About this Research Topic
Smart agriculture combines modern science and technology with agricultural cultivation, to achieve unmanned, automatic, intelligent management of agricultural production, such as intelligent irrigation, intelligent fertilization, and intelligent spraying. It is the application of artificial intelligence (AI) and Internet of Things (IoTs) in the field of modern agriculture. Agricultural AI (AAI) is the application of various information technologies and their cross-application in the field of agriculture, including intelligent equipment, IoTs, agricultural unmanned aerial vehicle, intelligent perception, deep learning, digital twin network, expert systems, agricultural cognitive computing, etc.
With the rapid development of smart agriculture, agricultural applications combined with deep learning are quite common, such as crop disease-pest detection, growth environment monitoring, automatic crop picking, unmanned farm management, etc. Edge computing can provide efficient and reliable new data processing solutions for multi-scenario and complex tasks in agriculture. At present, cloud computing, deep learning and digital twinning have been widely used in agricultural fields, such as plant identification and detection, pest diagnosis and recognition, remote sensing regional classification and monitoring, fruit carrier detection and agricultural product classification, animal identification and posture detection, etc.
Although smart agriculture is a rapidly developing field of scientific research, there are still several open research issues that need to be investigated and improved. IoTs make labour-free farms possible. However the complexity and diversity, as well as their size and widely distribution, pose many challenges in terms of network speed, computing storage, operation and maintenance management. This Research Topic will introduce new achievements in agricultural unmanned aerial vehicles, crop type mapping, crop phenotypic analysis, and identification of crop disease-pest in sustainable, intelligent phytoprotection, and intelligent agricultural applications. Original research and review articles are welcome.
A lot of topics in the smart agriculture field that are welcomed in this research topic, such as the application of IoTs, big data and deep learning to crop disease and pest identification, pest detection, plant species identification, crop yield prediction, precision fertilization, intelligent agriculture IoTs, food safety and other important issues in smart agriculture. Potential topics include but are not limited to the following:
• Agricultural Unmanned Aerial Vehicle (AUAV) for Precision Agriculture
• Smart Agriculture Based on IoT and Cloud Computing
• Knowledge Graph for Agriculture Data Management
• Deep Learning for Crop Disease and Insect Pest Recognition and Prediction
• Edge calculation for Precision Agriculture
• Expert Systems Crop Disease and Insect Pest Recognition and Prediction
• Lightweight Network for Smart Agriculture
• Digital Twin for Smart Agriculture
• Siamese Network for Plant Disease Detection
• Machine Learning for Crop Type Mapping
With the rapid development of smart agriculture, agricultural applications combined with deep learning are quite common, such as crop disease-pest detection, growth environment monitoring, automatic crop picking, unmanned farm management, etc. Edge computing can provide efficient and reliable new data processing solutions for multi-scenario and complex tasks in agriculture. At present, cloud computing, deep learning and digital twinning have been widely used in agricultural fields, such as plant identification and detection, pest diagnosis and recognition, remote sensing regional classification and monitoring, fruit carrier detection and agricultural product classification, animal identification and posture detection, etc.
Although smart agriculture is a rapidly developing field of scientific research, there are still several open research issues that need to be investigated and improved. IoTs make labour-free farms possible. However the complexity and diversity, as well as their size and widely distribution, pose many challenges in terms of network speed, computing storage, operation and maintenance management. This Research Topic will introduce new achievements in agricultural unmanned aerial vehicles, crop type mapping, crop phenotypic analysis, and identification of crop disease-pest in sustainable, intelligent phytoprotection, and intelligent agricultural applications. Original research and review articles are welcome.
A lot of topics in the smart agriculture field that are welcomed in this research topic, such as the application of IoTs, big data and deep learning to crop disease and pest identification, pest detection, plant species identification, crop yield prediction, precision fertilization, intelligent agriculture IoTs, food safety and other important issues in smart agriculture. Potential topics include but are not limited to the following:
• Agricultural Unmanned Aerial Vehicle (AUAV) for Precision Agriculture
• Smart Agriculture Based on IoT and Cloud Computing
• Knowledge Graph for Agriculture Data Management
• Deep Learning for Crop Disease and Insect Pest Recognition and Prediction
• Edge calculation for Precision Agriculture
• Expert Systems Crop Disease and Insect Pest Recognition and Prediction
• Lightweight Network for Smart Agriculture
• Digital Twin for Smart Agriculture
• Siamese Network for Plant Disease Detection
• Machine Learning for Crop Type Mapping
Keywords: Smart Agriculture, Digital Twin, Siamese Network, Agricultural Digitalization, Agricultural IoT, IoT Data Analysis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
