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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: 30 September 2025 | Viewed by 16250

Special Issue Editors


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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

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

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Keywords

  • unmanned aerial vehicles
  • sensors
  • sensing technologies
  • precision agriculture

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Published Papers (7 papers)

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Research

29 pages, 9019 KiB  
Article
Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms
by Zhong-Han Zhuang, Hui-Ping Tsai and Chung-I Chen
Sensors 2025, 25(7), 1966; https://doi.org/10.3390/s25071966 - 21 Mar 2025
Viewed by 242
Abstract
Tea (Camellia sinensis L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan’s annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and quality, necessitating an accurate real-time monitoring system to enhance [...] Read more.
Tea (Camellia sinensis L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan’s annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and quality, necessitating an accurate real-time monitoring system to enhance plantation management and production stability. This study surveys tea plantations at low, mid-, and high elevations in Nantou County, central Taiwan, collecting data from 21 fields using conventional farming methods (CFMs), which emphasize intensive management, and agroecological farming methods (AFMs), which prioritize environmental sustainability. This study integrates leaf area index (LAI), photochemical reflectance index (PRI), and quantum yield of photosystem II (ΦPSII) data with unmanned aerial vehicles (UAV)-derived visible-light and multispectral imagery to compute color indices (CIs) and multispectral indices (MIs). Using feature ranking methods, an optimized dataset was developed, and the predictive performance of eight regression algorithms was assessed for estimating tea plant physiological parameters. The results indicate that LAI was generally lower in AFMs, suggesting reduced leaf growth density and potential yield differences. However, PRI and ΦPSII values revealed greater environmental adaptability and potential long-term ecological benefits in AFMs compared to CFMs. Among regression models, MIs provided greater stability for tea plant physiological parameters, whereas feature ranking methods had minimal impact on accuracy. XGBoost outperformed all models in predicting parameters, achieving optimal results for (1) LAI: R2 = 0.716, RMSE = 1.01, MAE = 0.683, (2) PRI: R2 = 0.643, RMSE = 0.013, MAE = 0.009, and (3) ΦPSII: R2 = 0.920, RMSE = 0.048, MAE = 0.013. Overall, we highlight the effectiveness of integrating gradient boosting models with multispectral data to capture tea plant physiological characteristics. This study develops generalizable predictive models for tea plant physiological parameter estimation and advances non-contact crop physiological monitoring for tea plantation management, providing a scientific foundation for precision agriculture applications. Full article
(This article belongs to the Special Issue Application of UAV and Sensing in Precision Agriculture)
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24 pages, 1256 KiB  
Article
Automatic Cleaning of Time Series Data in Rural Internet of Things Ecosystems That Use Nomadic Gateways
by Jerzy Dembski, Agata Kołakowska and Bogdan Wiszniewski
Sensors 2025, 25(1), 189; https://doi.org/10.3390/s25010189 - 1 Jan 2025
Viewed by 672
Abstract
A serious limitation to the deployment of IoT solutions in rural areas may be the lack of available telecommunications infrastructure enabling the continuous collection of measurement data. A nomadic computing system, using a UAV carrying an on-board gateway, can handle this; it leads, [...] Read more.
A serious limitation to the deployment of IoT solutions in rural areas may be the lack of available telecommunications infrastructure enabling the continuous collection of measurement data. A nomadic computing system, using a UAV carrying an on-board gateway, can handle this; it leads, however, to a number of technical challenges. One is the intermittent collection of data from ground sensors governed by weather conditions for the UAV measurement missions. Therefore, each sensor should be equipped with software that allows for the cleaning of collected data before transmission to the fly-over nomadic gateway from erroneous, misleading, or otherwise redundant data—to minimize their volume and fit them in the limited transmission window. This task, however, may be a barrier for end devices constrained in several ways, such as limited energy reserve, insufficient computational capability of their MCUs, and short transmission range of their RAT modules. In this paper, a comprehensive approach to these problems is proposed, which enables the implementation of an anomaly detector in time series data with low computational demand. The proposed solution uses the analysis of the physics of the measured signals and is based on a simple anomaly model whose parameters can be optimized using popular AI techniques. It was validated during a full 10-month vegetation period in a real Rural IoT system deployed by Gdańsk Tech. Full article
(This article belongs to the Special Issue Application of UAV and Sensing in Precision Agriculture)
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24 pages, 51328 KiB  
Article
A Shortest Distance Priority UAV Path Planning Algorithm for Precision Agriculture
by Guoqing Zhang, Jiandong Liu, Wei Luo, Yongxiang Zhao, Ruiyin Tang, Keyu Mei and Penggang Wang
Sensors 2024, 24(23), 7514; https://doi.org/10.3390/s24237514 - 25 Nov 2024
Cited by 1 | Viewed by 1268
Abstract
Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start [...] Read more.
Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions. To address these challenges, this study proposes an enhanced Q-learning algorithm designed to optimize UAV path planning by combining static and dynamic obstacle avoidance features. A shortest distance priority (SDP) strategy is integrated into the learning process to minimize the distance the UAV must travel to reach the target. In addition, the root mean square propagation (RMSP) method is used to dynamically adjust the learning rate according to gradient changes, which accelerates the learning process and improves path planning efficiency. In this study, firstly, the proposed method was compared with state-of-the-art path planning techniques (including A-star, Dijkstra, and traditional Q-learning) in terms of learning time and path length through a grid-based 2D simulation environment. The results showed that the proposed method significantly improved performance compared to existing methods. In addition, 3D simulation experiments were conducted in the AirSim virtual environment. Due to the complexity of the 3D state, a deep neural network was used to calculate the Q-value based on the proposed algorithm. The results indicate that the proposed method can achieve the shortest path planning and obstacle avoidance operations in an orchard 3D simulation environment. Therefore, drones equipped with this algorithm are expected to make outstanding contributions to the development of precision agriculture through intelligent navigation and obstacle avoidance. Full article
(This article belongs to the Special Issue Application of UAV and Sensing in Precision Agriculture)
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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 8 | Viewed by 2872
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 7 | Viewed by 2665
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 - 2 Jun 2022
Cited by 10 | Viewed by 3523
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 13 | Viewed by 3173
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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