Artificial Intelligence Based Crop Health Analysis

Abstract

Title: Artificial Intelligence Based Crop Health Analysis Abstract: Crop health analysis is a important component in ensuring maximum agricultural production and sustainability in agriculture. It is essential for global food security. By analyzing various problems solve by the Artificial Intelligence, it is concluded that crop disease their is more loss in crop production and as sugarcane has large production in India.So, This study includes sugarcane disease classification and for that their is in previous study large includes classification of sugarcane disease using machine learning. So, This study's main goal is to create and evaluate a deep learning model that could use image-based analysis to reliably categorize different sugarcane diseases. In terms of methodology, there are different models available for this study convolutional neural network model was built and trained on the dataset of five classes as Healthy, Mosaic, RedRot, Rust, and Yellow. The architecture design in such a manner that improves performance. The findings showed encouraging results, demonstrating the model's capacity to accurately identify accuracy for the model's effectiveness in identifying diseases. This work highlights how important deep learning is to transforming crop health management, especially in sugarcane farming, by providing a dependable and effective instrument for early disease identification. With early interventions to reduce crop losses and enhance agricultural sustainability, the findings have enormous promise for practical use in precision agriculture.