Estimating Relative Chlorophyll Content in Rice Leaves Using Unmanned Aerial Vehicle Multi-Spectral Images and Spectral–Textural Analysis

Abstract

Leaf chlorophyll content is crucial for monitoring plant growth and photosynthetic capacity. The Soil and Plant Analysis Development (SPAD) values are widely utilized as a relative chlorophyll content index in ecological agricultural surveys and vegetation remote sensing applications. Multi-spectral cameras are a cost-effective alternative to hyperspectral cameras for agricultural monitoring. However, the limited spectral bands of multi-spectral cameras restrict the number of vegetation indices (VIs) that can be synthesized, necessitating the exploration of other options for SPAD estimation. This study evaluated the impact of using texture indices (TIs) and VIs, alone or in combination, for estimating rice SPAD values during different growth stages. A multi-spectral camera was attached to an unmanned aerial vehicle (UAV) to collect remote sensing images of the rice canopy, with manual SPAD measurements taken immediately after each flight. Random forest (RF) was employed as the regression method, and evaluation metrics included coefficient of determination (R2) and root mean squared error (RMSE). The study found that textural information extracted from multi-spectral images could effectively assess the SPAD values of rice. Constructing TIs by combining two textural feature values (TFVs) further improved the correlation of textural information with SPAD. Utilizing both VIs and TIs demonstrated superior performance throughout all growth stages. The model works well in estimating the rice SPAD in an independent experiment in 2022, proving that the model has good generalization ability. The results suggest that incorporating both spectral and textural data can enhance the precision of rice SPAD estimation throughout all growth stages, compared to using spectral data alone. These findings are of significant importance in the fields of precision agriculture and environmental protection.

Keywords

• SPAD
• UAV multi-spectral images
• vegetation indices
• texture indices
• rice