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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Format | eBook |
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Language | English |
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Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
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Online Access | Get full text |
ISBN | 3036566317 9783036566313 3036566309 9783036566306 |
DOI | 10.3390/books978-3-0365-6631-3 |
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Abstract | 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. |
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AbstractList | 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. |
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SubjectTerms | adversarial training agricultural water management Android apps aquatic products price forecast attention mechanism Biology, life sciences buffalo breeds Canonical Correlation Analysis (CCA) Chinese agricultural diseases and pests classification climate extreme conceptual model conceptual resilience model containerization convolutional neural network Convolutional Neural Network (CNN) COVID-19 pandemic data-driven modeling decision support decision support systems deep feature fusion deep learning DeepLabv3 digital agriculture e-commerce interest linkage early prediction of crop yield evolutionary game model farm management information system farmers’ cognition farmers’ information needs assessment food processing fresh agricultural products fuzzy collaborative intelligence government policies grape detection grape leaf disease grape varieties identification hemp duck count horticulture hybrid model IBES Indonesian chili farmers input uncertainty interactive interface IoT leisure agricultural park logistics LoRaWAN LSTM machine learning machine vision maize seeds Mathematics and Science multi-agent n/a named entity recognition neural network Neural Networks NSGA-II algorithm object detection operations participation willingness and behaviors picking point identification planning framework precision agriculture process-based modeling Reference, Information and Interdisciplinary subjects regression algorithms Research and information: general residual block Self Activated CNN self-attention semantic enhancement semantic segmentation semi-literate farmers sensor network smallholder farmers smart agriculture smart farming smart machinery soft system methodology split delivery structural equation model supply chain Support Vector Machine (SVM) sustainable food systems technology innovation Technology, Engineering, Agriculture, Industrial processes transformation transition pathways traveler group User Interface (UI) vehicle routing problem VMD water security water status web application WSN YOLOv7 |
Title | Internet and Computers for Agriculture |
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