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Application of UAV and Sensing in Precision Agriculture
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Application of UAV and Sensing in Precision Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 9496

Special Issue Editors

Dr. Christopher C. Stewart

E-Mail Website
Guest Editor
Computer Science and Engineering Department, The Ohio State University, 395 Dreese Laboratories2015 Neil Avenue, Columbus, OH 43210-1277, USA
Interests: distributed systems; performance modeling; autonomic computing; sustainable computing; data driven; data science
Dr. Huiping Tsai

E-Mail Website
Guest Editor
Department of Civil Engineering, National Chung Hsing University, Taichung 402, Taiwan
Interests: UAV photogrammetry; image processing; water vapor

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAV) can fly between way points without a human in the cockpit, drastically reducing the cost of aerial surveillance in precision agriculture. Aerial surveillance data are now available for every type of field operation, from scouting crop yields to detecting emerging pestilence and crop diseases to assessing the impact of floods and natural disasters to tracking livestock. However, farmers need analytic tools to translate data sensed by UAV into actions that will improve agricultural output. These tools must (1) provide robust insights for multiple operations, geographic regions, topological factors, and business models, (2) employ understandable and explainable techniques that build trust, and (3) have practical pathways to real-world use.

This Special Issue calls for papers related to all aspects of UAV in precision agriculture, including:

  • (1) New sensors capable of being deployed on unmanned aerial vehicles;
  • (2) Novel engineering solutions that fundamentally extend extant sensing technologies;
  • (3) Low-level algorithms to manage the use of multiple sensors over long missions for efficacy, efficiency, and cost effectiveness;
  • (4) Novel applications that transform UAV data into actionable insights for precision agriculture;
  • (5) New approaches to existing applications that improve efficacy, efficiency or end-to-end farm costs;

(6) Strategies to translate applications based on UAV sensing into practice, especially strategies that consider federal regulations, understandable models, ethical issues, and end-to-end cost.

Dr. Christopher C. Stewart
Dr. Huiping Tsai
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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 vehicles
  • sensors
  • sensing technologies
  • precision agriculture

Published Papers (4 papers)

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Research

12 pages, 668 KiB  
Article
AI-Driven Validation of Digital Agriculture Models
by Eduardo Romero-Gainza and Christopher Stewart
Sensors 2023, 23(3), 1187; https://doi.org/10.3390/s23031187 - 20 Jan 2023
Cited by 3 | Viewed by 1723
Abstract
Digital agriculture employs artificial intelligence (AI) to transform data collected in the field into actionable crop management. Effective digital agriculture models can detect problems early, reducing costs significantly. However, ineffective models can be counterproductive. Farmers often want to validate models by spot checking [...] Read more.
Digital agriculture employs artificial intelligence (AI) to transform data collected in the field into actionable crop management. Effective digital agriculture models can detect problems early, reducing costs significantly. However, ineffective models can be counterproductive. Farmers often want to validate models by spot checking their fields before expending time and effort on recommended actions. However, in large fields, farmers can spot check too few areas, leading them to wrongly believe that ineffective models are effective. Model validation is especially difficult for models that use neural networks, an AI technology that normally assesses crops health accurately but makes inexplicable recommendations. We present a new approach that trains random forests, an AI modeling approach whose recommendations are easier to explain, to mimic neural network models. Then, using the random forest as an explainable white box, we can (1) gain knowledge about the neural network, (2) assess how well a test set represents possible inputs in a given field, (3) determine when and where a farmer should spot check their field for model validation, and (4) find input data that improve the test set. We tested our approach with data used to assess soybean defoliation. Using information from the four processes above, our approach can reduce spot checks by up to 94%. Full article
(This article belongs to the Special Issue Application of UAV and Sensing in Precision Agriculture)
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16 pages, 1571 KiB  
Article
Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
by Saqib Majeed, Adnan Sohail, Kashif Naseer Qureshi, Saleem Iqbal, Ibrahim Tariq Javed, Noel Crespi, Wamda Nagmeldin and Abdelzahir Abdelmaboud
Sensors 2022, 22(16), 6130; https://doi.org/10.3390/s22166130 - 16 Aug 2022
Cited by 2 | Viewed by 1681
Abstract
Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to [...] Read more.
Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection. Full article
(This article belongs to the Special Issue Application of UAV and Sensing in Precision Agriculture)
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19 pages, 9147 KiB  
Article
Web and MATLAB-Based Platform for UAV Flight Management and Multispectral Image Processing
by Nourdine Aliane, Carlos Quiterio Gomez Muñoz and Javier Sánchez-Soriano
Sensors 2022, 22(11), 4243; https://doi.org/10.3390/s22114243 - 02 Jun 2022
Cited by 6 | Viewed by 2547
Abstract
The deployment of any UAV application in precision agriculture involves the development of several tasks, such as path planning and route optimization, images acquisition, handling emergencies, and mission validation, to cite a few. UAVs applications are also subject to common constraints, such as [...] Read more.
The deployment of any UAV application in precision agriculture involves the development of several tasks, such as path planning and route optimization, images acquisition, handling emergencies, and mission validation, to cite a few. UAVs applications are also subject to common constraints, such as weather conditions, zonal restrictions, and so forth. The development of such applications requires the advanced software integration of different utilities, and this situation may frighten and dissuade undertaking projects in the field of precision agriculture. This paper proposes the development of a Web and MATLAB-based application that integrates several services in the same environment. The first group of services deals with UAV mission creation and management. It provides several pieces of flight conditions information, such as weather conditions, the KP index, air navigation maps, or aeronautical information services including notices to Airmen (NOTAM). The second group deals with route planning and converts selected field areas on the map to an UAV optimized route, handling sub-routes for long journeys. The third group deals with multispectral image processing and vegetation indexes calculation and visualizations. From a software development point of view, the app integrates several monolithic and independent programs around the MATLAB Runtime package with an automated and transparent data flow. Its main feature consists in designing a plethora of executable MATLAB programs, especially for the route planning and optimization of UAVs, images processing and vegetation indexes calculations, and running them remotely. Full article
(This article belongs to the Special Issue Application of UAV and Sensing in Precision Agriculture)
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19 pages, 4996 KiB  
Article
Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors
by Xiaoyu Song, Guijun Yang, Xingang Xu, Dongyan Zhang, Chenghai Yang and Haikuan Feng
Sensors 2022, 22(2), 549; https://doi.org/10.3390/s22020549 - 11 Jan 2022
Cited by 9 | Viewed by 2274
Abstract
A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward [...] Read more.
A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods. Full article
(This article belongs to the Special Issue Application of UAV and Sensing in Precision Agriculture)
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