Computer Vision-Based Smart Monitoring and Control System for Crop

The agricultural sector is rapidly undergoing digital transformation, leveraging advanced technologies like artificial intelligence (AI) to revolutionize its operations. Deep learning-powered computer vision, a pivotal AI component, is driving significant progress in automating precise agricultural tasks, paving the way for smart farming. By incorporating computer vision techniques and remote cameras for precise image capture, agriculture can capitalize on contactless, efficient, technology-centric solutions. This in-depth analysis focuses on cutting-edge computer vision technologies built on deep learning that can help farmers with a variety of tasks, from planting to harvesting. The chapter examines recent developments in computer vision research, breaking it down into major areas such as yield prediction, weed control, soil analysis, optimal irrigation management, plant health evaluation, and seed quality assessment. Along with other well-known deep learning architectures, the chapter also explores new developments in computer vision, including Generative Adversarial Networks (GANs) and vision transformers (ViTs). The difficulties involved in putting these solutions into practice in actual farming situations are emphasized throughout the discussion. The central takeaway from this analysis is the fundamental role played by convolutional neural networks (CNNs) in contemporary computer vision methodologies, offering precise and accurate solutions across diverse agricultural tasks. However, achieving success with computer vision mandates the construction of robust models upon high-quality datasets, coupled with ensuring the practical viability of these solutions in real agricultural settings.