Computer vision in plant phenotyping and agriculture
Author: Valerio Giuffrida
Publisher: Frontiers Media SA
Published: 2023-06-06
Total Pages: 265
ISBN-13: 2832510655
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Advances Plant Phenotyping More Sustaihb
Author: Achim Walter
Publisher: Burleigh Dodds Series in Agricultural Science
Published: 2022-03-22
Total Pages: 420
ISBN-13: 9781786768568
DOWNLOAD EBOOKPlant 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.
Intelligent Image Analysis for Plant Phenotyping
Author: Ashok Samal
Publisher: CRC Press
Published: 2020-10-21
Total Pages: 347
ISBN-13: 1351709992
DOWNLOAD EBOOKDomesticated 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.
High-Throughput Crop Phenotyping
Author: Jianfeng Zhou
Publisher: Springer Nature
Published: 2021-07-17
Total Pages: 249
ISBN-13: 3030737349
DOWNLOAD EBOOKThis book provides an overview of the innovations in crop phenotyping using emerging technologies, i.e., high-throughput crop phenotyping technology, including its concept, importance, breakthrough and applications in different crops and environments. Emerging technologies in sensing, machine vision and high-performance computing are changing the world beyond our imagination. They are also becoming the most powerful driver of the innovation in agriculture technology, including crop breeding, genetics and management. It includes the state of the art of technologies in high-throughput phenotyping, including advanced sensors, automation systems, ground-based or aerial robotic systems. It also discusses the emerging technologies of big data processing and analytics, such as advanced machine learning and deep learning technologies based on high-performance computing infrastructure. The applications cover different organ levels (root, shoot and seed) of different crops (grains, soybean, maize, potato) at different growth environments (open field and controlled environments). With the contribution of more than 20 world-leading researchers in high-throughput crop phenotyping, the authors hope this book provides readers the needed information to understand the concept, gain the insides and create the innovation of high-throughput phenotyping technology.
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
DOWNLOAD EBOOKSince 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.
Plant Image Analysis
Author: S Dutta Gupta
Publisher: CRC Press
Published: 2014-09-17
Total Pages: 410
ISBN-13: 1466583029
DOWNLOAD EBOOKThe 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
Computer Vision and Machine Learning in Agriculture
Author: Mohammad Shorif Uddin
Publisher: Springer Nature
Published: 2021-03-23
Total Pages: 172
ISBN-13: 9813364246
DOWNLOAD EBOOKThis 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.
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
DOWNLOAD EBOOKPlant 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.
Agricultural Informatics
Author: Amitava Choudhury
Publisher: John Wiley & Sons
Published: 2021-03-02
Total Pages: 304
ISBN-13: 1119769213
DOWNLOAD EBOOKDespite the increasing population (the Food and Agriculture Organization of the United Nations estimates 70% more food will be needed in 2050 than was produced in 2006), issues related to food production have yet to be completely addressed. In recent years, Internet of Things technology has begun to be used to address different industrial and technical challenges to meet this growing need. These Agro-IoT tools boost productivity and minimize the pitfalls of traditional farming, which is the backbone of the world's economy. Aided by the IoT, continuous monitoring of fields provides useful and critical information to farmers, ushering in a new era in farming. The IoT can be used as a tool to combat climate change through greenhouse automation; monitor and manage water, soil and crops; increase productivity; control insecticides/pesticides; detect plant diseases; increase the rate of crop sales; cattle monitoring etc. Agricultural Informatics: Automation Using the IoT and Machine Learning focuses on all these topics, including a few case studies, and they give a clear indication as to why these techniques should now be widely adopted by the agriculture and farming industries.
Computer Vision and Machine Learning in Agriculture, Volume 2
Author: Mohammad Shorif Uddin
Publisher: Springer Nature
Published: 2022-03-13
Total Pages: 269
ISBN-13: 9811699917
DOWNLOAD EBOOKThis 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.
