Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework

Abstract

Cotton crops, often called "white gold," face significant production challenges, primarily due to various leaf-affecting diseases. As a major global source of fiber, timely and accurate disease identification is crucial to ensure optimal yields and maintain crop health. While deep learning and machine learning techniques have been explored to address this challenge, there remains a gap in developing lightweight models with fewer parameters which could be computationally effective for agricultural practitioners. To address this, we propose an innovative deep learning framework integrating a subset of trainable layers from MobileNet, transfer learning, data augmentation, a learning rate decay schedule, model checkpoints, and early stopping mechanisms. Our model demonstrates exceptional performance, accurately classifying seven cotton disease types with an overall accuracy of 98.42% and class-wise precision ranging from 96% to 100%. This results in significantly enhanced efficiency, surpassing recent approaches in accuracy and model complexity. The existing models in the literature have yet to attain such high accuracy, even when tested on data sets with fewer disease types. The substantial performance improvement, combined with the lightweight nature of the model, makes it practically suitable for real-world applications in smart farming. By offering a high-performing and efficient solution, our framework can potentially address challenges in cotton cultivation, contributing to sustainable agricultural practices.