Deep transfer learning for comprehensive diagnosis of cotton leaf pathologies.

Journal: Microbial pathogenesis
Published Date: Dec 19, 2025

Abstract

The cotton sector has recently encountered various obstacles, and traditional methods persist in the identification of cotton leaf diseases. This study has established an automated approach for diagnosing cotton leaf blast disease via deep learning methodologies and image processing. The research included deep learning architectures like Convolutional Neural Network, InceptionV3, ResNet50, VGG16, VGG19, and Xception. The extensive collection consists of over 4200 images, including around 3000 depicting cotton leaf blight and 1200 representing healthy leaves. The results demonstrated that the Convolutional Neural Network models InceptionV3, ResNet50, VGG16, and VGG19 attained final validation accuracies of 92.92 %, 64.1 %, 96.81 %, 95.42 %, and 95.97 %, respectively. The ResNet50 approach has exhibited greater accuracy than previous models, whereas the VGG19 model has achieved the second-highest accuracy. This research enhances precision agriculture by delivering a reliable and precise automated approach for predicting cotton diseases. Subsequent inquiries have been undertaken to enhance the precision and efficacy of deep learning models by the incorporation of cutting-edge technologies, including ResNet50, RegNet, EfficientNetB, and Vision Transformers. This study has resulted in a significant enhancement of cotton leaf diseases through identification with the model of surpassing existing leading methodologies in accuracy, complexity, and inference speed. Thus, the creation of these reliable and precise automated diagnostic tools for cotton leaf diseases markedly enhances precision agriculture. The current investigation could equip farmers with a dependable and effective method to detect and mitigate cotton leaf diseases prior to inflicting significant harm on cotton crops.

Authors

  • Abdul Ghafar
    Melbourne Veterinary School, The University of Melbourne, Werribee, Victoria, Australia.
  • Caikou Chen
    College of Information Engineering, Yangzhou University, Yangzhou 225009, China.
  • Irshad Ahmad
    Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia.
  • Muhammad Qasim
    Microelement Research Center, College of Resources and Environment, Huazhong Agricultural University, Wuhan, Hubei-40070, China. Electronic address: [email protected].
  • Shoaib Ahmed
    Faculty of Pharmacy, The University of Lahore, Lahore, Pakistan.
  • Faheem Ahmed Rajput
    Department of Entomology, Sindh Agriculture University, Tandojam 70050, Sindh, Pakistan. Electronic address: [email protected].
  • Usman Zulfiqar
    Department of Business Administration, Lahore Leads University, Lahore, Pakistan.
  • Tabarak Malik
    Department of Biochemistry, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.

Keywords

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