| 机器学习技术在现代农业气象中的应用 |
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| 引用本文： | 李颖,陈怀亮.机器学习技术在现代农业气象中的应用[J].应用气象学报,2020,31(3):257-266. |
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| 作者姓名： | 李颖 陈怀亮 |
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| 作者单位： | 1.中国气象局·河南省农业气象保障与应用技术重点实验室, 郑州 450003 |
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| 摘 要： | 智慧气象和精准农业结合下的现代农业气象工作意味着对包含遥感影像在内的大型农业和气象数据高时效性的分析与处理，机器学习技术是当代自然科学研究和技术发展的主流技术，亦是现代农业气象科研和业务发展的重要工具。该文系统论述了机器学习技术的主要方法及其在现代农业气象中的主要应用方向，比较了不同方法在农业气象不同领域应用的情况，侧重介绍了基于深度学习技术的成果和近年来的最新研究进展。传统浅层机器学习技术中，以支持向量机和人工神经网络应用最为广泛且效果最为理想。近年来，随机森林和梯度提升机等决策树集成方法普遍取得优于核方法的精度，深度学习技术则在某些任务中取得更优于集成学习的精度。未来，有待检验机器学习技术特别是深度学习技术在更多农业气象问题上的适用性和先进性，更好地迎接现代农业气象发展的新挑战与新机遇。
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| 关 键 词： | 机器学习 深度学习 农业气象 农业遥感 |
| 收稿时间： | 2019-10-22 |
Review of Machine Learning Approaches for Modern Agrometeorology |
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| Affiliation: | 1.CMA·Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 4500032.Henan Institute of Meteorological Sciences, Zhengzhou 4500033.Henan Meteorological Service, Zhengzhou 450003 |
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| Abstract: | With the development of smart meteorology and precision agriculture, modern agrometeorology tasks demand for efficient analyzing and processing of massive agricultural and meteorological data, including multi-source remote sensing images. Machine learning technology can powerfully contribute to the development of agrometeorology and the innovation of agrometeorological service mode. A targeted overview on the related work of machine learning in modern agrometeorology domains is given, including mapping and zoning, detection and observation, yield prediction, and parameter prediction, with specially focuses on deep learning approaches for agrometeorology and the latest research progress in recent years. From the aspect of mapping and zoning, machine learning technology can be combined with remote sensing images to map land cover and crop types in different scales, and can also be combined with remote sensing data, soil data and statistical data to make thematic maps of crop growth and vegetation quality and to zone crop management areas. From the aspect of detection and observation, machine learning technology is successfully used to detect weeds in field images. Deep learning technology is used in plant phenotype observation, disease and pest detection, obstacles and anomaly detection, fruit counting and so on with high accuracy, which could greatly improve the level of agrometeorology automatic observation. From the aspect of yield prediction, machine leaning technology combined with remote sensing time series data, meteorological data and soil data is successfully used to predict the yield of different crops in different scales. Machine learning technology also has great application potential in loss assessment for agrometeorological disasters. From the aspect of parameter prediction, the hydrological, soil and crop parameters concerned by agrometeorology tasks such as evapotranspiration, leaf area index, soil moisture and nitrogen can be accurately inverted and predicted by the combination of machine learning technology, meteorological data and remote sensing data. Overall, among the traditional machine learning approaches, support vector machine and artificial neural network are the most widely used and the most ideal methods. In recent years, ensemble-based methods such as random forest and gradient boosting machine have generally achieved higher accuracy than kernel methods, while deep learning approaches have achieved higher accuracy than ensemble-based methods in some tasks. In the future, it is necessary to verify the applicability and advancement of more different machine learning approaches, especially deep learning approaches in more different agrometeorological tasks, and choose the most suitable machine learning technology for each specific task in modern agrometeorological services according to the data using, which will help to meet new challenges and opportunities of the modern agrometeorology development. |
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