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Resource-Efficient Cotton Network: A Lightweight Deep Learning Framework for Cotton Disease and Pest Classification.

Zhengle Wang¹, Heng-Wei Zhang², Ying-Qiang Dai³

¹College of Information and Electrical Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China.

Plants (Basel, Switzerland)

|July 12, 2025

Summary

A new lightweight model, RF-Cott-Net, accurately detects cotton diseases and pests using an open-source dataset. This technology supports crop management and breeding research with efficient, real-time performance on edge devices.

Keywords:

MobileViT cotton disease and pest diagnosis images classification lightweight model

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Cotton cultivation faces significant yield and quality losses due to diseases and pests.
Accurate and rapid diagnosis is crucial for effective disease management and breeding programs.

Purpose of the Study:

To develop a lightweight, efficient deep learning model for diagnosing cotton diseases and pests.
To introduce an open-source dataset (CCDPHD-11) for training and evaluating cotton disease detection models.

Main Methods:

Proposed RF-Cott-Net model, utilizing a MobileViTv2 backbone with early exit and quantization-aware training (QAT).
Developed and utilized the CCDPHD-11 dataset comprising 11 categories of cotton diseases.
Evaluated model performance based on accuracy, F1-score, precision, and recall.

Main Results:

RF-Cott-Net achieved high accuracy (98.4%), F1-score (98.4%), precision (98.5%), and recall (98.3%) on the CCDPHD-11 dataset.
The model is highly efficient with 4.9M parameters, 310M FLOPs, 3.8ms inference time, and 4.8MB storage.
Demonstrated suitability for deployment on agricultural edge devices for real-time detection.

Conclusions:

RF-Cott-Net offers a highly accurate and efficient solution for automated in-field detection of cotton diseases and pests.
The model's lightweight nature and performance support practical applications in agriculture, aiding crop management and genetic research.