Smart Protection of Cotton Crops: A Review of IoT Sensor Networks and ML-Based Pest Decision Support Systems

DOI: 10.22161/ijeab.113.17

Abstract:
Cotton is found to be a globally significant commercial crop, with India contributing approximately 22% of the world's production and often referred to as “white gold”. But cotton is highly vulnerable to insect damage, so early diagnosis is necessary to reduce disease transmission and enhance treatment effectiveness. Traditional methods are labour-intensive, which often results in delayed responses and significant crop losses. This study explores various pests found in India and their management in the cotton field by using different sensors and a smart detection-response system utilising IoT sensor networks and machine learning to monitor and classify pests, specifically targeting the Bollworm complex and whiteflies. The proposed architecture comprises different sections: Field traps, Gateway, central control system, Drone spraying, and Farmer sections, integrating motion-detecting sensor traps, communication modules, and an autonomous aerial vehicle for targeted pesticide spraying. The proposed system optimises flight paths for rapid response and includes protocols for handling false alarms, leveraging camera-equipped drones and human assistance. While the integration of IoT offers precision and accuracy, challenges such as hardware durability in harsh environments, networking interference in rural areas, and the need for farmer education remain critical hurdles for widespread adoption.

Keywords:
Cotton pests, Drone spraying, IoT system, Sensors.

Article Info:
Received: 09 May 2026; Received in revised form: 02 Jun 2026; Accepted: 08 Jun 2026; Available online: 16 Jun 2026

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