Robust Cotton Leaf Disease Classification Using Hybrid Deep Learning Approach with Multi- Loss Function

Key Points

• Hybrid deep learning models enhance classification accuracy of cotton leaf diseases significantly.
• The multi-loss function approach improved model performance, achieving up to 92% accuracy in tests.
• Analysis incorporates various datasets of cotton leaf images to develop a solid classification framework.
• Improved methods for disease classification in plants may support better crop management policies.

Abstract