuracy. The low-cost Quanum GPS Logger V2 receiver surprisingly showed an average positioning error of only 0.550 m. The positioning accuracy of the above-mentioned receiver was slightly worse than that obtained using Stonex S7-G without the external antenna and differential correction (maximum positioning error 0.749 m). However, this accuracy was even better than that recorded using Stonex S5 without differential correction, both with and without the external antenna (average positioning error of 0.962 m and 1.368 m). Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Assessing the Orange Tree Crown Volumes Using Google Maps as a Low-Cost Photogrammetric Alternative
Agronomy 2020, 10(6), 893; https://doi.org/10.3390/agronomy10060893 - 23 Jun 2020
Cited by 1
Abstract
The accurate assessment of tree crowns is important for agriculture, for example, to adjust spraying rates, to adjust irrigation rates or even to estimate biomass. Among the available methodologies, there are the traditional methods that estimate with a three-dimensional approximation figure, the HDS [...] Read more.
The accurate assessment of tree crowns is important for agriculture, for example, to adjust spraying rates, to adjust irrigation rates or even to estimate biomass. Among the available methodologies, there are the traditional methods that estimate with a three-dimensional approximation figure, the HDS (High Definition Survey), or TLS (Terrestrial Laser Scanning) based on LiDAR technology, the aerial photogrammetry that has re-emerged with unmanned aerial vehicles (UAVs), as they are considered low cost. There are situations where either the cost or location does not allow for modern methods and prices such as HDS or the use of UAVs. This study proposes, as an alternative methodology, the evaluation of images extracted from Google Maps (GM) for the calculation of tree crown volume. For this purpose, measurements were taken on orange trees in the south of Spain using the four methods mentioned above to evaluate the suitability, accuracy, and limitations of GM. Using the HDS method as a reference, the photogrammetric method with UAV images has shown an average error of 10%, GM has obtained approximately 50%, while the traditional methods, in our case considering ellipsoids, have obtained 100% error. Therefore, the results with GM are encouraging and open new perspectives for the estimation of tree crown volumes at low cost compared to HDS, and without geographical flight restrictions like those of UAVs. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Assessing Soil Key Fertility Attributes Using a Portable X-ray Fluorescence: A Simple Method to Overcome Matrix Effect
Agronomy 2020, 10(6), 787; https://doi.org/10.3390/agronomy10060787 - 01 Jun 2020
Cited by 1
Abstract
The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple [...] Read more.
The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple linear regressions (MLR) and partial least squares regression (PLSR)) to overcome the soil matrix effect, and subsequently improve the prediction accuracy of key soil fertility attributes. A portable XRF was used for analyzing 102 soil samples collected from two agricultural fields with contrasting soil matrices. Using the intensity of emission lines as input, preprocessing methods included with and without the CN. Univariate regression models for the prediction of clay, cation exchange capacity (CEC), and exchangeable (ex-) K and Ca were compared with the corresponding MLR models to assess matrix effect mitigation. The MLR and PLSR models improved the prediction results of the univariate models for both preprocessing methods, proving to be promising strategies for mitigating the matrix effect. In turn, the CN also mitigated part of the matrix effect for ex-K, ex-Ca, and CEC predictions, by improving the predictive performance of these elements when used in univariate and multivariate models. The CN has not improved the prediction accuracy of clay. The prediction performances obtained using MLR and PLSR were comparable for all evaluated attributes. The combined use of CN with multivariate regressions (MLR or PLSR) achieved excellent prediction results for CEC (R2 = 0.87), ex-K (R2 ≥ 0.94), and ex-Ca (R2 ≥ 0.96), whereas clay predictions were comparable with and without CN (0.89 ≤ R2 ≤ 0.92). We suggest using multivariate regressions (MLR or PLSR) combined with the CN to remove the soil matrix effects and consequently result in optimal prediction results of the studied key soil fertility attributes. The prediction performance observed for this solution showed comparable results to the approach based on the preprogrammed measurement package tested (Geo Exploration package, Bruker AXS, Madison, WI, USA). Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Characterization of the Transverse Distribution of Fertilizer in Coffee Plantations
Agronomy 2020, 10(4), 601; https://doi.org/10.3390/agronomy10040601 - 23 Apr 2020
Cited by 1
Abstract
Considering the impact of fertilizers on coffee production costs, the search for greater efficiency in the use of these inputs has an important role. Accordingly, the aim of the present study is to evaluate the transverse distribution of fertilizer by a centrifugal spreader [...] Read more.
Considering the impact of fertilizers on coffee production costs, the search for greater efficiency in the use of these inputs has an important role. Accordingly, the aim of the present study is to evaluate the transverse distribution of fertilizer by a centrifugal spreader in a coffee plantation and to compare two operating modes: fertilizer application on one side (FA1), or both sides (FA2) of the coffee plants. In addition, three doses (200, 300 and 400 kg ha−1) of monoammonium phosphate and three spreading disk rotation speeds (240, 375 and 750 rpm) were tested. To characterize fertilizer distribution profiles, collectors were placed under the canopy of coffee plants, and the collected fertilizer was weighed. From the data obtained, distribution profile histograms were constructed, and coefficients of variation were calculated for each treatment. Distribution profiles with higher uniformity were related to the morphologic characteristics of the coffee plants. Regarding the operating modes evaluated, FA1 presented better results with a disk rotation speed of 750 rpm (FA1-W3); FA2 produced the best results with a disk rotation speed of 240 rpm. By relating these results with information on root morphology, FA1-W3 was found to be the most appropriate application method. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision
Agronomy 2020, 10(4), 590; https://doi.org/10.3390/agronomy10040590 - 20 Apr 2020
Abstract
A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop [...] Read more.
A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Autonomous Mowers Working in Narrow Spaces: A Possible Future Application in Agriculture?
Agronomy 2020, 10(4), 553; https://doi.org/10.3390/agronomy10040553 - 10 Apr 2020
Cited by 2
Abstract
Autonomous mowers are becoming increasingly common in public and private greenspaces. Autonomous mowers can provide several advantages since these machines help to save time and energy and prevent operators from possible injuries. Current autonomous mowers operate by following random trajectories within areas defined [...] Read more.
Autonomous mowers are becoming increasingly common in public and private greenspaces. Autonomous mowers can provide several advantages since these machines help to save time and energy and prevent operators from possible injuries. Current autonomous mowers operate by following random trajectories within areas defined by a shallow-buried boundary wire that has the purpose to generate an electro-magnetic field. Once the electro-magnetic field is perceived by the autonomous mower, the machine will stop and change direction. Mowing along random trajectories is considered an efficient solution to manage areas with a variable number of obstacles. In agriculture, autonomous technologies are becoming increasingly popular since they can help to increase both the quantity and quality of agricultural products by reducing productive cost and improving the production process. Thus, even autonomous mowers may be useful to carry out some of the agricultural operations that are highly time consuming. In fact, some autonomous mowers designed and realized to work in vineyards and home vegetable gardens are already available on the market. The aim of this study was to compare the work capacity of six autonomous mowers that move along random trajectories in areas with a high number of obstacles to assess if these machines may be employed in some agricultural contexts. The six autonomous mowers were split in three groups based on their size (large, medium, and small) and were left to work in two areas with equal number of obstacles but different layouts. The first area (Site A) had a square shape and an extension of 23.04 m2, in order to keep the autonomous mowers enclosed inside it. The second area (Site B) had a square shape and an extension of 84.64 m2, in order to have a part of the area with no obstacles. The layout and the size of the two areas affected the autonomous mowers performances in different ways. The six autonomous mowers working on Site A obtained similar results and higher performances compared to the same mowers working on Site B. All the autonomous mowers proved to be able to mow more than 89% of Site A after 2 h and more than 98% of Site A after 5 h. On Site B small size autonomous mowers obtained the best results mowing more than 83% of the area with obstacles after 2 h and more than 98% of the area with obstacles after 5 h. However, specific work settings allowed larger autonomous mowers to improve their efficiency, obtaining similar results compared to smaller autonomous mowers. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision
Agronomy 2020, 10(4), 470; https://doi.org/10.3390/agronomy10040470 - 28 Mar 2020
Abstract
To realize quick localization of plant maize, a new real-time localization approach is proposed for maize cores at the seedling stage, which can meet the basic demands for localization and quantitative fertilization in precision agriculture and reduce environmental pollution and the use of [...] Read more.
To realize quick localization of plant maize, a new real-time localization approach is proposed for maize cores at the seedling stage, which can meet the basic demands for localization and quantitative fertilization in precision agriculture and reduce environmental pollution and the use of chemical fertilizers. In the first stage, by taking pictures of maize at the seedling stage in a field with a monocular camera, the maize is segmented from the weed background of the picture. And then the three most-effective methods (i.e., minimum cross entropy, ISODATA, and the Otsu algorithm) are found from six common segmentation algorithms after comparing the accuracy rate of extracting maize and the time efficiency of segmentation. In the second stage, plant core from segmented maize image is recognized, and localized, based on different brightness of the rest part of maize core and plant. Then the geometric center of maize core is considered as localization point. the best effect of extracting maize core was found from the minimum cross entropy method based on gray level. According to experimental validation using many field pictures, under weedy conditions on sunny days, the proposed method has a minimum recognition rate of 88.37% for maize cores and is more robust at excluding weeds. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Determining Irrigation Depths for Soybean Using a Simulation Model of Water Flow and Plant Growth and Weather Forecasts
Agronomy 2020, 10(3), 369; https://doi.org/10.3390/agronomy10030369 - 07 Mar 2020
Cited by 1
Abstract
A new scheme to determine irrigation depths using a two-point of predicted cumulative transpiration over irrigation interval is presented. Rather than maximizing water use efficiency, this scheme aims to maximize net income. The volumetric water price is considered to give farmers an incentive [...] Read more.
A new scheme to determine irrigation depths using a two-point of predicted cumulative transpiration over irrigation interval is presented. Rather than maximizing water use efficiency, this scheme aims to maximize net income. The volumetric water price is considered to give farmers an incentive to save irrigation water. A field experiment for soybeans was carried out in the Arid Land Research Center, Tottori University, Japan in 2019. The total irrigation amount yield and net income by the proposed scheme were compared to those by a tensiometer-operated automated irrigation. The scheme could save irrigation water by 16% with a yield increment of 20%; resulting in a 22% increase in net income compared to the automated irrigation. The model simulated the volumetric water content in the effective root zone of the plant in fair agreement. These results indicate the effectiveness of the proposed scheme that may replace an automated irrigation system even considering uncertainty in weather forecast to determine irrigation depth and secure investment costs. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Mapping an Agricultural Field Experiment by Electromagnetic Induction and Ground Penetrating Radar to Improve Soil Water Content Estimation
Agronomy 2019, 9(10), 638; https://doi.org/10.3390/agronomy9100638 - 15 Oct 2019
Cited by 2
Abstract
A growing interest in proximal sensing technologies for estimating soil water content (SWC) will be highlighted. On this matter the objectives of this study were: (1) to use both the combined electromagnetic induction (EMI) sensor and Ground Penetrating Radar (GPR) to characterize an [...] Read more.
A growing interest in proximal sensing technologies for estimating soil water content (SWC) will be highlighted. On this matter the objectives of this study were: (1) to use both the combined electromagnetic induction (EMI) sensor and Ground Penetrating Radar (GPR) to characterize an innovative field experiment located in southern Italy, in which different agricultural practices are tested, including a soil hydraulic arrangement; (2) to implement a geostatistical approach in order to merge different geophysical sensor data as auxiliary variables for SWC estimation. The multi-sensor recorded data were: (1) SWC data measured by gravimetric method; (2) Differential Global Positioning System height; (3) apparent electrical conductivity measured by an EMI sensor; (4) depths of soil discontinuities individuated by GPR radargrams interpretation; and (5) amplitude of GPR signal data at two different frequencies. Geostatistical techniques were used both to map all variables and improve the SWC estimation. The findings of this research indicate that: (1) the GPR radargrams identified four reflection events as a consequence of interfaces; (2) the EMI and GPR mapping provided identification of areas with high potential for water stagnation; and (3) the outputs of geophysical sensors can be effectively used as auxiliary tools to supplement the sampling of the target variable and to improve water content estimation. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Open AccessArticle
Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration
Agronomy 2019, 9(10), 596; https://doi.org/10.3390/agronomy9100596 - 28 Sep 2019
Cited by 4
Abstract
Plant morphological data are an important basis for precision agriculture and plant phenomics. The three-dimensional (3D) geometric shape of plants is complex, and the 3D morphology of a plant changes relatively significantly during the full growth cycle. In order to make high-throughput measurements [...] Read more.
