Affiliation:
1. Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China
2. Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
Abstract
Crop growth monitoring plays an important role in estimating the scale of food production and providing a decision-making basis for agricultural policies. Moreover, it can allow understanding of the growth status of crops, seedling conditions, and changes in a timely manner, overcoming the disadvantages of traditional monitoring methods such as low efficiency and inaccuracy. In order to realize rapid and non-destructive monitoring of winter wheat growth status, this study introduced an equal weight method and coefficient of variation method to construct new comprehensive growth indicators based on drone images and measured data obtained from field experiments. The accuracy of the indicators in evaluating the growth of winter wheat can be judged by the construction, and the effects of different machine learning methods on the construction of indicators can be compared. Correlation analysis and variable screening were carried out on the constructed comprehensive growth indicators and the characteristic parameters extracted by the drone, and the comprehensive growth index estimation model was constructed using the selected parameter combination. Among them, when estimating the comprehensive growth index (CGIavg), the optimal model at the jointing stage is the support vector regression (SVR) model: R2 is 0.77, RMSE is 0.095; at the booting stage, the optimal model is the Gaussian process regression (GPR) model: R2 is 0.71, RMSE is 0.098; at the flowering stage, the optimal model is the SVR model: R2 is 0.78, RMSE is 0.087. When estimating the comprehensive growth index based on the coefficient of variation method (CGIcv), the optimal model at the jointing stage is the multi-scale retinex (MSR) model: R2 is 0.73, RMSE is 0.084; at the booting stage, the optimal model is the GPR model: R2 is 0.74, RMSE is 0.092; at the flowering stage, the optimal model is the SVR model, R2 is 0.78: RMSE is 0.085. The conclusion shows that the method of constructing the comprehensive growth index is superior to the function of a single parameter to some extent, providing a new way for wheat growth monitoring and process management.
Funder
Key Research and Development Program (Modern Agriculture) of Jiangsu Province
National Natural Science Foundation of China
National Key Research and Development Program of China
Subject
Agronomy and Crop Science
Reference24 articles.
1. Chen, K., Tian, G., Tian, Z., Ren, Y., and Liang, W. (2022). Evaluation of the Coupled and Coordinated Relationship between Agricultural Modernization and Regional Economic Development under the Rural Revitalization Strategy. Agronomy, 12.
2. Khan, N., Ray, R.L., Kassem, H.S., and Zhang, S. (2022). Mobile Internet Technology Adoption for Sustainable Agriculture: Evidence from Wheat Farmers. Appl. Sci., 12.
3. Feng, H., Tao, H., Li, Z., Yang, G., and Zhao, C. (2022). Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter Wheat Growth. Remote Sens., 14.
4. Study on Remote Sensing Monitoring of Crop Growth Based on Comprehensive Index: A Case Study of the Songnen Plain;Zhao;Geogr. Geo-Inf. Sci.,2022
5. Remote sensing monitoring of winter wheat growth in Hebei province based on comprehensive indicators;Zhou;J. Hengyang Norm. Univ.,2021