Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton ( Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms

• Published in: Plants (Basel) 2023-01, Vol.12 (3), p.455
• Main Authors: Han, Peng, Zhai, Yaping, Liu, Wenhong, Lin, Hairong, An, Qiushuang, Zhang, Qi, Ding, Shugen, Zhang, Dawei, Pan, Zhenyuan, Nie, Xinhui
• Format: Article
• Language: English
• Subjects: absorbed photosynthetically active radiation; Agricultural production; Algorithms; Artificial intelligence; Chlorophyll; Correlation analysis; Cotton; Crops; Data mining; Datasets; Environmental aspects; Farms; Fertilizer application; Learning algorithms; Leaves; Machine learning; machine learning algorithms models; Measurement; Monitoring; Multispectral photography; net photosynthetic rate; Nitrogen; Nitrogen fertilizers; nitrogen monitoring; Nutrition; Nutritional status; Photosynthesis; Photosynthetically active radiation; Radiation; Reflectance; Remote sensing; remote sensing monitoring; Spectral reflectance; Surgery; Wavelet transforms
• ISSN: 2223-7747; 2223-7747
• DOI: 10.3390/plants12030455

Hyperspectral technology has enabled rapid and efficient nitrogen monitoring in crops. However, most approaches involve direct monitoring of nitrogen content or physiological and biochemical indicators directly related to nitrogen, which cannot reflect the overall plant nutritional status. Two important photosynthetic traits, the fraction of absorbed photosynthetically active radiation (FAPAR) and the net photosynthetic rate (Pn), were previously shown to respond positively to nitrogen changes. Here, Pn and FAPAR were used for correlation analysis with hyperspectral data to establish a relationship between nitrogen status and hyperspectral characteristics through photosynthetic traits. Using principal component and band autocorrelation analyses of the original spectral reflectance, two band positions (350-450 and 600-750 nm) sensitive to nitrogen changes were obtained. The performances of four machine learning algorithm models based on six forms of hyperspectral transformations showed that the light gradient boosting machine (LightGBM) model based on the hyperspectral first derivative could better invert the Pn of function-leaves in cotton, and the random forest (RF) model based on hyperspectral first derivative could better invert the FAPAR of the cotton canopy. These results provide advanced metrics for non-destructive tracking of cotton nitrogen status, which can be used to diagnose nitrogen nutrition and cotton growth status in large farms.