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

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.

Plant phenotyping relevance

綿花の病害を画像から分類する軽量深層学習モデルと画像データセットを開発・評価しており、植物の病害状態を抽出するフェノタイピング手法が中心である。

Code and data availability

The paper's authors publicly released their self-constructed cotton disease/pest image dataset CCDPHD-11 (18,953 images, 11 classes) on GitHub, as stated in the Data Availability Statement. No code or trained model deposit is mentioned.