ArticleFrontiers in plant science2022
An intelligent monitoring system of diseases and pests on rice canopy.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Biotic Stress Resistance in Sweet Potato: Mechanisms, Perspectives, and Sustainable Production Strategies.Plants (Basel, Switzerland) · 2026Review
- Rice pest detection via multi-scale edge network and wavelet attention enhancement.Frontiers in plant science · 2026Article
- Article
- Detection technologies and sensing systems for crop pest identification and infestation severity prediction: a review.Frontiers in plant science · 2026Review
- Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility.Sensors (Basel, Switzerland) · 2025Article
- Mildew detection in rice grains based on computer vision and the YOLO convolutional neural network.Food science & nutrition · 2024Article
- Predicting rice diseases using advanced technologies at different scales: present status and future perspectives.aBIOTECH · 2023Review
- Aphid Recognition and Counting Based on an Improved YOLOv5 Algorithm in a Climate Chamber Environment.Insects · 2023Article
- A New Pest Detection Method Based on Improved YOLOv5m.Insects · 2023Article
- Efficient Windows malware identification and classification scheme for plant protection information systems.Frontiers in plant science · 2023Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Accurate and timely surveys of rice diseases and pests are important to control them and prevent the reduction of rice yields. The current manual survey method of rice diseases and pests is time-consuming, laborious, highly subjective and difficult to trace historical data. To address these issues, we developed an intelligent monitoring system for detecting and identifying the disease and pest lesions on the rice canopy. The system mainly includes a network camera, an intelligent detection model of diseases and pests on rice canopy, a web client and a server. Each camera of the system can collect rice images in about 310 m
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