Year: 2025
Type: article
Abstract: Insect pests pose a severe threat to global food security, with estimated losses of 20-40% of crop yields each year. Traditional monitoring relies on manual scouting and simple weather models, often reacting too late to prevent damage. In contrast, modern systems combine Unmanned Aerial Vehicles (drones) with artificial intelligence (AI) to create rapid, high-resolution insect surveillance and forecasting. Drones equipped with advanced sensors (multispectral, thermal, RGB cameras, etc.) survey fields and orchards, while AI algorithms process the imagery and sensor data to identify pests and predict infestation trends. This synergy enables early outbreak detection and automated alerts, allowing farmers to target only affected areas and significantly reduce pesticide use. In this article, we review the theoretical foundations of AI-based image recognition and sensor data analysis, and we describe the latest drone monitoring platforms. We then examine practical applications in India and worldwide, showing how AI-enabled drones improve pest forecasting and crop protection. The benefits improved yields, resource efficiency, and faster response are highlighted, along with current challenges such as technical and regulatory hurdles. Finally, we discuss future trends in precision agriculture, illustrating how emerging drone-AI innovations will further enhance integrated pest management systems.
Authors: Sneha Katherine, Swadhin Kumar Swain, Chinmoy Deori, Suraksha M Gouli, KS Pagire, Bhaswati Saikia, Tejas Ashok Bhosale, Avinash Chauhan, Narpat Choudhary, Bhavesh Choudhary
Institutions: Chaudhary Charan Singh Haryana Agricultural University, University of Agricultural Sciences, Bangalore, Dr Panjabrao Deshmukh Krishi Vidyapeeth, Assam Agricultural University, Veer Chandra Singh Garhwali Uttarakhand University of Horticulture & Forestry, Nagaland University, Odisha University of Agriculture and Technology, Central Agricultural University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4413293697