The development of Smart Plant Protection systems (i.e., intelligent phytoprotection ) has attracted a lot of attention. They aim to monitor, protect, and managing plant production using widely networked IoT devices to efficiently improve crop health, quality, and quantity. Smart Plant Protection systems have shown great potential in reforming many key components of plant production, such as autonomous and timely observation, monitoring and management of pests, wise and fine-grained use of pesticides, optimized use of seeds, fertilizers, fuel, time and labor, etc.
In recent years, the extensive development and achievements of deep learning techniques have shown great opportunities and significance for epoch-making improvements in almost all dimensions, making modern artificial intelligence techniques the key driver. Therefore, AI-based services for intelligent crop protection are also of great importance and urgent need. For example, the systems will continuously collect multi-modal data from huge amount of sensor devices distributed over the planted area and apply deep learning models like CNN, LSTM, GNN to gain accurate insights about the health of the plants, moisture of the soils etc. to manage various negative impacts like pest infestation in an economical and eco-friendly manner. AI-assisted intelligent crop protection systems are expected to significantly improve the sustainability of crop production from many aspects and with high granularity.
However, the development and adoption of smart plant protection systems is non-trivial in every aspect and is still at an early stage. On the one hand, the design of learning models for smart plant protection needs to meet various requirements and circumstances. For example, the different sensor data collected in the systems are quite heterogeneous both in terms of modalities and domains, while they all provide some meaningful and interrelated clues for analysis. Then, it is a very difficult task to develop appropriate models for services such as assessing the detailed status and potential threats to the health of different plants. Moreover, considering that devices with limited capabilities will be distributed over the area and are expected to work for a sufficiently long period of time, it is a big challenge to make the networked smart plant protection systems fit for AI services. Critical components such as network topology and operation, data flow schedule, and management of collected data in the smart crop protection systems are all tricky and should be designed properly.
Considering the trend of improving smart plant protection with AI techniques, this Research Topic welcomes original and innovative research that can form the basis of AI-enabled services for smart plant protection systems. Potential topics include the following:
- Architecture and frameworks for smart plant protection systems
- Machine learning methods for smart plant protection systems
- Deep learning methods for smart plant protection systems
- Data acquisition and aggregation for smart plant protection systems
- Multimedia data processing and analysis for smart plant protection systems
- Long-term and sustainable data acquisition and storage for smart plant protection systems
- Security, privacy and safety for intelligent crop protection systems
- Device deployment and network connectivity for smart plant protection systems
- Network operation and resource allocation for intelligent crop protection systems
- Data trading and tracking in smart plant protection systems
- Blockchain design for smart plant protection systems
- Applications for AI-enabled services in smart plant protection systems
The development of Smart Plant Protection systems (i.e., intelligent phytoprotection ) has attracted a lot of attention. They aim to monitor, protect, and managing plant production using widely networked IoT devices to efficiently improve crop health, quality, and quantity. Smart Plant Protection systems have shown great potential in reforming many key components of plant production, such as autonomous and timely observation, monitoring and management of pests, wise and fine-grained use of pesticides, optimized use of seeds, fertilizers, fuel, time and labor, etc.
In recent years, the extensive development and achievements of deep learning techniques have shown great opportunities and significance for epoch-making improvements in almost all dimensions, making modern artificial intelligence techniques the key driver. Therefore, AI-based services for intelligent crop protection are also of great importance and urgent need. For example, the systems will continuously collect multi-modal data from huge amount of sensor devices distributed over the planted area and apply deep learning models like CNN, LSTM, GNN to gain accurate insights about the health of the plants, moisture of the soils etc. to manage various negative impacts like pest infestation in an economical and eco-friendly manner. AI-assisted intelligent crop protection systems are expected to significantly improve the sustainability of crop production from many aspects and with high granularity.
However, the development and adoption of smart plant protection systems is non-trivial in every aspect and is still at an early stage. On the one hand, the design of learning models for smart plant protection needs to meet various requirements and circumstances. For example, the different sensor data collected in the systems are quite heterogeneous both in terms of modalities and domains, while they all provide some meaningful and interrelated clues for analysis. Then, it is a very difficult task to develop appropriate models for services such as assessing the detailed status and potential threats to the health of different plants. Moreover, considering that devices with limited capabilities will be distributed over the area and are expected to work for a sufficiently long period of time, it is a big challenge to make the networked smart plant protection systems fit for AI services. Critical components such as network topology and operation, data flow schedule, and management of collected data in the smart crop protection systems are all tricky and should be designed properly.
Considering the trend of improving smart plant protection with AI techniques, this Research Topic welcomes original and innovative research that can form the basis of AI-enabled services for smart plant protection systems. Potential topics include the following:
- Architecture and frameworks for smart plant protection systems
- Machine learning methods for smart plant protection systems
- Deep learning methods for smart plant protection systems
- Data acquisition and aggregation for smart plant protection systems
- Multimedia data processing and analysis for smart plant protection systems
- Long-term and sustainable data acquisition and storage for smart plant protection systems
- Security, privacy and safety for intelligent crop protection systems
- Device deployment and network connectivity for smart plant protection systems
- Network operation and resource allocation for intelligent crop protection systems
- Data trading and tracking in smart plant protection systems
- Blockchain design for smart plant protection systems
- Applications for AI-enabled services in smart plant protection systems
