Accurate and rapid disease identification at the beginning of an outbreak is essential for implementing effective management tactics. Diagnosis based on visual symptoms is often compromised by the inability to differentiate among similar symptoms caused by plants pathogens and abiotic disorders. Recent technological advances in sensors, machine vision, mechatronics, big data analytics, and artificial intelligence have enabled the development and implementation of remote sensing technologies for rapid disease identification and management. Early disease detection technologies can predict the spatial distribution of a disease outbreak for targeting precise management tactics. These technologies can be used to distinguish between a variety of disease symptoms in the field and/or transplant house, thus reducing both time and cost for diagnosis and management.
The use of remote sensing systems like satellite imagery, unmanned aerial vehicles (UAVs), and ground-based platforms together with big data analytics and artificial intelligence can improve disease detection and management, optimize agrochemical applications, increase profit, and reduce environmental impact. Artificial neural networks and deep learning are increasingly used in Intelligent Plant Protection (IPP). For example, image-based pattern recognition systems have been developed for pest and disease detection and target-based spray applications. Deep convolutional neural networks (CNNs) are the most widely used deep learning approach for image recognition. These methods and smart technologies have achieved dramatic improvements in many domains and have attracted considerable interest of both academic and industrial communities. Robotic sprayers can relieve the current stress on labor due to shortages of workers, improve crop production, and transform the food industry.
This Research Topic focuses on the recent advances and latest technical developments in artificial intelligence applications for IPP. We invite researchers to contribute original research articles, review articles, as well as opinion papers, and papers on perspectives and on novel methods in the areas of machine and computer vision, big data analytics, automation and robotics, machine learning, deep and transfer learning, reinforcement learning, and so on, with respect to intelligent disease detection and management.
Contributions will cover, but are not limited to, the following:
• Remote sensing, machine and computer vision for automated disease detection in the field and transplant houses
• Post-harvest disease detection and management
• Automated and smart spraying technologies
• Disease prediction modeling
• Food supply chain and disease management
Accurate and rapid disease identification at the beginning of an outbreak is essential for implementing effective management tactics. Diagnosis based on visual symptoms is often compromised by the inability to differentiate among similar symptoms caused by plants pathogens and abiotic disorders. Recent technological advances in sensors, machine vision, mechatronics, big data analytics, and artificial intelligence have enabled the development and implementation of remote sensing technologies for rapid disease identification and management. Early disease detection technologies can predict the spatial distribution of a disease outbreak for targeting precise management tactics. These technologies can be used to distinguish between a variety of disease symptoms in the field and/or transplant house, thus reducing both time and cost for diagnosis and management.
The use of remote sensing systems like satellite imagery, unmanned aerial vehicles (UAVs), and ground-based platforms together with big data analytics and artificial intelligence can improve disease detection and management, optimize agrochemical applications, increase profit, and reduce environmental impact. Artificial neural networks and deep learning are increasingly used in Intelligent Plant Protection (IPP). For example, image-based pattern recognition systems have been developed for pest and disease detection and target-based spray applications. Deep convolutional neural networks (CNNs) are the most widely used deep learning approach for image recognition. These methods and smart technologies have achieved dramatic improvements in many domains and have attracted considerable interest of both academic and industrial communities. Robotic sprayers can relieve the current stress on labor due to shortages of workers, improve crop production, and transform the food industry.
This Research Topic focuses on the recent advances and latest technical developments in artificial intelligence applications for IPP. We invite researchers to contribute original research articles, review articles, as well as opinion papers, and papers on perspectives and on novel methods in the areas of machine and computer vision, big data analytics, automation and robotics, machine learning, deep and transfer learning, reinforcement learning, and so on, with respect to intelligent disease detection and management.
Contributions will cover, but are not limited to, the following:
• Remote sensing, machine and computer vision for automated disease detection in the field and transplant houses
• Post-harvest disease detection and management
• Automated and smart spraying technologies
• Disease prediction modeling
• Food supply chain and disease management