Crop pests and diseases are serious biological disasters in agricultural production, and are the dominant factors that restrict high-yield, high-quality, efficient, ecological, and safe agriculture. However, the basic and public research on the mechanisms and laws of major crop pests and diseases, large-scale and rapid monitoring methods for pests and diseases, and the prediction of disaster trends are still weak, resulting in passive prevention and control of pests and diseases. In recent years, extreme weather has been frequent, and the occurrence and spread of pests and diseases have been on the rise, posing a serious threat to grain security production. Traditional visual inspection methods for pests and diseases and limited meteorological forecasting methods at single sites are far from sufficient to meet the needs of large-scale and timely prevention and control of pests and diseases. The widespread use of pesticides has led to serious agricultural non-point source pollution and affected the quality and safety of agricultural products. Therefore, there is an urgent need to develop and apply large-scale and rapid monitoring and prediction methods for pests and diseases.

The macro, dynamic, fast, and continuous characteristics of remote sensing technology give it unique advantages in the monitoring and forecasting of crop pests and diseases. On the one hand, it can obtain real-time continuous information on the occurrence of pests and diseases on the ground, breaking through the traditional visual inspection method of single-point monitoring, and solving the problem of poor representativeness and effectiveness of traditional methods. On the other hand, it can quickly obtain continuous habitat information on the occurrence and development of pests and diseases, and combined with ground point observation and meteorological data, using geographic information systems, it can conduct suitability evaluation of crop pest occurrence and short- to medium-term prediction of development trends and migration directions, thus overcoming the limitation of long-term prediction using only meteorological data and historical data on pest occurrence.

Based on key technologies such as satellite-ground coordinated observation and satellite network, fast quantitative processing of multi-scale and temporal remote sensing data, and remote sensing product generation, this system brings together multiple data sources, model resources, thematic products, etc., to provide global-scale remote sensing monitoring and prediction of crop pests and diseases, supporting massive data processing and high-concurrency access. It realizes online calculation of models, production of monitoring and forecasting products, and formulation of macro-control decision-making technical solutions.

Based on this system, the research team monitors and predicts major crop pests and diseases and major migratory pests, including wheat yellow rust, wheat aphid, wheat sharp eyespot, wheat fusarium head blight,‍ rice leaf folder, rice blast, rice planthoppers, rice sheath blight, maize armyworm, maize northern leaf blight, soybean cyst nematode, soybean rust, soybean aphid, soybean bollworm, fall armyworm, Oriental migratory locust, and desert locust.

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