Mohammad Shorif Uddin (editor) - Computer Vision and Machine Learning in Agriculture (Algorithms for Intelligent Systems)

This book series publishes research on the analysis and development of algorithms for intelligent systems with their applications to various real world problems. It covers research related to autonomous agents, multi-agent systems, behavioral modeling, reinforcement learning, game theory, mechanism design, machine learning, meta-heuristic search, optimization, planning and scheduling, artificial neural networks, evolutionary computation, swarm intelligence and other algorithms for intelligent systems.

The book series includes recent advancements, modification and applications of the artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, fuzzy system, autonomous and multi agent systems, machine learning and other intelligent systems related areas. The material will be beneficial for the graduate students, post-graduate students as well as the researchers who want a broader view of advances in algorithms for intelligent systems. The contents will also be useful to the researchers from other fields who have no knowledge of the power of intelligent systems, e.g. the researchers in the field of bioinformatics, biochemists, mechanical and chemical engineers, economists, musicians and medical practitioners.

The series publishes monographs, edited volumes, advanced textbooks and selected proceedings.

More information about this series at http://www.springer.com/series/16171

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In recent years, computer vision, a noncontact- as well as a nondestructive-technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition finds huge applications in agricultural productions. Rendering the machine learning techniques to computer vision algorithms is boosting this sector with better productivity by developing more precise systems.

Computer vision and Machine learning (CV-ML) helps in plant disease assessment along with crop condition monitoring to control the degradation of yield, quality, and severe financial loss for farmers. Significant scientific and technological advances have been made in defect assessment, quality grading, disease recognition, pests, insects, fruits, and vegetable recognition and evaluation of a wide range of agricultural plants, crops, leaves, and fruits. Intelligent robots and drones developed with the touch of CV-ML can help farmers to perform various tasks like planting, weeding, harvesting, plant health monitoring, and so on.

This book is intended to publish the latest advances and developments in the field of computer vision, machine learning including deep learning tools and techniques in assessing and monitoring agricultural products and productions. The topics covered include plant, leaf, and fruit-disease detection, crop health monitoring, applications of robots and drones in agriculture, precision farming, assessment of product quality and defects, pest, insect, fruits, and vegetable types recognition, etc. It contains 11 chapters.

Chapter proposes a bag-of-features based diseased plant identification method using gray relational analysis. It presents the experimental results on a publicly available leaf-image dataset (PlantVillage).

This book is expected to be very useful the researchers, academicians, undergraduate and postgraduate students who wish to work and explore the applications of computer vision and machine learning systems in the agricultural sector for boosting productions.

We sincerely appreciate the time, effort, and contribution of the authors and esteemed reviewers in maintaining the quality of the papers. Special thanks to the editors and supporting team of Springer for helping in publishing this book.