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AgriEngineering
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Unmanned Aerial Vehicle and Remote Sensing Systems Usage in Precision...
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Unmanned Aerial Vehicle and Remote Sensing Systems Usage in Precision Agriculture

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 4624

Special Issue Editors

Dr. Jing Zhou

E-Mail Website
Guest Editor
Department of Crop and Soil Science, Oregon State University, Corvallis, OR 97331, USA
Interests: precision agriculture; high-throughput phenotyping; unmanned aerial vehicle; remote sensing; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Lirong Xiang

E-Mail Website
Co-Guest Editor
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA
Interests: agricultural robotics; 2D and 3D computer vision; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is facing challenges in crop production due to the rapid expansion of the human population and the adverse agricultural environments caused by climate change, urbanization, soil degradation, water shortages, and pollution. To improve crop productivity while reducing environmental losses, precision agriculture, utilizing remote sensing, automated solutions, and data analytics, are applied to assess, manage, and evaluate spatial-temporal variability in agricultural production. With the emerging advances in sensors, computational capacity, robotics, and artificial intelligence, precision agricultural activities have been promoted to an unprecedented level, coming along with innovative applications in all aspects. In particular, Unmanned Aerial Vehicles and Remote Sensing technologies have drawn attention due to their potential for non-destructive operations and reduced human interactions. Associated with promising demonstrations, challenges and opportunities have also emerged in applying the developments in practices over larger scales or for minor aspects of precision agriculture.

This Special Issue is devoted to bridging closer advanced developments of various technologies and all areas of research in agricultural activities. It is expected research studies that cover the development of technological solutions for precision agriculture, with a particular interest in the utilization of Unmanned Aerial Vehicles and Remote Sensing Systems. The application scope includes all aspects in Precision Agriculture, including but not limited to irrigation, fertilization, pest management, early disease detection, pre- and post-harvest processing, as well as yield and quality monitoring.

Dr. Jing Zhou
Dr. Lirong Xiang
Guest Editors

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 submissions that pass pre-check are 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. AgriEngineering is an international peer-reviewed open access quarterly 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

  • unmanned aerial vehicle
  • remote sensing
  • precision agriculture
  • data analysis and mining
  • precision farming

Published Papers (4 papers)

