Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters

• Published in: Plants (Basel) Vol. 13; no. 10; p. 1314
• Main Authors: Shi, Hongzhao, Lu, Xingxing, Sun, Tao, Liu, Xiaochi, Huang, Xiangyang, Tang, Zijun, Li, Zhijun, Xiang, Youzhen, Zhang, Fucang, Zhen, Jingbo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-05-2024
• Subjects: Accuracy; Agricultural practices; Agriculture; Algorithms; Analysis; Artificial neural networks; Back propagation networks; Chlorophyll; chlorophyll content; Correlation coefficient; Correlation coefficients; Crop growth; Crops; Data acquisition; Ethanol; Fertilizers; Food supply; hyperspectral; Leaf area; Leaves; Machine learning; Methods; Neural networks; Nitrogen; Parameters; Physiology; Potassium; potato; Potatoes; Precision farming; Prediction models; Remote sensing; Support vector machines; Vegetable industry; Vegetables; Vegetation; Vegetation index; China; chlorophyll content; machine learning; potato; hyperspectral
• ISSN: 2223-7747; 2223-7747
• DOI: 10.3390/plants13101314

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCC
) and the chlorophyll content per unit of fresh weight (LCC
) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the "trilateral" parameters with the best correlation between potato LCC
and LCC
, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCC
and LCC
were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the "trilateral" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCC
and LCC
. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCC
and LCC
estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCC
to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.