Publication Date:
2023-04-05
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.
Keywords:
grape varieties identification
;
Support Vector Machine (SVM)
;
Convolutional Neural Network (CNN)
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deep feature fusion
;
Canonical Correlation Analysis (CCA)
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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)
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Android apps
;
machine learning
;
regression algorithms
;
web application
;
early prediction of crop yield
;
grape detection
;
self-attention
;
buffalo breeds
;
Neural Networks
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Self Activated CNN
;
DeepLabv3+
;
semantic segmentation
;
picking point identification
;
e-commerce interest linkage
;
participation willingness and behaviors
;
government policies
;
farmers’ cognition
;
evolutionary game model
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structural equation model
;
object detection
;
YOLOv7
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hemp duck count
;
smart agriculture
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LoRaWAN
;
water status
;
supply chain
;
horticulture
;
logistics
;
operations
;
planning framework
;
decision support
;
n/a
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bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general
;
bic Book Industry Communication::P Mathematics & science::PS Biology, life sciences
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bic Book Industry Communication::T Technology, engineering, agriculture
Language:
English
Format:
application/octet-stream
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