Plant diseases are an important factor in determining how environmental factors affect plant growth and development. They can assist in plant breeding and cultivation, ensuring crop yield and quality. Traditional disease control methods are increasingly difficult to match modern time, space, and cost demands. With the continuous iteration and updating of computer resources, deep learning (DL) technology has developed rapidly in recent years. By adaptive training on a large amount of data, DL technology can improve the efficiency of plant disease diagnosis and detection, and greatly reduce the risk of human intervention and misjudgment. Additionally, DL technology can combine with other technologies, such as the Internet of Things and cloud computing, to build an intelligent plant disease monitoring and control system. This system can monitor and predict the spread and occurrence of diseases in real-time, providing timely and effective guarantees for agricultural production. Nonetheless, challenges such as data acquisition, environmental interference, and model deployment complexity hinder DL's application in plant disease control. Therefore, optimizing the algorithm design for disease characteristics is crucial for successful implementation.
The goal of this research topic is to provide a platform to share the latest research achievements of deep learning (DL) in plant disease control. The focus on the progress of DL technology in this field mainly involves the application of advanced technology in plant disease feature adaptation. Effective algorithms are urgently needed to protect current large-scale agricultural production. We hope to improve the performance of algorithms and increase the accessibility of researchers to their research results. This research will help improve disease control strategies. At the same time, we hope that researchers can share their datasets to contribute to the application of DL in agriculture. In addition, we look forward to researchers deploying the model to reflect the ability of DL in plant disease control.
This topic will focus on discussing the latest research and applications of deep learning technology in plant disease identification and control, and other agricultural science research is also welcome. The following topics are of interest, but not limited to:
1. Identification techniques for early plant diseases
2. Prediction and prevention of the spread of plant diseases
3. New methods for collecting multi-source plant disease image data
4. Quantitative evaluation of plant damage
5. Real-time application deployment based on embedded devices and edge wearable devices
6. Adaptive image processing methods for different application environments
Plant diseases are an important factor in determining how environmental factors affect plant growth and development. They can assist in plant breeding and cultivation, ensuring crop yield and quality. Traditional disease control methods are increasingly difficult to match modern time, space, and cost demands. With the continuous iteration and updating of computer resources, deep learning (DL) technology has developed rapidly in recent years. By adaptive training on a large amount of data, DL technology can improve the efficiency of plant disease diagnosis and detection, and greatly reduce the risk of human intervention and misjudgment. Additionally, DL technology can combine with other technologies, such as the Internet of Things and cloud computing, to build an intelligent plant disease monitoring and control system. This system can monitor and predict the spread and occurrence of diseases in real-time, providing timely and effective guarantees for agricultural production. Nonetheless, challenges such as data acquisition, environmental interference, and model deployment complexity hinder DL's application in plant disease control. Therefore, optimizing the algorithm design for disease characteristics is crucial for successful implementation.
The goal of this research topic is to provide a platform to share the latest research achievements of deep learning (DL) in plant disease control. The focus on the progress of DL technology in this field mainly involves the application of advanced technology in plant disease feature adaptation. Effective algorithms are urgently needed to protect current large-scale agricultural production. We hope to improve the performance of algorithms and increase the accessibility of researchers to their research results. This research will help improve disease control strategies. At the same time, we hope that researchers can share their datasets to contribute to the application of DL in agriculture. In addition, we look forward to researchers deploying the model to reflect the ability of DL in plant disease control.
This topic will focus on discussing the latest research and applications of deep learning technology in plant disease identification and control, and other agricultural science research is also welcome. The following topics are of interest, but not limited to:
1. Identification techniques for early plant diseases
2. Prediction and prevention of the spread of plant diseases
3. New methods for collecting multi-source plant disease image data
4. Quantitative evaluation of plant damage
5. Real-time application deployment based on embedded devices and edge wearable devices
6. Adaptive image processing methods for different application environments
