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dc.contributor.editorDimitrov, Dimitre
dc.date.accessioned2023-04-05T12:51:32Z
dc.date.available2023-04-05T12:51:32Z
dc.date.issued2023
dc.identifierONIX_20230405_9783036566306_73
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/98794
dc.description.abstractGiven the current growth in global challenges, the need for smart agriculture practices and effective strategies is emerging as an imminent issue at a planetary scale. Agriculture 4.0 involves a large variety of mobile apps, web applications, Internet of Things (IoT) devices and platforms, drones, robots, and smart machinery for precision agriculture. The expansion of cloud technologies, artificial intelligence (AI), machine learning (ML), deep learning (DL), and big data collection are setting the stage for Agriculture 5.0. Agriculture science and natural sciences are further promoting this trend with the development of leading-edge scientific models and platforms, including stochastic, process-based, and data-driven machine learning modeling. This Special Issue covers the most recent and up-to-date progress in all aspects of internet and computer software applications in agriculture, focusing on the development of web applications and mobile apps, smart IoT devices and platforms, AI, ML and DL solutions in precision agriculture for detection, recognition, classification, monitoring, cultivation, harvesting, and marketing; development of cloud technologies for smart agriculture; computer and machine vision methods and applications for drones and smart machinery, and sensors for field operations; diagnostics and data collection; big data science; scientific process-based and stochastic modeling; and machine learning modeling for agriculture, agroecosystems and natural ecosystems. The research in this Special Issue will contribute to the promotion of modern agriculture practices in the current climate and in the future.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: generalen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PS Biology, life sciencesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processesen_US
dc.subject.othergrape varieties identification
dc.subject.otherSupport Vector Machine (SVM)
dc.subject.otherConvolutional Neural Network (CNN)
dc.subject.otherdeep feature fusion
dc.subject.otherCanonical Correlation Analysis (CCA)
dc.subject.othersmart machinery
dc.subject.otherdigital agriculture
dc.subject.otherChinese agricultural diseases and pests
dc.subject.othernamed entity recognition
dc.subject.otheradversarial training
dc.subject.othersemantic enhancement
dc.subject.othertechnology innovation
dc.subject.otherfood processing
dc.subject.othertransition pathways
dc.subject.othersustainable food systems
dc.subject.othertransformation
dc.subject.othersmart farming
dc.subject.otherIoT
dc.subject.otherWSN
dc.subject.othercontainerization
dc.subject.othermulti-agent
dc.subject.otherneural network
dc.subject.otherLSTM
dc.subject.otherleisure agricultural park
dc.subject.othertraveler group
dc.subject.otherCOVID-19 pandemic
dc.subject.otherfuzzy collaborative intelligence
dc.subject.othermachine vision
dc.subject.othermaize seeds
dc.subject.otherclassification
dc.subject.otherdeep learning
dc.subject.otherconvolutional neural network
dc.subject.otherdecision support systems
dc.subject.otheragricultural water management
dc.subject.otherwater security
dc.subject.otherdata-driven modeling
dc.subject.otherconceptual resilience model
dc.subject.otherinput uncertainty
dc.subject.otherclimate extreme
dc.subject.otherprocess-based modeling
dc.subject.othervehicle routing problem
dc.subject.otherfresh agricultural products
dc.subject.othersplit delivery
dc.subject.otherNSGA-II algorithm
dc.subject.otherfarm management information system
dc.subject.otherfarmers’ information needs assessment
dc.subject.othersoft system methodology
dc.subject.othersmallholder farmers
dc.subject.otherconceptual model
dc.subject.otherIndonesian chili farmers
dc.subject.otherresidual block
dc.subject.otherattention mechanism
dc.subject.othergrape leaf disease
dc.subject.otheraquatic products price forecast
dc.subject.otherVMD
dc.subject.otherIBES
dc.subject.otherhybrid model
dc.subject.otherprecision agriculture
dc.subject.othersensor network
dc.subject.othersemi-literate farmers
dc.subject.otherinteractive interface
dc.subject.otherUser Interface (UI)
dc.subject.otherAndroid apps
dc.subject.othermachine learning
dc.subject.otherregression algorithms
dc.subject.otherweb application
dc.subject.otherearly prediction of crop yield
dc.subject.othergrape detection
dc.subject.otherself-attention
dc.subject.otherbuffalo breeds
dc.subject.otherNeural Networks
dc.subject.otherSelf Activated CNN
dc.subject.otherDeepLabv3+
dc.subject.othersemantic segmentation
dc.subject.otherpicking point identification
dc.subject.othere-commerce interest linkage
dc.subject.otherparticipation willingness and behaviors
dc.subject.othergovernment policies
dc.subject.otherfarmers’ cognition
dc.subject.otherevolutionary game model
dc.subject.otherstructural equation model
dc.subject.otherobject detection
dc.subject.otherYOLOv7
dc.subject.otherhemp duck count
dc.subject.othersmart agriculture
dc.subject.otherLoRaWAN
dc.subject.otherwater status
dc.subject.othersupply chain
dc.subject.otherhorticulture
dc.subject.otherlogistics
dc.subject.otheroperations
dc.subject.otherplanning framework
dc.subject.otherdecision support
dc.subject.othern/a
dc.titleInternet and Computers for Agriculture
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-6631-3
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036566306
oapen.relation.isbn9783036566313
oapen.pages436
oapen.place.publicationBasel


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