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001435250 001__ 1435250
001435250 003__ OCoLC
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001435250 019__ $$a1243306040
001435250 020__ $$a9813364246$$q(v.1, electronic book)
001435250 020__ $$a9789813364240$$q(v.1, electronic bk.)
001435250 020__ $$a9789811699917$$q(v.2, electronic bk.)
001435250 020__ $$a9811699917$$q(v.2, electronic bk.)
001435250 020__ $$z9813364238
001435250 020__ $$z9789813364233
001435250 0247_ $$a10.1007/978-981-33-6424-0$$2doi
001435250 035__ $$aSP(OCoLC)1243553322
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001435250 049__ $$aISEA
001435250 050_4 $$aS494.5.D3$$bC66eb
001435250 08204 $$a630.2085$$223
001435250 24500 $$aComputer vision and machine learning in agriculture /$$cMohammad Shorif Uddin, Jagdish Chand Bansal, editors.
001435250 264_1 $$aSingapore :$$bSpringer,$$c[2021-]
001435250 300__ $$a1 online resource (2 volumes)
001435250 336__ $$atext$$btxt$$2rdacontent
001435250 337__ $$acomputer$$bc$$2rdamedia
001435250 338__ $$aonline resource$$bcr$$2rdacarrier
001435250 4901_ $$aAlgorithms for intelligent systems
001435250 504__ $$aIncludes bibliographical references.
001435250 50500 $$gVolume 1.$$tIntroduction to Computer Vision and Machine Learning Applications in Agriculture --$$tRobots and Drones in Agriculture: A Survey --$$tDetection of Rotten Fruits and Vegetables using Deep Learning --$$tDeep Learning-Based Essential Paddy Pests Filtration Technique: A Better Economic Damage Management Process --$$tDeep CNN-Based Mango Insect Classification --$$tImplementation of a Deep Convolutional Neural Network for the Detection of Tomato Leaf Diseases --$$tA Multi-Plant Disease Diagnosis Method using Convolutional Neural Network --$$tA Deep Learning-Based Approach for Potato Diseases Classification --$$tAn In-Depth Analysis of Different Segmentation Techniques in Automated Local Fruit Disease Recognition --$$tMachine Vision Based Fruit and Vegetable Disease Recognition: A Review --$$tAn Efficient Bag-of-Features for Diseased Plant Identification.
001435250 50500 $$gVolume 2.$$tHarvesting Robots for Smart Agriculture --$$tDrone-Based Weed Detection Architectures Using Deep Learning Algorithms and Real-Time Analytics --$$tA Deep Learning-Based Detection System of Multi-class Crops and Orchards Using a UAV --$$tReal-Life Agricultural Data Retrieval for Large-Scale Annotation Flow Optimization --$$tDesign and Analysis of IoT-Based Modern Agriculture Monitoring System for Real-Time Data Collection --$$tEstimation of Wheat Yield Based on Precipitation and Evapotranspiration Using Soft Computing Methods --$$tCoconut Maturity Recognition Using Convolutional Neural Network -- Agri-Food Products Quality Assessment Methods --$$tMedicinal Plant Recognition from Leaf Images Using Deep Learning --$$tESMO-based Plant Leaf Disease Identification: A Machine Learning Approach --$$tDeep Learning-Based Cauliflower Disease Classification --$$tAn Intelligent System for Crop Disease Identification and Dispersion Forecasting in Sri Lanka --$$tApple Leaves Diseases Detection Using Deep Convolutional Neural Networks and Transfer Learning --$$tA Deep Learning Paradigm for Detection and Segmentation of Plant Leaves Diseases --$$tEarly Stage Prediction of Plant Leaf Diseases Using Deep Learning Models.
001435250 506__ $$aAccess limited to authorized users.
001435250 520__ $$aThis volume set 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. The second volume 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.
001435250 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 6, 2021).
001435250 650_0 $$aArtificial intelligence$$xAgricultural applications.
001435250 650_0 $$aComputer vision.
001435250 650_0 $$aMachine learning.
001435250 650_6 $$aIntelligence artificielle$$xApplications agricoles.
001435250 650_6 $$aVision par ordinateur.
001435250 650_6 $$aApprentissage automatique.
001435250 655_0 $$aElectronic books.
001435250 7001_ $$aUddin, Mohammad Shorif,$$eeditor.
001435250 7001_ $$aBansal, Jagdish Chand,$$eeditor.
001435250 77608 $$iPrint version:$$z9789813364233
001435250 830_0 $$aAlgorithms for intelligent systems.
001435250 852__ $$bebk
001435250 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-6424-0$$zOnline Access$$91397441.1
001435250 909CO $$ooai:library.usi.edu:1435250$$pGLOBAL_SET
001435250 980__ $$aBIB
001435250 980__ $$aEBOOK
001435250 982__ $$aEbook
001435250 983__ $$aOnline
001435250 994__ $$a92$$bISE