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

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