Harnessing Environmental Intelligence to Enhance Crop Management by Leveraging Deep Learning Technique

Abstract

This chapter aims to enhance crop management practices by harnessing environmental intelligence through the power of deep learning techniques. Efficient and sustainable crop management is crucial for meeting the increasing demand for agricultural products while minimizing environmental impact. In recent years, the integration of deep learning techniques with environmental data has shown great potential in improving crop management practices. The proposed approach involves training deep learning model Dense Net 121 to predict important crop management factors, including yield estimation, disease, and pest outbreaks. The models are trained using historical and real-time data, enabling them to adapt and respond to dynamic environmental conditions. By capturing complex patterns and interactions, deep learning models can provide valuable insights and recommendations to farmers, enabling them to optimize resource allocation, reduce input wastage, and improve overall crop productivity.