Intelligent Image Analysis for Plant Phenotyping

Intelligent Image Analysis for Plant Phenotyping PDF

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.

Intelligent Image Analysis for Plant Phenotyping

Intelligent Image Analysis for Plant Phenotyping PDF

Author: Ashok Samal

Publisher: CRC Press

Published: 2020-10-21

Total Pages: 347

ISBN-13: 1351709992

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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.

Plant Image Analysis

Plant Image Analysis PDF

Author: S Dutta Gupta

Publisher: CRC Press

Published: 2014-09-17

Total Pages: 398

ISBN-13: 1466583029

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The application of imaging techniques in plant and agricultural sciences had previously been confined to images obtained through remote sensing techniques. Technological advancements now allow image analysis for the nondestructive and objective evaluation of biological objects. This has opened a new window in the field of plant science. Plant Image

Advances Plant Phenotyping More Sustaihb

Advances Plant Phenotyping More Sustaihb PDF

Author: Achim Walter

Publisher: Burleigh Dodds Series in Agricultural Science

Published: 2022-03-22

Total Pages: 420

ISBN-13: 9781786768568

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Plant phenotyping is an emerging technology that involves the quantitative analysis of structural and functional plant traits. However, it is widely recognised that phenotyping needs to match similar advances in genetics if it is to not create a bottleneck in plant breeding. Advances in plant phenotyping for more sustainable crop production reviews the wealth of research on advances in plant phenotyping to meet this challenge, such as the development of new technologies including hyperspectral sensors such as LIDAR, NIR/SWIR, as well as alternative delivery/carrier systems, such as ground-based proximal distance systems and UAVs. The book details the development of plant phenotyping as a technique to analyse crop roots and functionality, as well as its use in understanding and improving crop response to biotic and abiotic stresses.

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.

High-Throughput Plant Phenotyping

High-Throughput Plant Phenotyping PDF

Author: Argelia Lorence

Publisher: Springer Nature

Published: 2022-07-27

Total Pages: 300

ISBN-13: 1071625373

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This volume looks at a collection of the latest techniques used to quantify the genome-by-environment-by-management (GxExM) interactions in a variety of model and plant crops. The chapters in this book are organized into five parts. Part One discusses high-throughput plant phenotyping (HTPP) protocols for plants growing under controlled conditions. Part Two present novel algorithms for extracting data from seed images, color analysis from fruits, and other digital readouts from 2D objects. Part Three covers molecular imaging protocols using PET and X-ray approaches, and Part Four presents a collection of HTPP techniques for crops growing under field conditions. The last part focuses on molecular analysis, metabolomics, network analysis, and statistical methods for the quantitative genetic analysis of HTP data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and practical, High-Throughput Plant Phenotyping: Review and Protocols is a valuable resource for both novice and expert researchers looking to learn more about this important field. Chapter 21 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Sensing, Data Managing, and Control Technologies for Agricultural Systems

Sensing, Data Managing, and Control Technologies for Agricultural Systems PDF

Author: Shaochun Ma

Publisher: Springer Nature

Published: 2022-06-06

Total Pages: 336

ISBN-13: 3031038347

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Agricultural automation is the emerging technologies which heavily rely on computer-integrated management and advanced control systems. The tedious farming tasks had been taken over by agricultural machines in last century, in new millennium, computer-aided systems, automation, and robotics has been applied to precisely manage agricultural production system. With agricultural automation technologies, sustainable agriculture is being developed based on efficient use of land, increased conservation of water, fertilizer and energy resources. The agricultural automation technologies refer to related areas in sensing & perception, reasoning & learning, data communication, and task planning & execution. Since the literature on this diverse subject is widely scattered, it is necessary to review current status and capture the future challenges through a comprehensive monograph. In this book we focus on agricultural automation and provide critical reviews of advanced control technologies, their merits and limitations, application areas and research opportunities for further development. This collection thus serves as an authoritative treatise that can help researchers, engineers, educators, and students in the field of sensing, control, and automation technologies for production agriculture.

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