Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
基于表型和机器学习技术的温室生产中植物根区水分状况判别
作者: Doudou GuoJiaxiang JuanLiying ChangJingjin ZhangDanfeng Huang
作者单位: 10000 0004 0368 8293, grid.16821.3c, School of agriculture and biology, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
刊名: Scientific Reports, 2017, Vol.7 (1), pp.30-40
来源数据库: Springer Nature Journal
DOI: 10.1038/s41598-017-08235-z
英文摘要: Abstract(#br)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...
全文获取路径: Springer Nature  (合作)
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影响因子:2.927 (2012)

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