Monitoring canopy SPAD based on UAV and multispectral imaging over fruit tree growth stages and species - Huang, Yirui; Li, Dongming; Liu, Xuan; Ren, Zhenhui (Frontiers in Plant Science, 2024)
Chlorophyll monitoring is an important topic in phenotypic research. For fruit trees, chlorophyll content can reflect the real-time photosynthetic capacity, which is a great reference for the nutrient status assessment. Traditional in situ estimation methods are labor-and time-consuming. Remote sensing spectral imagery has been widely applied in agricultural research. This study aims to explore a transferable model to estimate canopy SPAD across growth stages and tree species. Unmanned aerial vehicle (UAV) system was applied for multispectral images acquisition. Results showed that univariate model yielded with Green Normalized Difference Vegetation Index (GNDVI) gave valuable prediction results, providing a simple and effective method for chlorophyll monitoring for single species. Reflection features (RF) and texture features (TF) were extracted for multivariate modeling. Gaussian Process Regression (GPR) models yielded better performance for mixed species research than other algorithms models, and the R 2 of the RF+TF+GPR model were about 0.7 both single and mixed species. In addition, this method can also be used to predict canopy SPAD over various growth stages, especially in the third and fourth stages with the R 2 higher than 0.6. This paper highlights the importance of using RF+TF for canopy features expression, and deep connections exploration between canopy features with GPR algorithm. This research provides a universal model for canopy SPAD inversion which can promote growth status monitoring and management of fruit trees.