水稻病害监测预警数据

1、通过对监测数据进行统计分析，可以分析出不同水稻品种容易发生的病害有哪些，可以帮助水稻种植业从业者提前做好相关的水稻病害预防措施，保障产量；
2、通过对监测数据进行统计分析，可以分析出同年不同地区水稻病害发展趋势，可以帮助行业科研人员、政府决策部门等，提供研究、决策支撑。病害识别的算法包括以下几个方面：
1、病害模型训练数据采集：通过各水稻生产区大量拍照采集水稻照片，作为原始数据；
2、病害模型训练数据处理：加载原始水稻多光谱图片，根据不同的病害，在不同光谱下的表现特征，进行标定训练，产生不同病害分类及特征库；
3、病害分析识别：对于用户上传的水稻病害图片，通过已训练出来的病害模型库，进行自动匹配，确认近似哪种病害；
4、病害识别应用：识别出哪种病害后，可以结合病害知识库，匹配该病害所对应的特征描述、防治方法、防治药物等信息，辅助生产者及时处理病害问题。
5、病害识别数据归集：通过采集归集病害识别数据，形成平台层面的病害监测数据，以便进一步应用于生产、研究的辅助及辅助决策等。

1. Statistical analysis of monitoring data can identify the diseases prone to affect different rice varieties, helping rice planting practitioners take targeted preventive measures in advance to ensure crop yield.
2. Statistical analysis of monitoring data can also reveal the development trends of rice diseases in different regions within the same year, providing research and decision-making support for industry researchers, government decision-making departments and other relevant stakeholders.
The disease recognition algorithm includes the following aspects:
1. Collection of disease model training data: Collect a large number of rice photos via photography across various rice production areas as raw data.
2. Processing of disease model training data: Load the original rice multispectral images, and conduct calibration training based on the performance characteristics of different diseases under different spectra to generate classification and feature libraries for various diseases.
3. Disease analysis and recognition: For rice disease images uploaded by users, automatically match them with the trained disease model library to confirm the similar disease type.
4. Application of disease recognition: After identifying the specific disease, retrieve the corresponding characteristic description, control methods, control pesticides and other relevant information from the disease knowledge base to assist producers in timely addressing the disease issue.
5. Aggregation of disease recognition data: Collect and consolidate disease recognition data to form platform-level disease monitoring data, which can be further applied to production assistance, research support, auxiliary decision-making and other scenarios.