Optimistic Crop Management Using Deep Learning and Machine Learning for Sustainable Agriculture

Authors- Golla Jensy Sandilya, Alluri Harshitha, Dama Greeshman, Adari Phanendra, Assistant Professor Kaki Leela Prasad

Abstract-Agriculture is the backbone of the Indian economy, providing a living for more than half of the country’s people. India is an agrarian country whose economy is mostly focused on crop productivity. Sustainable agriculture faces numerous challenges, including pest infestations, unpredictable crop yields, and plant diseases, which can significantly impact food security and economic stability. Pest infestations can devastate crops, leading to significant yield losses and necessitating the use of harmful pesticides, which can have adverse environmental effects. Unpredictable crop yields make it difficult for farmers to plan and allocate resources effectively. Additionally, plant diseases, often detected too late, can spread rapidly and cause widespread damage. Traditional methods of crop management are typically reactive, labor-intensive, and inefficient, exacerbating these issues. These problems collectively undermine agricultural sustainability, highlighting the urgent need for innovative solutions to improve crop management practices. This research aims to address these issues by leveraging deep learning and machine learning technologies to create an advanced, optimistic crop management system. The proposed solution involves the integration of various machine learning models to monitor and predict crop health, yield, and potential threats. Key areas of investigation include predictive crop yield modeling and disease detection. The project will develop sophisticated algorithms that analyze a combination of historical and real-time data. These algorithms aim to predict crop yields with high accuracy, enabling farmers to make informed decisions regarding resource allocation and planning. Implementing deep learning techniques, the system will detect early signs of plant diseases through analysis of leaf images. Early disease detection allows for prompt treatment, preventing the spread of infections and minimizing crop losses.

DOI: /10.61463/ijset.vol.13.issue1.171