Estimation of Chlorophyll Content in Potato Leaves Based on
Machine Learning
LI Cheng-ju1, 3, LIU Yin-du1, 3, QIN Tian-yuan1, 3, WANG Yi-hao1, 3, FAN You-fang1, 3, YAO Pan-feng2, 3, SUN Chao1, 3, BI Zhen-zhen1, 3*, BAI Jiang-ping1, 3*
1. College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China
2. State Key Laboratory of Aridland Crop Science, Lanzhou 730070, China
3. Gansu Key Laboratory of Crop Improvement & Germplasm Enhancement, Gansu Agricultural University, Lanzhou 730070, China
Abstract：To improve the accuracy of the photo chlorophyll content estimation model, the remote sensing images of different growth stages of potatoes under control and drought treatments were obtained using a multi-spectral camera on a UAV platform. Thirteen vegetation indices were selected as input variables of the chlorophyll content inversion model, and the estimation model of potato chlorophyll content was constructed by using multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR) and decision tree regression (DTR). Correlation analysis between vegetation index and chlorophyll content showed that at the tuber formation stage of the control treatment, the absolute values of correlation coefficients between CIre, GNDVI, NDVIre, NDWI, GRVI, LCI and chlorophyll content were above 0.5, and their were significant (p<0.05) or highly significant (p<0.01) correlations. In other growth stages of potato, the absolute values of correlation coefficients between 13 vegetation indexes and chlorophyll content were all above 0.5, which was a highly significant correlation (p<0.001). In addition, the accuracy of MLR, SVR, RFR and DTR models were compared. The results showed that the SVR model has the best prediction effects in the tuber formation stage, tuber expansion stage and starch accumulation stage of the control treatment. The control treatment's R2 and RMSE were 0.89 and 2.11 in the tuber formation stage, 0.59 and 4.03 in the tuber expansion stage, and 0.80 and 3.18 in the starch accumulation stage. The RFR model produces the best prediction effects in the tuber formation, tuber expansion, and starch accumulation stages of the drought treatment. The outcomes of R2 and RMSE on drought treatment were 0.90 and 1.57 in the tuber formation stage, 0.87 and 2.16 in the tuber expansion stage, and 0.63 and 3.01 in the starch accumulation stage. This study presents a new approach for monitoring the chlorophyll content of potatoes, and a corresponding estimating model can be selected based on the specific potato growth stage and different experimental treatments in future.
Key words：Potato; Chlorophyll content; Multispectral; Support vector regression; Random forest regression; Decision tree regression
李成举，刘寅笃，秦天元，王一好，范又方，姚攀锋，孙 超，毕真真，白江平. 基于机器学习的马铃薯叶片叶绿素含量估算[J]. 光谱学与光谱分析, 2024, 44(04): 1117-1127.
LI Cheng-ju, LIU Yin-du, QIN Tian-yuan, WANG Yi-hao, FAN You-fang, YAO Pan-feng, SUN Chao, BI Zhen-zhen, BAI Jiang-ping. Estimation of Chlorophyll Content in Potato Leaves Based on
Machine Learning. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1117-1127.
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