Machine Vision in Plant Leaf Disease Detection for Sustainable Agriculture

Inbunden, Engelska, 2025

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This book offers a comprehensive exploration of the intersection between advanced technology and agricultural sustainability. With a focus on leveraging machine vision techniques for the early detection and management of plant diseases, this book serves as a vital resource for researchers, practitioners, and stakeholders in the agricultural sector. The book begins by providing an overview of the challenges posed by plant diseases to global food security and agricultural sustainability. It highlights the limitations of traditional disease detection methods and underscores the need for innovative approaches that can offer timely and accurate diagnosis. Through a systematic examination of machine vision principles and methodologies, the book delves into the various stages of disease detection, from image acquisition to feature extraction and classification. Key concepts such as image preprocessing, feature selection, and machine learning algorithms are discussed in detail, with emphasis on their practical implementation in real-world scenarios. Moreover, the book explores the potential of machine vision to contribute to sustainable agriculture practices.

Produktinformation

Utgivningsdatum2025-08-22
Mått210 x 279 x 15 mm
Vikt758 g
FormatInbunden
SpråkEngelska
SerieStudies in Computational Intelligence
Antal sidor167
FörlagSpringer Nature Switzerland AG
ISBN9789819645190

Tillhör följande kategorier

Agronomi och lantbruk inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och IT
Agronomi inom Naturvetenskap och teknik

M. F. Mridha (Senior Member IEEE, Professional Member ACM) is currently working as Associate Professor in the Department of Computer Science, American International University-Bangladesh (AIUB). He also worked as Associate Professor and Chairman in the Department of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT), from 2019 to 2022 and as CSE Department Faculty Member at the University of Asia Pacific and as Graduate Head from 2012 to 2019. He is Founder and Director of Advanced Machine Intelligence Research Lab (AMIR Lab). He received his Ph.D. in the domain of AI from Jahangirnagar University in the year 2017. For more than 18 years, he has been with the master’s and undergraduate students as Supervisor of their thesis work. He has authored/edited several books with Springer and published more than 280 journal and conference papers. He has served as Program Committee Member in several international conferences/workshops.Nilanjan Dey received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata, and Visiting Fellow of the University of Reading, UK. He is Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society, and Series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier, etc. He is Fellow of IETE and Senior Member of IEEE.

Towards Explainable AI in Agriculture: SHAP-Interpretable Vision Transformer for Bean Disease Classification.- Challenges and Opportunities with Deep Learning and Computer Vision in disease free Agricultural Product Manufacturing.- Improving Agronomic Disease Detection and Classification: The Superiority of Hybrid Inception-Xception Ensemble Model for Rice Leaf Disease Classification.- Banana Leaf Spot Disease Detection using Deep Learning Algorithms.- A Computer Vision based Approach for Rice Pest Detection Using Deep Learning.- Revolutionizing Agriculture: Deep Learning Models for Crop Pest and Disease Analysis.- Corn Leaf Disease Detection Using Deep Convolutional Neural Networks and Grad-CAM Explainability.- A Hybrid CNN Architecture for Efficient Detection of Maize Plant Diseases.- Deep Learning based watermelon leaf Disease Classification.- Deploying CNN-ResNet50-BiLSTM for Paddy Leaf Disease Detection.