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Research

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14 pages, 3314 KiB  
Article
Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels
by José Donizete de Queiroz Otone, Gustavo de Faria Theodoro, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Job Teixeira de Oliveira, Izabela Cristina de Oliveira, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro and Fabio Henrique Rojo Baio
AgriEngineering 2024, 6(1), 330-343; https://doi.org/10.3390/agriengineering6010020 - 7 Feb 2024
Cited by 1 | Viewed by 821
Abstract
Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production [...] Read more.
Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle and Remote Sensing Systems Usage in Precision Agriculture)
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15 pages, 4075 KiB  
Article
Use of Unmanned Aerial Vehicle for Pesticide Application in Soybean Crop
by Luana de Lima Lopes, João Paulo Arantes Rodrigues da Cunha and Quintiliano Siqueira Schroden Nomelini
AgriEngineering 2023, 5(4), 2049-2063; https://doi.org/10.3390/agriengineering5040126 - 3 Nov 2023
Viewed by 1454
Abstract
The use of unmanned aerial vehicles (UAVs) for pesticide application has increased substantially. However, there is a lack of technical information regarding the optimal operational parameters. The aim of this study was to evaluate the quality of pesticide application on a soybean crop [...] Read more.
The use of unmanned aerial vehicles (UAVs) for pesticide application has increased substantially. However, there is a lack of technical information regarding the optimal operational parameters. The aim of this study was to evaluate the quality of pesticide application on a soybean crop using a UAV employing different spray nozzles. The experiments were conducted using a completely randomized design with four treatments and eight repetitions. The trial was conducted in a soybean growing area during the soybean reproductive stage (1.1 m tall). The treatments included aerial application (rate: 10 L hm−2) using an Agras MG1-P UAV with XR 11001 (flat fan), AirMix 11001 (air-induction flat fan), and COAP 9001 (hollow cone spray) nozzles; for comparison, ground application (rate of 100 L hm−2) using a constant pressure knapsack sprayer with an XR 110015 (flat fan) nozzle was performed. The deposition was evaluated by quantifying a tracer (brilliant blue) using spectrophotometry and analyzing the droplet spectrum using water-sensitive paper. Furthermore, the application quality was investigated using statistical process control methodology. The best deposition performance was exhibited by the application via UAV using the COAP 9001 and AirMix 11001 nozzles. For all the treatments, the process remained under statistical control, indicating commendable adherence to quality standards. The aerial application provided greater penetration of the spray into the crop canopy. With the use of the UAV, the coverage on the water-sensitive paper was <1%; moreover, the AirMix 11001 and XR 110015 nozzles had the lowest drift potential. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle and Remote Sensing Systems Usage in Precision Agriculture)
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21 pages, 6499 KiB  
Article
Statistics and 3D Modelling on Soil Analysis by Using Unmanned Aircraft Systems and Laboratory Data for a Low-Cost Precision Agriculture Approach
by Alessandro Mei, Alfonso Valerio Ragazzo, Elena Rantica and Giuliano Fontinovo
AgriEngineering 2023, 5(3), 1448-1468; https://doi.org/10.3390/agriengineering5030090 - 30 Aug 2023
Viewed by 1031
Abstract
The aim of this work was to elaborate a new methodology that can allow for the identification of the topsoil homogeneous area (tSHA) distribution along land parcels, supporting farmers in keeping low-cost, sustainable, and light logistic management of precision agriculture (PA) practices. This [...] Read more.
The aim of this work was to elaborate a new methodology that can allow for the identification of the topsoil homogeneous area (tSHA) distribution along land parcels, supporting farmers in keeping low-cost, sustainable, and light logistic management of precision agriculture (PA) practices. This paper shows the assessment of tSHA variability over two production units (PUs), considering radiometric response (optical camera), physicochemical (texture, pH, electrical conductivity), and statistical and geostatistical data analysis. By using unmanned aircraft systems (UASs) and laboratory analysis, our results revealed that the integration between UAS-RGB and physicochemical data can improve the estimation accuracy of tSHA distribution. Firstly, the UAS-RGB dataset was used to isolate bare soil from the vegetative radiometric contribution. Secondly, starting from statistical approaches (correlation matrices), the highest correlation with UAS-RGB and physicochemical data was stated. Thirdly, by using a geostatistical approach (ordinary cokriging), the map representing the tSHA variability was finally obtained. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle and Remote Sensing Systems Usage in Precision Agriculture)
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Review

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24 pages, 2720 KiB  
Review
Peculiarities of Unmanned Aerial Vehicle Use in Crop Production in Russia: A Review
by Marina Zvezdina, Yuliya Shokova and Sergey Lazarenko
AgriEngineering 2024, 6(1), 455-478; https://doi.org/10.3390/agriengineering6010028 - 21 Feb 2024
Viewed by 706
Abstract
This review article examines the potential for intensifying Russian crop production through digital transformation, particularly through the use of unmanned aerial vehicles (UAVs). (1) The importance of this topic is driven by declining food security in some parts of the world and the [...] Read more.
This review article examines the potential for intensifying Russian crop production through digital transformation, particularly through the use of unmanned aerial vehicles (UAVs). (1) The importance of this topic is driven by declining food security in some parts of the world and the Russian government’s goal to increase grain exports by 2050. (2) Comparisons of agriculture technologies suggest that the use of UAVs for crop treatment with agrochemicals is economically effective in certain cases. (3) Specifically, UAV treatment is advantageous for plots with irregular shapes, larger than 2 ha, and containing between 9 and 19% infertile land. It is also important to agree on the flight parameters of the UAV, such as speed and altitude, as well as the type of on-board sprayer and agrochemical. In case of insufficient funds or expertise, it is recommended to hire specialized companies. (4) The listed peculiarities of Russian crop production led to assumptions about the regions where the use of UAVs for agrochemical treatment of crops would be economically effective. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle and Remote Sensing Systems Usage in Precision Agriculture)
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