摘要
【目的】以油青菜心Brassica chinensis var.parachinensis为试验对象,基于冠层温度研究其生长过程中的水分胁迫变化规律,并利用机器学习方法,以水分胁迫指数(Crop water stress index,CWSI)和光合有效辐射预测光合作用速率。【方法】试验期间,在营养生长阶段(V期)和生殖生长阶段(R期)对油青菜心进行不同田间持水量处理,采集冠层温度、空气温湿度数据,建立无蒸腾作用基线(上限方程)、无水分胁迫基线(下限方程),通过经验公式计算CWSI。利用基于密度的空间聚类方法和空气温度研究油青菜心的冠气温差上限分布情况,选取固定值作为上限;以CWSI经验公式为基础,使用不同温度定值的无蒸腾作用基线计算CWSI,验证聚类效果。为更简便获取光合作用速率,使用4种机器学习方法:最邻近节点算法(k-Nearest neighbor,KNN)、支持向量回归(Support vector regression,SVR)、极端梯度提升法(Extreme gradient boosting,XGBoost)、随机森林(Random forest,RF)进行预测,并对比预测效果。【结果】在不同田间持水量处理下,CWSI能较好地监测油青菜心水分胁迫状况。通过聚类分析,将V期和R期冠气温差上限分类到2个簇中,得到簇心分别为3.4和4.2℃,与CWSI经验公式计算值显著相关,表明使用固定值作为油青菜心冠气温差上限值具有可行性。KNN、SVM、XGBoost和RF预测模型均取得较好效果,相关系数分别为0.873、0.877、0.887和0.863。【结论】机器学习方法可用于油青菜心光合作用速率的预测,可以避免使用大型笨重仪器,降低对油青菜心叶片的损伤,减少测量时间。
【Objective】Brassica chinensis var.parachinensis was used as the experimental object to study the change rule of water stress during growth process based on canopy temperature,and the machine learning method was used for predicting the photosynthetic rate based on crop water stress index(CWSI)and photosynthetically active radiation.【Method】During the test,the experiment adopted different field capacities for B.chinensis var.parachinensis at the vegetative growth stage(V stage)and reproductive growth stage(R stage),collected the canopy temperature,air temperature and humidity data,established non-transpirationbaseline(upper limit equation),non-water-stress-baseline(lower limit equation),and calculated CWSI by empirical formulation.Cluster method of density-based spatial clustering of application with noise and air temperature were used to study the upper limit distribution of canopy temperature minus air temperature of B.chinensis var.parachinensis,and the fixed values were selected as the upper limit.Based on the CWSI empirical formulation,CWSI was calculated using the non-transpiration-baselines with different temperature fixed values to verify the clustering effect.In order to obtain the photosynthetic rate more easily,four machine learning methods of k-nearest neighbor(KNN),support vector regression(SVR),extreme gradient boosting(XGBoost)and random forest(RF)were used for prediction,and the prediction effects were compared.【Result】Under different field capacities,CWSI could better monitor the water stress status of B.chinensis var.parachinensis.Through cluster analysis,the upper limit of canopy temperature minus air temperature at V stage and R stage was classified into two clusters,and the cluster centers were 3.4 and 4.2℃,respectively,which were significantly correlated with the values calculated by the empirical formula of CWSI,indicating that it was feasible to use a fixed value as the upper limit of canopy temperature minus air temperature in B.chinensis var.parachinensis.The prediction models of KNN,SVM,XGBoost and RF all achieved good results,and the correlation coefficients were 0.873,0.877,0.887 and 0.863,respectively.【Conclusion】Machine learning can be used for predicting the photosynthetic rate of B.chinensis var.parachinensis,avoid the use of large and cumbersome instruments,reduce the damage to the leaves of B.chinensis var.parachinensis,and reduce the measurement time.
作者
杨明欣
高鹏
陈文彬
周平
孙道宗
谢家兴
陆健强
王卫星
YANG Mingxin;GAO Peng;CHEN Wenbin;ZHOU Ping;SUN Daozong;XIE Jiaxing;LU Jianqiang;WANG Weixing(College of Electronic Engineering/College of Artificial Intelligence,South China Agricultural University,Guangzhou 510642,China;Guangdong Engineering Research Center for Monitoring Agricultural Information,Guangzhou 510642,China)
出处
《华南农业大学学报》
CAS
CSCD
北大核心
2021年第5期117-126,共10页
Journal of South China Agricultural University
基金
广东省重点领域研发计划(2019B020214003)。
关键词
油青菜心
水分胁迫指数
光合作用速率
机器学习
Brassica chinensis var.parachinensis
crop water stress index
photosynthetic rate
machine learning
