预测稻瘟病：机器学习与基于过程的模型
Predicting rice blast disease: machine learning versus process based models
摘要：
水稻是世界上第二大谷类作物，也是以水稻为主要主食的第一大粮食作物。稻瘟病是水稻栽培中最重要的生物限制因素，每年造成数百万美元的损失。尽管努力培育新的抗性品种，农业实践和化学防治仍然是最重要的疾病管理方法。因此，稻瘟病预测是支持水稻种植者控制病害的主要工具。在这项研究中，我们比较了四个模型预测稻瘟病，两个操作过程为基础的模型（吉野和WARM）和两种方法的基础上机器学习算法（M5规则和RNN），前者诱导基于规则的模型，后者建立一个神经网络。现场遥测对于获得用于预测模型的高质量现场数据非常重要，这是本研究所基于的Rice-GUARD项目的一个关键方面。根据作者的说法，这是第一次比较基于过程和机器学习建模方法来支持植物病害管理。
英文摘要：
Rice is the second most important cereal crop worldwide, and the first in terms of number of people who depend on it as a major staple food. Rice blast disease is the most important biotic constraint of rice cultivation causing each year millions of dollars of losses. Despite the efforts for breeding new resistant varieties, agricultural practices and chemical control are still the most important methods for disease management. Thus, rice blast forecasting is a primary tool to support rice growers in controlling the disease. In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and WARM) and two approaches based on machine learning algorithms (M5Rules and RNN), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
