Using UAV Multispectral Imagery to Predict Leaf SPAD Dynamics During Maize Growth Under Different Plant Densities

Abstract

Chlorophyll content represents a key growth indicator for maize. The traditional SPAD method, though easy to operate, is inefficient, destructive, and unsuitable for high throughput field monitoring. Unmanned Aerial Vehicle (UAV) remote sensing technology is highly efficient and detects abundant indicators, enabling large-scale SPAD measurement. In this study, 18 vegetation indices and 8 texture features were selected as the indicator system by combining prior knowledge and experimental analysis. In a two-year maize density experiment, multispectral images were collected in the full growth period. The correlations between SPAD values, multispectral indices and texture features were analyzed using Pearson correlation coefficients. Then the detection accuracies of three algorithms i.e. Random Forest (RF), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), were compared under this indicator system. Compared with models constructed using single vegetation indices or single texture features, the estimation accuracy of the indicator system at the jointing stage was improved by 0.13 and 0.22, respectively. The results showed that SVR achieved the highest estimation accuracy among the three algorithms, with determination coefficients (R²) of 0.73, 0.77and 0.70 at the jointing, silking, and grain-filling stages, respectively. This study established a non-destructive monitoring framework for chlorophyll content during entire maize growth period based on UAV data.

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