Includes bibliographical references.

Volume 1. Introduction to Computer Vision and Machine Learning Applications in Agriculture -- Robots and Drones in Agriculture: A Survey -- Detection of Rotten Fruits and Vegetables using Deep Learning -- Deep Learning-Based Essential Paddy Pests Filtration Technique: A Better Economic Damage Management Process -- Deep CNN-Based Mango Insect Classification -- Implementation of a Deep Convolutional Neural Network for the Detection of Tomato Leaf Diseases -- A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network -- A Deep Learning-Based Approach for Potato Diseases Classification -- An In-Depth Analysis of Different Segmentation Techniques in Automated Local Fruit Disease Recognition -- Machine Vision Based Fruit and Vegetable Disease Recognition: A Review -- An Efficient Bag-of-Features for Diseased Plant Identification.

Volume 2. Harvesting Robots for Smart Agriculture -- Drone-Based Weed Detection Architectures Using Deep Learning Algorithms and Real-Time Analytics -- A Deep Learning-Based Detection System of Multi-class Crops and Orchards Using a UAV -- Real-Life Agricultural Data Retrieval for Large-Scale Annotation Flow Optimization -- Design and Analysis of IoT-Based Modern Agriculture Monitoring System for Real-Time Data Collection -- Estimation of Wheat Yield Based on Precipitation and Evapotranspiration Using Soft Computing Methods -- Coconut Maturity Recognition Using Convolutional Neural Network -- Agri-Food Products Quality Assessment Methods -- Medicinal Plant Recognition from Leaf Images Using Deep Learning -- ESMO-based Plant Leaf Disease Identification: A Machine Learning Approach -- Deep Learning-Based Cauliflower Disease Classification -- An Intelligent System for Crop Disease Identification and Dispersion Forecasting in Sri Lanka -- Apple Leaves Diseases Detection Using Deep Convolutional Neural Networks and Transfer Learning -- A Deep Learning Paradigm for Detection and Segmentation of Plant Leaves Diseases -- Early Stage Prediction of Plant Leaf Diseases Using Deep Learning Models.

Volume 3. Computer Vision and Machine Learning in Agriculture -- Deep Learning Modeling for Gourd Species Recognition Using VGC-16 -- Sugarcane Diseases Identification and Detection via Machine Learning -- Freshness Identification of Fruits Through the Development of a Dataset -- Rice Leaf Disease Classification Using Deep Learning with Fusion Concept -- Advances in Deep Learning- Based Technologies in Rice Crop Management -- Al- Based Agriculture Recommendation System for Farmers -- A New Methodology to Detect Plant Disease Using Reprojected Multispectral Images from RGB Colour Space -- Analysis of the Performance of YOLO Models for Tomato Plant Diseases Identification -- Strawberries Maturity Level Detection Using Convolutional Neural Network (CNN) and Ensemble Method -- RGB to Multispectral Remap: A Cost-Effective Novel Approach to Recognize and Segment Plant Disease -- An Intelligent Vision- Guided Framework of the Unmanned Aerial System for Precision Agriculture -- Leveraging Computer Vision for Precision Viticulture --

This volume set discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agricultural productions. It also entails how rendering of 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 types recognition and evaluation of a wide range of agricultural plants, crops, leaves, and fruits. The book discusses intelligent robots developed with the touch of CV-ML which can help farmers to perform various tasks like planting, weeding, harvesting, plant health monitoring, and so on. The topics covered in the book include plant, leaf, and fruit disease detection, crop health monitoring, applications of robots in agriculture, precision farming, assessment of product quality and defects, pest, insect, fruits, and vegetable types recognition. The second volume 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.