基于分光测色反演哈密瓜叶片叶绿素与含水率研究

doi:10.3964/j.issn.1000-0593(2024)08-2296-07

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Abstract In order to predict the water content and chlorophyll content of cantaloupe leaves quickly and accurately and improve the accurate management level of Cantaloupe crops, the leaves of cantaloupe in three different growth stages, namely the growing stage, the flowering stage, and the fruiting stage, were selected as experimental research objects by using the spectrophotometry technology. The correlation changes of leaf temperature, leaf water content, and chlorophyll content with LAB eigenvalues of color space were studied in three different collection periods: 9:00—10:00, 14:00—15:00, and 20:00—21:00, respectively. The least square method (LS) was used to preprocess the changes in temperature, water content, chlorophyll content, and color eigenvalues of different samples, and the eigenvalues with the best fit were selected for regression analysis and prediction model verification. The results showed that① Leaf temperature, leaf water content, and chlorophyll content had different color eigenvalues under different parameters. ② For leaves with 84%~93% moisture content, leaf temperature, and chlorophyll content were negatively correlated with leaf moisture content. ③ the chlorophyll content and leaf water content and color space LAB, there is a linear correlation. As the leaf water content -rises, L is on the rise, and the color becomes shallow gradually with light green leaves; with the increase of chlorophyll, L has a downward trend, showing the leaf color gradually deepens with black-green, exists in all types of sample data, L B positive. ④ Through model prediction and evaluation, random forest (RF), partial least squares (PLS), support vector machine (SVM), and LASSO can be used to predict chlorophyll content effectively. Among the chlorophyll prediction models, RF had the best prediction performance, R²_c=0.939, RMSE_C=0.868 and MAE=0.686, R²_p=0.915, RMSE_P=1.194 and MAE=0.942. ⑤ Through model prediction and evaluation, RF, PLS, AdaBoost, and polynomial regression (POLYNOMIAL) can effectivelypredict leaf water contents. In the prediction model of leaf moisture content, the POLYNOMIAL prediction performance is the best, R²_c=0.884, RMSE_C=0.005 9 and MAE=0.005 2, R²_p=0.920 and RMSE_P=0.006 2 and MAE=0.005 7. The spectrophotometry method can effectively and rapidly determine leaf water and chlorophyll content, which is expected to provide an optional feasible method for nondestructive, rapid, and accurate determination of leaf water and chlorophyll content.

Key words：Hami melon; Chlorophyll; Water content; LAB; Model regression

Received: 2022-08-09 Accepted: 2023-11-28

ZTFLH:

S126

Corresponding Authors: GUO Jun-xian E-mail: junxianguo@163.com

Cite this article:

LI Long-jie,SHI Yong,GUO Jun-xian. Inversion of Chlorophyll and Water Content of Hami Melon Leaves Based on Spectrophotometry on Study[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2296-2302.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)08-2296-07 OR https://www.gpxygpfx.com/EN/Y2024/V44/I08/2296

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