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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
- adversarial training
- agricultural water management
- Android apps
- aquatic products price forecast
- attention mechanism
- 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 & science
- multi-agent
- n/a
- named entity recognition
- Neural Network
- neural networks
- NSGA-II algorithm
- object detection
- operations
- participation willingness and behaviors
- Physics
- picking point identification
- planning framework
- precision agriculture
- process-based modeling
- Reference, information & interdisciplinary subjects
- regression algorithms
- Research & 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
- transformation
- transition pathways
- traveler group
- User Interface (UI)
- vehicle routing problem
- VMD
- Water security
- water status
- web application
- WSN
- YOLOv7
Links
DOI: 10.3390/books978-3-0365-6631-3Editions