東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Computer vision and machine learning...

Uddin, Mohammad Shorif.

Linked to FindBook

Google Book

Amazon

博客來

Computer vision and machine learning in agriculture.. Volume 2

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computer vision and machine learning in agriculture./ edited by Mohammad Shorif Uddin, Jagdish Chand Bansal.
other author:	Uddin, Mohammad Shorif.
Published:	Singapore :Springer Singapore : : 2022.,
Description:	xiii, 260 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Agricultural applications. -
Online resource:	https://doi.org/10.1007/978-981-16-9991-7
ISBN:	9789811699917

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

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

3299845
Artificial intelligence
--Agricultural applications.

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

Dewey Class. No.: 630.2085

Computer vision and machine learning in agriculture.. Volume 2
LDR:03298nmm a2200337 a 4500 001 2298995
003 DE-He213
005 20220313171101.0
006 m d
007 cr nn 008maaau
008 230324s2022 si s 0 eng d
020 $a 9789811699917 $q (electronic bk.)
020 $a 9789811699900 $q (paper)
024 7 $a 10.1007/978-981-16-9991-7 $2 doi
035 $a 978-981-16-9991-7
040 $a GP $c GP
041 0 $a eng
050 4 $a S494.5.D3 $b C65 2022
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 630.2085 $2 23
090 $a S494.5.D3 $b C738 2022
245 0 0 $a Computer vision and machine learning in agriculture. $n Volume 2 $h [electronic resource] / $c edited by Mohammad Shorif Uddin, Jagdish Chand Bansal.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xiii, 260 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Algorithms for intelligent systems, $x 2524-7573
505 0 $a 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.
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.
650 0 $a Artificial intelligence $x Agricultural applications. $3 3299845
650 0 $a Computer vision. $3 540671
650 0 $a Machine learning. $3 533906
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Robotics. $3 519753
650 2 4 $a Agriculture. $3 518588
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
700 1 $a Uddin, Mohammad Shorif. $3 3443392
700 1 $a Bansal, Jagdish Chand. $3 3378378
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Algorithms for intelligent systems. $3 3443393
856 4 0 $u https://doi.org/10.1007/978-981-16-9991-7
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)