Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm.

ABSTRACT: Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. Such automation not only assists farmers in mitigating the effects of the disease but also conserves resources in terms of labor and fertilizer costs. However, accurate classification of multiple diseases simultaneously in cotton remains challenging due to multiple factors, including class imbalance, variation in disease symptoms, and the need for real-time detection, as most existing datasets are acquired under controlled conditions. This

SUBMITTER: Aslam A 

PROVIDER: S-EPMC12112145 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature