Internet and Computers for Agriculture
Given 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, r...
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| Formaat: | Online |
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| Taal: | Engels |
| Gepubliceerd in: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Onderwerpen: | |
| Online toegang: | ONIX_20230405_9783036566306_73 |
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| _version_ | 1869520695790141440 |
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| collection | Directory of Open Access Books |
| description | Given 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. |
| format | Online |
| id | doab-20.500.12854ir-98794 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-987942024-03-28T03:31:22Z Internet and Computers for Agriculture Dimitrov, Dimitre grape varieties identification Support Vector Machine (SVM) Convolutional Neural Network (CNN) deep feature fusion Canonical Correlation Analysis (CCA) smart machinery digital agriculture Chinese agricultural diseases and pests named entity recognition adversarial training semantic enhancement technology innovation food processing transition pathways sustainable food systems transformation smart farming IoT WSN containerization multi-agent neural network LSTM leisure agricultural park traveler group COVID-19 pandemic fuzzy collaborative intelligence machine vision maize seeds classification deep learning convolutional neural network decision support systems agricultural water management water security data-driven modeling conceptual resilience model input uncertainty climate extreme process-based modeling vehicle routing problem fresh agricultural products split delivery NSGA-II algorithm farm management information system farmers’ information needs assessment soft system methodology smallholder farmers conceptual model Indonesian chili farmers residual block attention mechanism grape leaf disease aquatic products price forecast VMD IBES hybrid model precision agriculture sensor network semi-literate farmers interactive interface User Interface (UI) Android apps machine learning regression algorithms web application early prediction of crop yield grape detection self-attention buffalo breeds Neural Networks Self Activated CNN DeepLabv3+ semantic segmentation picking point identification e-commerce interest linkage participation willingness and behaviors government policies farmers’ cognition evolutionary game model structural equation model object detection YOLOv7 hemp duck count smart agriculture LoRaWAN water status supply chain horticulture logistics operations planning framework decision support n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes Given 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. 2023-04-05T12:51:32Z 2023-04-05T12:51:32Z 2023 book ONIX_20230405_9783036566306_73 9783036566306 9783036566313 https://directory.doabooks.org/handle/20.500.12854/98794 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/6830 https://mdpi.com/books/pdfview/book/6830 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-6631-3 10.3390/books978-3-0365-6631-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036566306 9783036566313 436 Basel open access |
| spellingShingle | grape varieties identification Support Vector Machine (SVM) Convolutional Neural Network (CNN) deep feature fusion Canonical Correlation Analysis (CCA) smart machinery digital agriculture Chinese agricultural diseases and pests named entity recognition adversarial training semantic enhancement technology innovation food processing transition pathways sustainable food systems transformation smart farming IoT WSN containerization multi-agent neural network LSTM leisure agricultural park traveler group COVID-19 pandemic fuzzy collaborative intelligence machine vision maize seeds classification deep learning convolutional neural network decision support systems agricultural water management water security data-driven modeling conceptual resilience model input uncertainty climate extreme process-based modeling vehicle routing problem fresh agricultural products split delivery NSGA-II algorithm farm management information system farmers’ information needs assessment soft system methodology smallholder farmers conceptual model Indonesian chili farmers residual block attention mechanism grape leaf disease aquatic products price forecast VMD IBES hybrid model precision agriculture sensor network semi-literate farmers interactive interface User Interface (UI) Android apps machine learning regression algorithms web application early prediction of crop yield grape detection self-attention buffalo breeds Neural Networks Self Activated CNN DeepLabv3+ semantic segmentation picking point identification e-commerce interest linkage participation willingness and behaviors government policies farmers’ cognition evolutionary game model structural equation model object detection YOLOv7 hemp duck count smart agriculture LoRaWAN water status supply chain horticulture logistics operations planning framework decision support n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes Internet and Computers for Agriculture |
| title | Internet and Computers for Agriculture |
| title_full | Internet and Computers for Agriculture |
| title_fullStr | Internet and Computers for Agriculture |
| title_full_unstemmed | Internet and Computers for Agriculture |
| title_short | Internet and Computers for Agriculture |
| title_sort | internet and computers for agriculture |
| topic | grape varieties identification Support Vector Machine (SVM) Convolutional Neural Network (CNN) deep feature fusion Canonical Correlation Analysis (CCA) smart machinery digital agriculture Chinese agricultural diseases and pests named entity recognition adversarial training semantic enhancement technology innovation food processing transition pathways sustainable food systems transformation smart farming IoT WSN containerization multi-agent neural network LSTM leisure agricultural park traveler group COVID-19 pandemic fuzzy collaborative intelligence machine vision maize seeds classification deep learning convolutional neural network decision support systems agricultural water management water security data-driven modeling conceptual resilience model input uncertainty climate extreme process-based modeling vehicle routing problem fresh agricultural products split delivery NSGA-II algorithm farm management information system farmers’ information needs assessment soft system methodology smallholder farmers conceptual model Indonesian chili farmers residual block attention mechanism grape leaf disease aquatic products price forecast VMD IBES hybrid model precision agriculture sensor network semi-literate farmers interactive interface User Interface (UI) Android apps machine learning regression algorithms web application early prediction of crop yield grape detection self-attention buffalo breeds Neural Networks Self Activated CNN DeepLabv3+ semantic segmentation picking point identification e-commerce interest linkage participation willingness and behaviors government policies farmers’ cognition evolutionary game model structural equation model object detection YOLOv7 hemp duck count smart agriculture LoRaWAN water status supply chain horticulture logistics operations planning framework decision support n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes |
| topic_facet | grape varieties identification Support Vector Machine (SVM) Convolutional Neural Network (CNN) deep feature fusion Canonical Correlation Analysis (CCA) smart machinery digital agriculture Chinese agricultural diseases and pests named entity recognition adversarial training semantic enhancement technology innovation food processing transition pathways sustainable food systems transformation smart farming IoT WSN containerization multi-agent neural network LSTM leisure agricultural park traveler group COVID-19 pandemic fuzzy collaborative intelligence machine vision maize seeds classification deep learning convolutional neural network decision support systems agricultural water management water security data-driven modeling conceptual resilience model input uncertainty climate extreme process-based modeling vehicle routing problem fresh agricultural products split delivery NSGA-II algorithm farm management information system farmers’ information needs assessment soft system methodology smallholder farmers conceptual model Indonesian chili farmers residual block attention mechanism grape leaf disease aquatic products price forecast VMD IBES hybrid model precision agriculture sensor network semi-literate farmers interactive interface User Interface (UI) Android apps machine learning regression algorithms web application early prediction of crop yield grape detection self-attention buffalo breeds Neural Networks Self Activated CNN DeepLabv3+ semantic segmentation picking point identification e-commerce interest linkage participation willingness and behaviors government policies farmers’ cognition evolutionary game model structural equation model object detection YOLOv7 hemp duck count smart agriculture LoRaWAN water status supply chain horticulture logistics operations planning framework decision support n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes |
| url | ONIX_20230405_9783036566306_73 |