ArticlePlants (Basel, Switzerland)2025
Resource-Efficient Cotton Network: A Lightweight Deep Learning Framework for Cotton Disease and Pest Classification.
Article in Plants (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- TriAttnNet based deep learning model for automated cotton pest detection and disease classification.Scientific reports · 2026Article
- Lightweight Cross-Domain Few-Shot Plant Disease Recognition Through Target-Domain Statistical Calibration.Sensors (Basel, Switzerland) · 2026Article
- A hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification.BMC plant biology · 2026Article
- Towards Robust UAV Navigation in Agriculture: Key Technologies, Application, and Future Directions.Plants (Basel, Switzerland) · 2026Review
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- An attention enhanced CNN ensemble for interpretable and accurate cotton leaf disease classification.Scientific reports · 2026Article
- Real-Time Panoramic Surveillance Video Stitching Method for Complex Industrial Environments.Sensors (Basel, Switzerland) · 2025Article
- DLCPD-25: A Large-Scale and Diverse Dataset for Crop Disease and Pest Recognition.Sensors (Basel, Switzerland) · 2025Article
- Spatiotemporal Risk Assessment of H5 Avian Influenza in China: An Interpretable Machine Learning Approach to Uncover Multi-Scale Drivers.Animals : an open access journal from MDPI · 2025Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Cotton is the most widely cultivated natural fiber crop worldwide, yet it is highly susceptible to various diseases and pests that significantly compromise both yield and quality. To enable rapid and accurate diagnosis of cotton diseases and pests-thus supporting the development of effective control strategies and facilitating genetic breeding research-we propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories. Built upon the MobileViTv2 backbone, RF-Cott-Net integrates an early exit mechanism and quantization-aware training (QAT) to enhance deployment efficiency without sacrificing accuracy. Experimental results on CCDPHD-11 demonstrate that RF-Cott-Net achieves an accuracy of 98.4%, an F1-score of 98.4%, a precision of 98.5%, and a recall of 98.3%. With only 4.9 M parameters, 310 M FLOPs, an inference time of 3.8 ms, and a storage footprint of just 4.8 MB, RF-Cott-Net delivers outstanding accuracy and real-time performance, making it highly suitable for deployment on agricultural edge devices and providing robust support for in-field automated detection of cotton diseases and pests.
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