Machine Vision in Plant Leaf Disease Detection for Sustainable Agriculture / edited by M. F. Mridha, Nilanjan Dey.

Format:

Book

Author/Creator:

Mridha, M. F.

Contributor:

Dey, Nilanjan.

Series:

Studies in Computational Intelligence, 1860-9503 ; 1202

Language:

English

Subjects (All):

Computational intelligence.

Machine learning.

Agriculture.

Computational Intelligence.

Machine Learning.

Local Subjects:

Computational Intelligence.

Machine Learning.

Agriculture.

Physical Description:

1 online resource (317 pages)

Edition:

1st ed. 2025.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2025.

Summary:

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9645-20-4

OCLC:

1534515322