Internet and Computers for Agriculture

doi:10.3390/books978-3-0365-6631-3

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Internet and Computers for Agriculture

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February 2023

436 pages

ISBN978-3-0365-6630-6 (Hardback)
ISBN978-3-0365-6631-3 (PDF)

https://doi.org/10.3390/books978-3-0365-6631-3

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This book is a reprint of the Special Issue Internet and Computers for Agriculture that was published in

Biology & Life Sciences

Engineering

Environmental & Earth Sciences

Summary

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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Keywords

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; digital agriculture; 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; convolutional neural network; residual block; attention mechanism; grape leaf disease; aquatic products price forecast; VMD; IBES; LSTM; hybrid model; smart farming; precision agriculture; IoT; 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; convolutional neural network; self-attention; deep learning; buffalo breeds; Neural Networks; Self Activated CNN; deep learning; DeepLabv3+; deep learning; 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; attention mechanism; deep learning; hemp duck count; smart agriculture; IoT; LoRaWAN; WSN; water status; supply chain; horticulture; logistics; operations; planning framework; decision support; n/a