Leveraging Inception-V3 and Transfer Learning for Early Detection and Classification of Cotton Crop Diseases

Background: Cotton is an important crop globally and early detection of plant diseases is crucial for maintaining yields. Traditional methods for disease detection are manual and inefficient, highlighting the need for advanced technology like AI to enhance productivity. Methods: The study utilized the Inception-v3 deep learning model along with techniques such as transfer learning and hyper-parameter tuning. These approaches helped design an efficient system to classify whether a cotton plant is healthy or diseased. Comparisons were made with other pre-trained models like VGG16, ResNet50 and ResNet152V2. Result: The Inception-v3 model showed exceptional performance: • Achieved 87.52% accuracy without tuning. • Achieved 98.85% accuracy after hyper-parameter tuning, marking an improvement of ~11%. This approach also demonstrated faster and more precise predictions for diseases like bacterial blight, army worms and aphids. It supports sustainable farming by reducing chemical usage while maintaining crop quality and yield.