Cotton Vision: A Machine Learning-Based App for Rapid Diagnosis of Cotton Diseases.

Abstract

Abstract: This The detrimental impact of bacterial and fungal diseases on cotton crop yields and profitability underscores the urgency for rapid and precise field diagnosis. This paper introduces CottonVision, a pioneering mobile application leveraging deep learning for real-time identification of cotton diseases from images. By enabling farmers to capture smartphone photos of leaves, the system employs a robust convolutional neural network, specifically an Inception-v3 model, trained on a comprehensive dataset of over 2300 cotton crop images. The application swiftly classifies these images into four distinct categories: diseased cotton leaf, diseased cotton plant, Fresh cotton leaf, and Fresh cotton plant. Deployed on Android devices via TensorFlow Lite, the optimized model boasts a remarkable 97% test accuracy. CottonVision serves as an indispensable tool, furnishing farmers with instant diagnostic results crucial for early intervention. By facilitating prompt identification of emerging infections, the application aids in curtailing further spread and implementing timely control measures. The user-friendly interface of CottonVision offers an accessible and practical solution, empowering growers with real-time decision support for efficient disease management in cotton crops.