Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.
Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.
复制标题
基于表型和机器学习技术判别温室生产中植物根区水分状况
DOI：
10.1038/s41598-017-08235-z
复制
发表时间：
2017-08-15
影响因子：
4.6
通讯作者：
Huang D
中科院分区：
文献类型：
--
作者：
Guo D;Juan J;Chang L;Zhang J;Huang D
基于植物的水分胁迫传感技术可以为温室精确灌溉系统提供灵敏、直接的参考依据。然而，植物信息的获取，解释和系统的应用仍然不足。本研究将表型分析与机器学习技术相结合，建立了温室植物根区水分状况的判别方法。以小白菜为试验材料，设置相对含水量为40%、60%和80%的3个根区水分水平。三个分类模型，随机森林（RF），神经网络（NN）和支持向量机（SVM）的开发和验证在不同的情况下，总体准确率超过90%。SVM模型的价值最高，但需要的训练时间最长。所有模型在所有场景下的准确率均超过85%，并且在RF模型中观察到更稳定的性能。由前五个最具贡献的性状建立的简化SVM模型准确率下降最大，为29.5%，而简化RF和NN模型仍保持约80%。对于真实的案例应用，在模型选择时应综合考虑运行成本、精度要求、系统反应时间等因素。我们的工作表明，通过实施表型分析和机器学习技术来识别植物根区水分状况，用于精确灌溉管理是有希望的。
Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.
登录
查看更多内容
DOI：
10.3390/s141120078
发表时间：
2014-10-24
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Li L;Zhang Q;Huang D
通讯作者：
Huang D
影响因子：
11.6
作者：
Chen, Dijun;Neumann, Kerstin;Klukas, Christian
通讯作者：
Klukas, Christian
影响因子：
7.5
作者：
Hand, David J.
通讯作者：
Hand, David J.
DOI：
10.13031/2013.9923
发表时间：
2002-07-01
期刊：
TRANSACTIONS OF THE ASAE
影响因子：
--
作者：
Kacira, M;Ling, PP;Short, TH
通讯作者：
Short, TH
DOI：
10.3390/s16050641
发表时间：
2016-05-05
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Navarro PJ;Pérez F;Weiss J;Egea-Cortines M
通讯作者：
Egea-Cortines M