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

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.

Intelligent Image Analysis for Plant Phenotyping
Intelligent Image Analysis for Plant Phenotyping

Author: Ashok Samal

Publisher: CRC Press

Published: 2020-10-21

Total Pages: 271

ISBN-13: 1351709984

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Domesticated crops are the result of artificial selection for particular phenotypes or, in some cases, natural selection for an adaptive trait. Plant traits can be identified through image-based plant phenotyping, a process that was, until recently, strenous and time-consuming. Intelligent Image Analysis for Plant Phenotyping reviews information on time-saving techniques, using computer vision and imaging technologies. These methodologies provide an automated, non-invasive, and scalable mechanism by which to define and collect plant phenotypes. Beautifully illustrated, with numerous color images, the book focuses on phenotypes measured from individual plants under controlled experimental conditions, which are widely available in high-throughput systems. Features: Presents methodologies for image processing, including data-driven and machine learning techniques for plant phenotyping. Features information on advanced techniques for extracting phenotypes through images and image sequences captured in a variety of modalities. Includes real-world scientific problems, including predicting yield by modeling interactions between plant data and environmental information. Discusses the challenge of translating images into biologically informative quantitative phenotypes. A practical resource for students, researchers, and practitioners, this book is invaluable for those working in the emerging fields at the intersection of computer vision and plant sciences.

Computer Vision and Machine Learning in Agriculture
Computer Vision and Machine Learning in Agriculture

Author: Mohammad Shorif Uddin

Publisher: Springer Nature

Published: 2021-03-23

Total Pages: 172

ISBN-13: 9813364246

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This book 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.

Sustainable Farming through Machine Learning
Sustainable Farming through Machine Learning

Author: Suneeta Satpathy

Publisher: CRC Press

Published: 2024-11-25

Total Pages: 301

ISBN-13: 1040254780

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This book explores the transformative potential of machine learning (ML) technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how artificial intelligence/machine learning (AI/ML) can optimize resource management and improve overall productivity in farming practices. Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. The authors also cover applications in livestock management, including feed formulation and disease detection; they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore the ethical and social implications of using such technologies. This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications.

Modern Techniques for Agricultural Disease Management and Crop Yield Prediction
Modern Techniques for Agricultural Disease Management and Crop Yield Prediction

Author: Pradeep, N.

Publisher: IGI Global

Published: 2019-08-16

Total Pages: 310

ISBN-13: 1522596348

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Since agriculture is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional agricultural practices to smart agriculture. New agricultural technologies provide numerous opportunities to maximize crop yield by recognizing and analyzing diseases and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart agriculture. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction is an essential publication that widens the spectrum of computational methods that can aid in agriculture disease management, weed detection, and crop yield prediction. Featuring coverage on a wide range of topics such as soil and crop sensors, swarm robotics, and weed detection, this book is ideally designed for environmentalists, farmers, botanists, agricultural engineers, computer engineers, scientists, researchers, practitioners, and students seeking current research on technology and techniques for agricultural diseases and predictive trends.

Information Modelling and Knowledge Bases XXXI
Information Modelling and Knowledge Bases XXXI

Author: A. Dahanayake

Publisher: IOS Press

Published: 2020-01-06

Total Pages: 562

ISBN-13: 1643680455

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Information modeling and knowledge bases have become an important area of academic and industry research in the 21st century, addressing complexities of modeling that reach beyond the traditional borders of information systems and academic computer science research. This book presents 32 reviewed, selected and updated papers delivered at the 29th International Conference on Information Modeling and Knowledge Bases (EJC2019), held in Lappeenranta, Finland, from 3 to 7 June 2019. In addition, two papers based on the keynote presentations and one paper edited from the discussion of the panel session are included in the book. The conference provided a forum to exchange scientific results and experience, and attracted academics and practitioners working with information and knowledge. The papers cover a wide range of topics, ranging from knowledge discovery through conceptual and linguistic modeling, knowledge and information modeling and discovery, cross-cultural communication and social computing, environmental modeling and engineering, and multimedia data modeling and systems to complex scientific problem-solving. The conference presentation sessions: Learning and Linguistics; Systems and Processes; Data and Knowledge Representation; Models and Interface; Formalizations and Reasoning; Models and Modeling; Machine Learning; Models and Programming; Environment and Predictions; and Emotion Modeling and Social Networks reflect the main themes of the conference. The book also includes 2 extended publications of keynote addresses: ‘Philosophical Foundations of Conceptual Modeling’ and ́Sustainable Solid Waste Management using Life Cycle Modeling for Environmental Impact Assessment’, as well as additional material covering the discussion and findings of the panel session. Providing an overview of current research in the field, the book will be of interest to all those working with information systems, information modeling and knowledge bases.