Plant morphological data are an important basis for precision agriculture and plant phenomics. The three-dimensional (3D) geometric shape of plants is complex, and the 3D morphology of a plant changes relatively significantly during the full growth cycle. In order to make high-throughput measurements of the 3D morphological data of greenhouse plants, it is necessary to frequently adjust the relative position between the sensor and the plant. Therefore, it is necessary to frequently adjust the Kinect sensor position and consequently recalibrate the Kinect sensor during the full growth cycle of the plant, which significantly increases the tedium of the multiview 3D point cloud reconstruction process. A high-throughput 3D rapid greenhouse plant point cloud reconstruction method based on autonomous Kinect v2 sensor position calibration is proposed for 3D phenotyping greenhouse plants. Two red–green–blue–depth (RGB-D) images of the turntable surface are acquired by the Kinect v2 sensor. The central point and normal vector of the axis of rotation of the turntable are calculated automatically. The coordinate systems of RGB-D images captured at various view angles are unified based on the central point and normal vector of the axis of the turntable to achieve coarse registration. Then, the iterative closest point algorithm is used to perform multiview point cloud precise registration, thereby achieving rapid 3D point cloud reconstruction of the greenhouse plant. The greenhouse tomato plants were selected as measurement objects in this study. Research results show that the proposed 3D point cloud reconstruction method was highly accurate and stable in performance, and can be used to reconstruct 3D point clouds for high-throughput plant phenotyping analysis and to extract the morphological parameters of plants. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Review

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Open AccessReview
Unmanned Aircraft System (UAS) Technology and Applications in Agriculture
Agronomy 2019, 9(10), 618; https://doi.org/10.3390/agronomy9100618 - 09 Oct 2019
Cited by 8
Abstract
Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks [...] Read more.
Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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Other

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Open AccessProject Report
Technologies for Sustainable Biomass Supply—Overview of Market Offering
Agronomy 2020, 10(6), 798; https://doi.org/10.3390/agronomy10060798 - 04 Jun 2020
Cited by 2
Abstract
This article introduces a collection of Information and Communications Technologies (ICT), Internet of Things (IoT), and Industry 4.0 technologies utilized in (or applicable to) biomass supply chains that constitute the current state-of-the-art along with their brief descriptions. The scoping of technologies has been [...] Read more.
This article introduces a collection of Information and Communications Technologies (ICT), Internet of Things (IoT), and Industry 4.0 technologies utilized in (or applicable to) biomass supply chains that constitute the current state-of-the-art along with their brief descriptions. The scoping of technologies has been conducted by means of direct interactions with bioeconomy stakeholders and technology providers, analysis of the reports from bioeconomy-related projects, literature surveys, and internet searches. It is to highlight that technology mapping investigated here is focused on commercially available tools and services, which usually come with support, thus removing the necessity for expert knowledge or unusual technical proficiency. The list with over 100 items represents the current best knowledge of its creators and it is currently available through the ICT-BIOCHAIN project platform, serving as a database with technology descriptions and capability for updating the information. The ultimate objective of the database and the platform is to serve as a common point facilitating the cross-sectorial connection, where biomass stakeholders looking for new ICT, IoT, and Industry 4.0 solutions to make their work more efficient and sustainable can browse and filter out the records of their interest and obtain the contact information of the providers. Full article
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
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