The book explores how machine learning (ML), deep learning (DL), and metaheuristic optimization techniques can revolutionize plant disease detection and agricultural intelligence. Unlike traditional agronomic approaches, this book bridges advanced computational methods with real-world agricultural needs. It emphasizes both the scientific and practical dimensions—focusing on image-based disease detection, sensor data interpretation, optimization of predictive models, and real-world deployment strategies. The book looks at the subject from a technology, biological & agricultural, computational optimization, practical and social impact perspective.
This edited book is designed for researchers, academicians, and professionals working in Artificial Intelligence, Machine Learning, Deep Learning, Metaheuristics, and agricultural sciences. It will also benefit agricultural engineers, data scientists, agritech industries, policymakers, and undergraduate, postgraduate, and doctoral students interested in AI-driven plant disease detection and smart agriculture applications.