Semi-supervised cooperative regression model for small sample estimation of citrus leaf nitrogen content with UAV images

ABSTRACT

Nitrogen is an essential nutrient element for the growth of citrus tree. The accurate estimation of leaf nitrogen content (LNC) is important to guarantee fruit quality and yield. The unmanned aerial vehicle (UAV) remote sensing has shown great potential for monitoring the LNC of crops. However, the number of training samples is always limited due to the high cost and time consumption of acquiring LNC samples. Obtaining satisfactory results is difficult for most machine-learning models with insufficient samples. Thus, a semi-supervised regression model is designed in this paper to improve the performance of estimating citrus LNC from UAV images under the condition of small samples. First, the local binary pattern (LBP) operator is employed to fully extract textural features in UAV images. Second, semi-supervised cooperative regression models based on ridge regression (Ridge), support vector regression (SVR), and random forest (RF) are constructed by combining spectral and textural features. Finally, the best learning model is used to realize the accurate limited sample LNC estimation from the available training samples. The experiments confirm that the semi-supervised cooperative regression model has better and promising results than other machine-learning methods under the condition of small samples. The RF-based cooperative regression model (CoRF) performs best among other models, with a determination coefficient (R²) of 0.716 and a root mean square error (RMSE) of 0.620 g·kg⁻¹. The CoRF model also achieves superior performance when combining LBP textural features. The results estimated by the semi-supervised cooperative regression model is capable of providing instructions for the production and management of citrus orchard.

KEYWORDS:

• Citrus
• leaf nitrogen content
• UAV remote sensing
• semi-supervised regression
• machine learning

Highlights

• A semi-supervised cooperative regression model is designed to accurately estimate the LNC of citrus orchard under the condition of small samples.

• The local binary pattern (LBP) operator is employed to fully extract textural features in UAV images.

• Combining the spectral and LBP textural features successfully to achieve superior performances of LNC estimation.

Acknowledgements

We sincerely thank the technical staff of orchard for their support and help in data collection; and other members of the Plateau characteristic agriculture project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Tong Wu conceived and designed the experiment; Yong Li revised and improved the manuscript; Ying Ge provided supervision on implementation of the research; Shunzhong Xi, Tingxuan Zhang, and Wusiqin Zhang analysed the data.

Data availability statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 41977394); the Major Project of Science and Technology of Yunnan Province (Grant No. 202002AE090010).