Hybrid Approach of Cotton Disease Detection f...

Hybrid Approach of Cotton Disease Detection for Enhanced Crop Health and Yield

Author Name : Harsh Vijay Mahajan, Krushnal Sunil Patil, Mahesh Madhukar Suryavanshi, Abhishek Sopan Khode, Dr. Sivaram Ponnusamy

ABTRACT Plant diseases can have devastating effects on crops, leading to significant economic losses and food insecurity. Early detection and classification of plant diseases are crucial for effective management and prevention. This paper proposes a deep learningbased approach for plant disease classification and detection using Convolutional Neural Networks (CNNs) and transfer learning. We employ pre-trained CNN models and fine-tune them on a dataset of plant images with various diseases. Our results show that the transfer learning approach achieves high accuracy (95.6%) in classifying plant diseases, outperforming traditional machine learning methods. We also investigate the use of data augmentation and transfer learning to overcome the issue of limited dataset size. The proposed system has the potential to assist farmers, researchers, and policymakers in monitoring and managing plant diseases, ultimately improving crop yields and food security. Keywords: Plant disease classification, Deep learning, Convolutional Neural Networks (CNNs), Transfer learning.