The merging of Artificial Intelligence (AI) and Internet-of-Things is known as Artificial Intelligence-of-Things (AIoT). IoT consists of interlinked computing devices and machines which can acquire, transfer, and execute field/industrial operations without human involvement, while AI processes the acquired data and helps extract the required information. The technologies work in synergy: AI enriches IoT through machine learning and deep learning-based data analysis and learning capabilities, whereas IoT enriches AI through data acquisition, connectivity, and data exchange.
Precision agriculture is becoming critically important for sustainable food production to meet the growing food demand. In recent decades, AI and IoT techniques have played an increasing role within industrial operations (e.g. autonomous manufacturing, automated supply chain management, predictive maintenance, smart energy grids, smart home appliances, and wearables), however, agricultural field operations are still heavily dependent on human labor. This is because these operations are ill-defined, unstructured, and susceptible to variation in natural conditions (e.g. illumination, landscape, atmosphere) plus the biological nature of crops (fruits, stems, leaves, and/or shoots continuously change their shape and/or color as they grow).
Technical advances in AI and IoT can help solve various agricultural field problems and optimize resource utilization (e.g. water, pesticide, fertilizer, seed, energy), improve production management and productivity, and reduce labor dependency. AI and IoT-enabled applications are increasingly used for precision agriculture applications such as crop growth monitoring, weed removal control, pest and disease detection, planting, crop yield estimation, targeted spraying and pollination, smart irrigation and nutrient management, field analysis, and plant phenotyping. For example, IoT-based AI applications using machine learning and deep learning are widely used to recognize fruits, vegetables, weeds, pests, and diseases; measure soil quality and nutrients. Such information will inform better crop management practices.
This Research Topic focuses on the recent advancement in the area of AIoT applications for precision agriculture for both field and specialty crops. Therefore, we invite researchers to contribute Original Research and Review articles focusing (but not limited to) novel perspectives and methods of IoT combined with various AI tools. These tools may include machine vision, artificial intelligence, deep learning, machine learning, artificial neural networks, convolutional neural networks, transfer learning, reinforcement learning, UAVs, and smart sensors.
The following subthemes are most welcome:
• Edge AI and IoT computing applications for field and specialty crops, and plant phenotyping;
• Machine vision and IoT for robotic operations, crop yield estimation, and farm management practices;
• Smart sensors and IoT applications for decision support systems and precision agriculture;
• AI and IoT empowered smart farm machinery;
• AI- and IoT-empowered UAVs applications for precision agriculture;
• AI and IoT-based crop monitoring;
• IoT-based sensing for in-field postharvest operations;
• Cloud and Edge-based methods for AI and IoT-empowered smart agriculture.
The merging of Artificial Intelligence (AI) and Internet-of-Things is known as Artificial Intelligence-of-Things (AIoT). IoT consists of interlinked computing devices and machines which can acquire, transfer, and execute field/industrial operations without human involvement, while AI processes the acquired data and helps extract the required information. The technologies work in synergy: AI enriches IoT through machine learning and deep learning-based data analysis and learning capabilities, whereas IoT enriches AI through data acquisition, connectivity, and data exchange.
Precision agriculture is becoming critically important for sustainable food production to meet the growing food demand. In recent decades, AI and IoT techniques have played an increasing role within industrial operations (e.g. autonomous manufacturing, automated supply chain management, predictive maintenance, smart energy grids, smart home appliances, and wearables), however, agricultural field operations are still heavily dependent on human labor. This is because these operations are ill-defined, unstructured, and susceptible to variation in natural conditions (e.g. illumination, landscape, atmosphere) plus the biological nature of crops (fruits, stems, leaves, and/or shoots continuously change their shape and/or color as they grow).
Technical advances in AI and IoT can help solve various agricultural field problems and optimize resource utilization (e.g. water, pesticide, fertilizer, seed, energy), improve production management and productivity, and reduce labor dependency. AI and IoT-enabled applications are increasingly used for precision agriculture applications such as crop growth monitoring, weed removal control, pest and disease detection, planting, crop yield estimation, targeted spraying and pollination, smart irrigation and nutrient management, field analysis, and plant phenotyping. For example, IoT-based AI applications using machine learning and deep learning are widely used to recognize fruits, vegetables, weeds, pests, and diseases; measure soil quality and nutrients. Such information will inform better crop management practices.
This Research Topic focuses on the recent advancement in the area of AIoT applications for precision agriculture for both field and specialty crops. Therefore, we invite researchers to contribute Original Research and Review articles focusing (but not limited to) novel perspectives and methods of IoT combined with various AI tools. These tools may include machine vision, artificial intelligence, deep learning, machine learning, artificial neural networks, convolutional neural networks, transfer learning, reinforcement learning, UAVs, and smart sensors.
The following subthemes are most welcome:
• Edge AI and IoT computing applications for field and specialty crops, and plant phenotyping;
• Machine vision and IoT for robotic operations, crop yield estimation, and farm management practices;
• Smart sensors and IoT applications for decision support systems and precision agriculture;
• AI and IoT empowered smart farm machinery;
• AI- and IoT-empowered UAVs applications for precision agriculture;
• AI and IoT-based crop monitoring;
• IoT-based sensing for in-field postharvest operations;
• Cloud and Edge-based methods for AI and IoT-empowered smart agriculture.