Cotton Care: Harnessing Transfer Learning for Real-Time Health Assessment with Mobile Devices

Cotton frequently experiences substantial yield reduction due to disease afflictions. This paper introduces a proof-of-concept mobile application that employs transfer learning with pre-trained MobileNetV3 and NASNetMobile models to detect cotton plant diseases. The system addresses a four-class problem, identifying the health status of cotton plants or leaves. Using TensorFlow, these models are refined with publicly available data and deployed on an iOS app. This study also provides a performance assessment of both models, previously unexplored in this specific context. The methodology, showcasing significant potential for real-world implementations, recorded overall accuracies of 97.7% for NASNetMobile and 96.7% for MobileNetV3.