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Computer vision and machine learning in agriculture.Volume 2

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computer vision and machine learning in agriculture.edited by Mohammad Shorif Uddin, Jagdish Chand Bansal.
其他作者:	Uddin, Mohammad Shorif.
出版者:	Singapore :Springer Singapore :2022.
面頁冊數:	xiii, 260 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Artificial intelligenceAgricultural applications.
電子資源:	https://doi.org/10.1007/978-981-16-9991-7
ISBN:	9789811699917$q(electronic bk.)

Computer vision and machine learning in agriculture.Volume 2
Computer vision and machine learning in agriculture.Volume 2[electronic resource] /edited by Mohammad Shorif Uddin, Jagdish Chand Bansal. - Singapore :Springer Singapore :2022. - xiii, 260 p. :ill. (some col.), digital ;24 cm. - Algorithms for intelligent systems,2524-7573. - Algorithms for intelligent systems..

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 cuali flower disease classification -- An Intelligent System for Crop Disease Identification and Dispersion Forecasting in SriLanka -- 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.

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.

ISBN: 9789811699917$q(electronic bk.)

Standard No.: 10.1007/978-981-16-9991-7doiSubjects--Topical Terms:

803531
Artificial intelligence
--Agricultural applications.

LC Class. No.: S494.5.D3 / C65 2022

Dewey Class. No.: 630.2085

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520 $a 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.
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