Vision Based Deep Learning Frameworks for Precision Agriculture and Crop Health Monitoring
This chapter explores the application of vision-based deep learning frameworks for precision agriculture and crop health monitoring. It addresses the critical need for early and accurate detection of crop diseases and pests to enhance agricultural productivity and sustainability. A novel deep learning framework, "AgroVision-Net," is proposed, which leverages a combination of Convolutional Neural Networks (CNNs) and transfer learning for robust crop disease classification. The framework is trained and evaluated on a comprehensive dataset of plant leaf images, encompassing various crop types and disease conditions. The experimental results demonstrate the superior performance of AgroVision-Net, achieving a high accuracy in disease identification. The chapter also discusses the integration of this framework with unmanned aerial vehicles (UAVs) for large-scale crop monitoring. The findings highlight the transformative potential of deep learning in modernizing agricultural practices and ensuring global food security.
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