Machine Learning Strategies and Security Enhancement in Agricultural Sustainability

The book offers a unique perspective on the investigation of machine learning's incorporation into agriculture. It discusses the important components of agricultural sustainability and security enhancement. It provides a deeper exploration of the optimization component using machine learning approaches. The book offers a unique fusion of cutting-edge machine-learning applications and security considerations in the context of agricultural sustainability. Examine the most recent developments in machine learning techniques designed especially to improve security and sustainable agriculture. Discusses machine learning techniques for crop health surveillance and disease early warning systems. Covers decision support systems for sustainable farming, and data-driven decision-making in agriculture. Presents cybersecurity measures for agricultural systems, implementation of security protocols for data protection, and addressing cybersecurity challenges in agriculture. Showcases how smart technologies are influencing agricultural practices, promoting sustainable agriculture, stimulating economic growth, and safeguarding essential agricultural resources. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, agricultural science, and information technology.