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This book features high-quality research papers presented at the International Conference on Computing and Machine Learning (CML 2024), organized by Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India during April 29–30, 2024. The book presents diverse range of topics, including machine learning algorithms and models, deep learning and neural networks, computer vision and image processing, natural language processing, robotics and automation, reinforcement learning, big data analytics, cloud computing, internet of things, human-robot interaction, ethical and social implications of AI, applications in healthcare, finance, and industry, computer modeling, quantum computing, high-performance computing, cognitive and parallel computing, cloud computing, distributed computing, embedded computing, human-centered computing and mobile computing.

This book constitutes the refereed proceedings of the 8th International Conference on Information, Communication and Computing Technology, ICICCT 2023, held in New Delhi, India, during May 27, 2023.The 14 full papers included in this book were carefully reviewed and selected from 60 submissions. They were organized in topical sections as follows: global platform for researchers, scientists and practitioners from both academia and industry to present their research and development activities in all the aspects of Pattern Recognition and computational Intelligence techniques.

This book is as an extension of previous book “Computer Vision and Machine Learning in Agriculture” for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book contains 15 chapters. The first three chapters are devoted to crops harvesting, weed, and multi-class crops detection with the help of robots and UAVs through machine learning and deep learning algorithms for smart agriculture. Next, two chapters describe agricultural data retrievals and data collections. Chapters 6, 7, 8 and 9 focuses on yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies.