Deep Learning for Agricultural Visual Perception

Deep Learning for Agricultural Visual Perception PDF

Author: Rujing Wang

Publisher: Springer Nature

Published: 2023-09-20

Total Pages: 140

ISBN-13: 9819949734

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This monograph provides a detailed and systematic introduction to the application of deep learning technology in the intelligent monitoring of crop diseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with complex backgrounds as examples, a large-scale crop pest and disease dataset was constructed to provide necessary data support for the deep learning module. Various schemes for identifying and detecting large-scale crop diseases and pests based on deep convolutional neural network technology have also been proposed. This book can be used as a reference for teachers and students majoring in agriculture, computer science, artificial intelligence, intelligent science and technology, and other related fields in higher education institutions. It can also be used as a reference book for researchers in fields such as image processing technology, intelligent manufacturing, and high-tech applications.

Computer Vision and Machine Learning in Agriculture, Volume 2

Computer Vision and Machine Learning in Agriculture, Volume 2 PDF

Author: Mohammad Shorif Uddin

Publisher: Springer Nature

Published: 2022-03-13

Total Pages: 269

ISBN-13: 9811699917

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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.

Machine Learning and Deep Learning for Smart Agriculture and Applications

Machine Learning and Deep Learning for Smart Agriculture and Applications PDF

Author: Hashmi, Mohamamd Farukh

Publisher: IGI Global

Published: 2023-08-29

Total Pages: 276

ISBN-13: 1668499762

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Machine Learning and Deep Learning for Smart Agriculture and Applications delves into the captivating realm of artificial intelligence and its pivotal role in transforming the landscape of modern agriculture. With a focus on precision agriculture, digital farming, and emerging concepts, this book illuminates the significance of sustainable food production and resource management in the face of evolving digital hardware and software technologies. Geospatial technology, robotics, the Internet of Things (IoT), and data analytics converge with machine learning and big data to unlock new possibilities in agricultural management. This book explores the synergy between these disciplines, offering cutting-edge insights into data-intensive processes within operational agricultural environments. From automated irrigation systems and agricultural drones for field analysis to crop monitoring and precision agriculture, the applications of machine learning are far-reaching. Animal identification and health monitoring also benefit from these advanced techniques. With practical case studies on vegetable and fruit leaf disease detection, drone-based agriculture, and the impact of pesticides on plants, this book provides a comprehensive understanding of the applications of machine learning and deep learning in smart agriculture. It also examines various modeling techniques employed in this field and showcases how artificial intelligence can revolutionize plant disease detection. This book serves as a comprehensive guide for researchers, practitioners, and students seeking to harness the power of AI in transforming the agricultural landscape.

Deep Learning for Sustainable Agriculture

Deep Learning for Sustainable Agriculture PDF

Author: Ramesh Chandra Poonia

Publisher: Academic Press

Published: 2022-01-09

Total Pages: 408

ISBN-13: 0323903622

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The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. Introduces new deep learning models developed to address sustainable solutions for issues related to agriculture Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture Illustrates through case studies how deep learning has been used to address a variety of agricultural diseases that are currently on the cutting edge Delivers an accessible explanation of artificial intelligence algorithms, making it easier for the reader to implement or use them in their own agricultural domain

Machine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture

Machine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture PDF

Author: Huajian Liu

Publisher: Frontiers Media SA

Published: 2024-01-18

Total Pages: 423

ISBN-13: 283254293X

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Plant phenotyping (PP) describes the physiological and biochemical properties of plants affected by both genotypes and environments. It is an emerging research field that is assisting the breeding and cultivation of new crop varieties to be more productive and resilient to challenging environments. Precision agriculture (PA) uses sensing technologies to observe crops and then manage them optimally to ensure that they grow in healthy conditions, have maximum productivity, and have minimal negative effects on the environment. Traditionally, the observation of plant traits heavily relies on human experts which is labor intensive, time-consuming, and subjective. Automatic crop traits measurement in PP and PA are two different fields, but they share the same sensing and data processing technologies in many respects. Recently, driven by computer and sensor technologies, machine vision (MV) and machine learning (ML) have contributed to accurate, high-throughput, and nondestructive plant phenotyping and precision agriculture. However, these technologies are still in their infant stage and there are many challenges and questions related to them that still need to be addressed. The goal of this Research Topic is to provide a platform to share the latest research results on the application of MV and ML for PP and PA. It aims to highlight cutting-edge technologies, bottle-necks, and future research directions for MV and ML in crop breeding, crop cultivation, disease management, weed control, and pest control.

Application of Machine Learning in Agriculture

Application of Machine Learning in Agriculture PDF

Author: Mohammad Ayoub Khan

Publisher: Academic Press

Published: 2022-05-14

Total Pages: 332

ISBN-13: 0323906680

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Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning. As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development. This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics. Addresses the technology of smart agriculture from a technical perspective Reveals opportunities for technology to improve and enhance not only yield and quality, but the economic value of a food crop Discusses physical instruments, simulations, sensors, and markets for machine learning in agriculture

Computer Vision and Machine Learning in Agriculture, Volume 3

Computer Vision and Machine Learning in Agriculture, Volume 3 PDF

Author: Jagdish Chand Bansal

Publisher:

Published: 2023

Total Pages: 0

ISBN-13: 9789819937561

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This book is as an extension of the previous two volumes on "Computer Vision and Machine Learning in Agriculture". This volume 3 discusses solutions to the problems of agricultural production by rendering advanced machine learning including deep learning tools and techniques. The book contains 13 chapters that focus on in-depth research outputs in precision agriculture, crop farming, horticulture, floriculture, vertical farming, animal husbandry, disease detection, plant recognition, production yield, product quality, defect assessment, and overall automation through robots and drones. The topics covered in the current volume, along with the previous volumes, are comprehensive literature for both beginners and experienced in this domain.

Applications of Image Processing and Soft Computing Systems in Agriculture

Applications of Image Processing and Soft Computing Systems in Agriculture PDF

Author: Razmjooy, Navid

Publisher: IGI Global

Published: 2019-02-22

Total Pages: 337

ISBN-13: 152258028X

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The variety and abundance of qualitative characteristics of agricultural products have been the main reasons for the development of different types of non-destructive methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. Applications of Image Processing and Soft Computing Systems in Agriculture examines applications of artificial intelligence in agriculture and the main uses of shape analysis on agricultural products such as relationships between form and genetics, adaptation, product characteristics, and product sorting. Additionally, it provides insights developed through computer vision techniques. Highlighting such topics as deep learning, agribusiness, and augmented reality, it is designed for academicians, researchers, agricultural practitioners, and industry professionals.

Smart Agriculture

Smart Agriculture PDF

Author: Govind Singh Patel

Publisher: CRC Press

Published: 2021-02-11

Total Pages: 268

ISBN-13: 1000327892

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This book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.

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