Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling
Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) tec...
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MDPI AG
2025-05-01
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| author | Shanmugam Vijayakumar Palanisamy Shanmugapriya Pasoubady Saravanane Thanakkan Ramesh Varunseelan Murugaiyan Selvaraj Ilakkiya |
| author_facet | Shanmugam Vijayakumar Palanisamy Shanmugapriya Pasoubady Saravanane Thanakkan Ramesh Varunseelan Murugaiyan Selvaraj Ilakkiya |
| author_sort | Shanmugam Vijayakumar |
| collection | DOAJ |
| description | Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders are deployed on large-scale farms in developed countries. Similarly, UAVs are gaining popularity in many countries, particularly in Asia, for weed monitoring and herbicide application. Despite advancements in robotic and UAV weed control, their large-scale adoption remains limited. The reasons for this slow uptake and the barriers to widespread implementation are not fully understood. To address this knowledge gap, our review analyzes 155 articles and provides a comprehensive understanding of PWC challenges and needed interventions for scaling. This review revealed that AI-driven weed mapping in robots and UAVs struggles with data (quality, diversity, bias) and technical (computation, deployment, cost) barriers. Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. Specifically, robotic weed control adoption is hindered by challenges in weed recognition, navigation complexity, limited battery life, data management (connectivity), fragmented farms, high costs, and limited digital literacy. Scaling requires advancements in weed detection and energy efficiency, development of affordable robots with shared service models, enhanced farmer training, improved rural connectivity, and precise engineering solutions. Similarly, UAV adoption in agriculture faces hurdles such as regulations (permits), limited payload and battery life, weather dependency, spray drift, sensor accuracy, lack of skilled operators, high initial and operational costs, and absence of standardized protocol. Scaling requires financing (subsidies, loans), favorable regulations (streamlined permits, online training), infrastructure development (service providers, hiring centers), technological innovation (interchangeable sensors, multipurpose UAVs), and capacity building (farmer training programs, awareness initiatives). |
| format | Article |
| id | doaj-art-62bf3793ff7749e89269e2e7813291e7 |
| institution | OA Journals |
| issn | 2813-477X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | NDT |
| spelling | doaj-art-62bf3793ff7749e89269e2e7813291e72025-08-20T02:21:46ZengMDPI AGNDT2813-477X2025-05-01321010.3390/ndt3020010Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for ScalingShanmugam Vijayakumar0Palanisamy Shanmugapriya1Pasoubady Saravanane2Thanakkan Ramesh3Varunseelan Murugaiyan4Selvaraj Ilakkiya5ICAR-Indian Institute of Rice Research, Hyderabad 500 030, Telangana, IndiaDepartment of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, IndiaDepartment of Agronomy, Pandit Jawaharlal Nehru College of Agriculture & Research Institute, Karaikal 609 603, U.T. of Puducherry, IndiaDepartment of Agronomy, Anbil Dharmalingam Agricultural College and Research Institute, Tiruchirappalli 620 027, Tamil Nadu, IndiaInternational Rice Research Institute, Los Baños 4031, Laguna, PhilippinesDepartment of Aerospace Engineering, MIT Campus, Anna University, Chennai 600 044, Tamil Nadu, IndiaWeeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders are deployed on large-scale farms in developed countries. Similarly, UAVs are gaining popularity in many countries, particularly in Asia, for weed monitoring and herbicide application. Despite advancements in robotic and UAV weed control, their large-scale adoption remains limited. The reasons for this slow uptake and the barriers to widespread implementation are not fully understood. To address this knowledge gap, our review analyzes 155 articles and provides a comprehensive understanding of PWC challenges and needed interventions for scaling. This review revealed that AI-driven weed mapping in robots and UAVs struggles with data (quality, diversity, bias) and technical (computation, deployment, cost) barriers. Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. Specifically, robotic weed control adoption is hindered by challenges in weed recognition, navigation complexity, limited battery life, data management (connectivity), fragmented farms, high costs, and limited digital literacy. Scaling requires advancements in weed detection and energy efficiency, development of affordable robots with shared service models, enhanced farmer training, improved rural connectivity, and precise engineering solutions. Similarly, UAV adoption in agriculture faces hurdles such as regulations (permits), limited payload and battery life, weather dependency, spray drift, sensor accuracy, lack of skilled operators, high initial and operational costs, and absence of standardized protocol. Scaling requires financing (subsidies, loans), favorable regulations (streamlined permits, online training), infrastructure development (service providers, hiring centers), technological innovation (interchangeable sensors, multipurpose UAVs), and capacity building (farmer training programs, awareness initiatives).https://www.mdpi.com/2813-477X/3/2/10UAVsrobotsherbicidelaser weedingmachine learning |
| spellingShingle | Shanmugam Vijayakumar Palanisamy Shanmugapriya Pasoubady Saravanane Thanakkan Ramesh Varunseelan Murugaiyan Selvaraj Ilakkiya Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling NDT UAVs robots herbicide laser weeding machine learning |
| title | Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling |
| title_full | Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling |
| title_fullStr | Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling |
| title_full_unstemmed | Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling |
| title_short | Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling |
| title_sort | precision weed control using unmanned aerial vehicles and robots assessing feasibility bottlenecks and recommendations for scaling |
| topic | UAVs robots herbicide laser weeding machine learning |
| url | https://www.mdpi.com/2813-477X/3/2/10 |
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