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Monitoring and diagnosis of potassium nutrition in Ipomoea batatas leaves based on spectral reflectivity
LU Yan-jun, WANG Xu-wei, HU Ji-jie, CHEN Shao-jie, CHEN Yu, LYU Zun-fu
HUBEI AGRICULTURAL SCIENCES
2024, 63 (8):
257-261.
DOI: 10.14088/j.cnki.issn0439-8114.2024.08.043
Two Ipomoea batatas varieties, Shangshu 19 and Xinxiang, were used as experimental materials. By setting different gradient potassium treatments to determine the spectral reflectance of leaves, Ipomoea batatas leaves potassium content and potassium nutrient index prediction models were constructed based on the ratio vegetation index （RVI）. The results showed that the linear model constructed by RVI and potassium content in leaves showed that RVI （R1 598 nm, R1 771 nm） had a high prediction accuracy for potassium content in Ipomoea batatas leaves,the regression equation was y=58.601 0x-58.446（R2=0.741 4, RMSE=0.83）,using validation data to test the linear model, the model showed good predictive ability for potassium content in Ipomoea batatas leaves under different potassium fertilizer levels （R2=0.732 4, RMSE=0.85）;the linear model constructed by RVI and potassium nutrition index indicated that RVI （R700 nm, R1 385 nm） had a high prediction accuracy for the potassium nutrition index of Ipomoea batatas leaves,the regression equation was y=6.032 9x-0.833 （R2=0.768 8, RMSE=0.15）,using validation data to test the linear model, the model showed good predictive ability for the potassium nutrient index of Ipomoea batatas leaves under different potassium fertilizer levels （R2=0.639 5,RMSE=0.20）;the use of RVI could effectively monitor and diagnose potassium nutrition in Ipomoea batatas.
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