AI based Efficient Analysis and Monitoring of Sugarcane Buds Detection and Leaf Disease Classification

Sugarcane is a significant crop, and its productivity is greatly affected by the early identification of bud problems and leaf diseases. The challenge with present agricultural practices is that these issues are often identified at late stages, which leads to loss of yield. This study puts forward an AI-based system for efficient analysis and monitoring of sugarcane bud detection and leaf disease classification. The system uses Convolutional Neural Networks (CNNs) to detect unhealthy buds and classify leaf diseases at an early stage. The proposed solution makes use of image processing techniques for capturing high-resolution images of sugarcane buds and leaves followed by training a CNN model for disease classification. It mainly aims to create an automated system that can quickly and accurately identify issues, thereby reducing the dependency on manual inspection. The system has the advantages of early diagnosis, increased accuracy in disease detection, and increased crop yield through timely interventions.