Intelligent Detection System for Rice Pests and Diseases Based on UAV and Deep Learning
Year: 2026
Type: article
Abstract: China possesses a vast rice-planting area, and pests and diseases constitute critical factors restricting rice yield and quality. Traditional manual field inspection for pest and disease monitoring suffers from low efficiency, strong subjectivity, limited coverage, delayed prevention and control, and improper pesticide abuse. Combined with UAV remote sensing, multi-sensor acquisition and lightweight YOLO deep-learning target-detection algorithms, this paper develops the “Agri-IntelliDet” rice pest-disease intelligent detection system. A four-layer architecture including data acquisition, transmission, processing and application is constructed to realize automatic acquisition of rice-field images, intelligent identification and positioning of pests and diseases, data analysis, visual display and automatic export of monitoring reports. Adaptable to large-scale farms, household farmers, plant protection departments and agricultural research experimental plots, the system achieves a mean detection accuracy of 85%-95% in tests. It greatly improves field pest-disease monitoring efficiency and reduces pesticide input, providing technical support for green rice prevention-control, smart-agriculture implementation and national food security guarantee.
Authors: Kunrui Li, Hao, Yang, Bowen, Ding, Zhixuan Li, Han Sun
Institution: Changchun University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7223218472