Plant pests and diseases cause an annual average of 40% global food failure addressed by FAO and more than 100 billion dollars in loss of forest and grass resources. Scientific prevention and control of pests and diseases in agriculture, forestry and grass is important to ensure food security, ecological and environmental safety, etc. At present, the accuracy of individual identification of agricultural, forestry and grass pests and diseases are low, making it difficult to achieve accurate outpost warning, occurrence environment monitoring and multiple pest and disease type differentiation, resulting in the inability to achieve early detection and control of pests and diseases. With the rapid development of remote sensing, big data, and artificial intelligence technologies, information technology has been widely used in agriculture, forestry and grass pest and disease precision monitoring and forecasting. Digital precision monitoring and forecasting of major pests and diseases have become a major development trend in the agriculture, forestry, and grass industry.
This research topic aims to collect the latest advances related to digital accurate monitoring and forecasting of pests and diseases in agriculture, forestry, and grass. We welcome research on monitoring of vegetation parameters, digital image processing of pests and diseases, and monitoring and forecasting of pests and diseases, such as inversion of vegetation physical and chemical parameters, vegetation growth monitoring, identification of individual species of pests and diseases, quantitative extraction of pests and diseases, early warning of pest and disease outposts, and rapid monitoring and evaluation of large areas of pests and diseases. The research topic will provide key technologies and solutions for digital monitoring and early warning of pests and diseases, and the established multidisciplinary cross-fertilization and collaborative innovation mechanism is of great significance for promoting the construction of plant protection systems and the development of pest and disease monitoring and forecasting industry.
We welcome submissions of the following article types: Hypothesis & Theory, Methods, Mini Review, Original Research, Review, Technology and Code. Articles may address, but are not limited, to the following topics:
• Data processing (multispectral, hyperspectral, thermal, etc.)
• Inversion of vegetation physical and chemical parameters
• Vegetation growth monitoring
• Pest and disease image processing
• Pests and disease habitat monitoring
• Damage evaluation
• Pest and disease forecasting
• Pest and disease damage assessment
Plant pests and diseases cause an annual average of 40% global food failure addressed by FAO and more than 100 billion dollars in loss of forest and grass resources. Scientific prevention and control of pests and diseases in agriculture, forestry and grass is important to ensure food security, ecological and environmental safety, etc. At present, the accuracy of individual identification of agricultural, forestry and grass pests and diseases are low, making it difficult to achieve accurate outpost warning, occurrence environment monitoring and multiple pest and disease type differentiation, resulting in the inability to achieve early detection and control of pests and diseases. With the rapid development of remote sensing, big data, and artificial intelligence technologies, information technology has been widely used in agriculture, forestry and grass pest and disease precision monitoring and forecasting. Digital precision monitoring and forecasting of major pests and diseases have become a major development trend in the agriculture, forestry, and grass industry.
This research topic aims to collect the latest advances related to digital accurate monitoring and forecasting of pests and diseases in agriculture, forestry, and grass. We welcome research on monitoring of vegetation parameters, digital image processing of pests and diseases, and monitoring and forecasting of pests and diseases, such as inversion of vegetation physical and chemical parameters, vegetation growth monitoring, identification of individual species of pests and diseases, quantitative extraction of pests and diseases, early warning of pest and disease outposts, and rapid monitoring and evaluation of large areas of pests and diseases. The research topic will provide key technologies and solutions for digital monitoring and early warning of pests and diseases, and the established multidisciplinary cross-fertilization and collaborative innovation mechanism is of great significance for promoting the construction of plant protection systems and the development of pest and disease monitoring and forecasting industry.
We welcome submissions of the following article types: Hypothesis & Theory, Methods, Mini Review, Original Research, Review, Technology and Code. Articles may address, but are not limited, to the following topics:
• Data processing (multispectral, hyperspectral, thermal, etc.)
• Inversion of vegetation physical and chemical parameters
• Vegetation growth monitoring
• Pest and disease image processing
• Pests and disease habitat monitoring
• Damage evaluation
• Pest and disease forecasting
• Pest and disease damage assessment
