Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices

• Published in: Applied sciences Vol. 14; no. 10; p. 4245
• Main Authors: Wu, Renhong, Fan, Yuqing, Zhang, Liuya, Yuan, Debao, Gao, Guitang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Agricultural production; Algorithms; Back propagation; Crop yields; Harvesting; International economic relations; Methods; multiple regression; neural network; Neural networks; Physiology; principal component regression; Principal components analysis; random forest; Regression analysis; Remote sensing; Software; Statistical analysis; Variables; Vegetation; vegetation index; Wheat; Wheat industry; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14104245

Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, agricultural producers can quickly obtain information about wheat yield, manage wheat fields more scientifically and accurately, and ensure national food security. Taking the Xinxiang Experimental Base of the Crop Science Research Institute, Chinese Academy of Agricultural Sciences as an example, hyperspectral data for the critical growth stages of wheat were pre-processed. A total of 27 vegetation indices were calculated from the experimental plots. These indices were then subjected to correlation analysis with measured wheat yield. Vegetation indices with Pearson correlation coefficients greater than 0.5 were selected. Five methods, including multiple linear regression, stepwise regression, principal component regression, neural networks and random forests, were used to construct wheat yield estimation models. Among the methods used, multiple linear regression, stepwise regression and the models developed using principal component analysis showed a lower modelling accuracy and validation precision. However, the neural network and random forest methods both achieved a modelling accuracy R2 greater than 0.6, with validation accuracy R2 values of 0.729 and 0.946, respectively. In addition, the random forest method had a lower cross-validation RMSE value, with values of 869.8 kg/hm−2, indicating a higher model accuracy. In summary, the random forest method provided the optimal estimation for wheat yield, enabling the timely and accurate pre-harvest wheat yield prediction, which has significant value for precision agriculture management and decision making.