FULL-LENGTH RESEARCH ARTICLE Development of an IOT-Based Semi-Autonomous Vehicle Sprayer Mrutyunjay Padhiary 1 • Sunny V. Tikute 1 • Debapam Saha 2 • Javed Akhtar Barbhuiya 1 • Laxmi Narayan Sethi 1 Received: 13 December 2023 / Accepted: 18 June 2024 / Published online: 27 June 2024 Ó The Author(s), under exclusive licence to National Academy of Agricultural Sciences 2024 Abstract Mechanization is essential for improving farming processes to achieve the best possible use of resources, reduce costs, and increase operational efficiency. Novel spraying methods are crucial for reducing costs, minimizing chemical effects, and improving operator safety. In response, a semiautonomous vehicle sprayer (SAVS) has been developed, featuring an 800 9 500 9 400 mm primary frame, four wheels, and a 15-L pesticide tank, alongside front wheel steering and rear wheel propulsion systems, and a spraying unit. Equipped with an integrated anemometer, pressure gauge, and flow meter, all linked to a microprocessor, the SAVS operates on four 10,000 mAh LiPo (Lithium Polymer) batteries managed through the Blynk platform. This setup enables real-time decision-making and precise control over variables such as pressure, speed, and discharge. Integrated electronic valves regulate nozzle pressure (adjustable from 100 to 400 kPa) and nozzle spray discharge (controlled between 60 and 90 L/h). The SAVS can maintain a constant application rate (240–260 L/ha) by adjusting discharge and pressure based on ground velocity (4–6 km/h), thus minimizing drift and ensuring uniform spraying. With a percent drift of 9–13.2%, the SAVS demonstrates higher spray uniformity (96.82–97.67%), field capacity (0.2–0.3 ha/h), and field efficiency (65%) compared to traditional manually operated backpack sprayers. With enhanced operator comfort, the SAVS represents a cost-effective solution for precision agriculture without compromising field capacity or safety. Keywords Semi-autonomous  Battery-operated sprayer  Precision agriculture  Coefficient of uniformity  Application rate Introduction India has a rich agricultural heritage, with vast expanses of agricultural land serving as the backbone of their cultures and traditions. However, the agricultural sector faces numerous challenges, with pest and insect attacks being a major concern for farmers [11, 34]. Traditional methods of pest control involving the use of chemicals come with significant costs and potential long-term risks to both the environment and human health. To tackle these challenges and revolutionize farming practices, advanced technologies [33] like the Internet of Things (IoT) [10], machine learning [36], computer vision, and neural networks have been integrated into precision agriculture. Precision agri- culture [18] offers a transformative approach to farming, optimizing fertility rates, increasing crop yield, minimizing resource wastage, and improving time management even in remote locations and hilly terrains [23]. Given the global food demand and the need to avert potential food crises, technological interventions that enhance crop productivity [35] have become imperative. One notable innovation in the agricultural domain is the development of high-tech autonomous [3] precision sprayers. These sprayers & Mrutyunjay Padhiary mrutyu@gmail.com 1 Farm Machinery and Power Lab, Department of Agricultural Engineering, TSSOT, Assam University, Silchar 788011, India 2 Department of Agricultural and Food Engineering, IIT Kharagpur, Kharagpur 721302, India 123 Agric Res (March 2025) 14(1):229–239 https://doi.org/10.1007/s40003-024-00760-4