无人机影像光谱和纹理融合信息估算马铃薯叶片叶绿素含量.

Chlorophyll is an important pigment for crop light energy utilization, which directly affects the process of energy material conversion and transmission. The change of chlorophyll content directly reflects the ability of photosynthesis and the nutritional status of crop growth. When the traditional unmanned aerial vehicle (UAV) remote sensing was used for crop nutrition monitoring, most of them started from the spectral vegetation indices, ignoring the characteristics of the image itself. In this study, we estimated potato leaf chlorophyll content from a comprehensive index formed by the fusion of multi-spectral vegetation indices, texture features and comprehensive indicators of data fusion. The effect of comprehensive index model on estimating potato leaf chlorophyll content was explored. First, we used the UAV multi-spectral images during the whole potato growth period in 2018 in Xiaotangshan, Changping, and Beijing. The multi-spectral vegetation index, texture characteristics and other variables were first extracted from UVA images, then their correlation relationships with leaf chlorophyll content were analyzed. The optimal image variables were screened out, and the whole subset analysis was based on adjusted determination coefficient and 10-fold cross-validation was used to estimate the leaf chlorophyll content of potato. Finally, the vegetation index and texture features were reconstructed by principal component fusion to establish a new comprehensive index for chlorophyll content estimation. It was found that the leaf chlorophyll content estimation model based on comprehensive index was better than that based on multi-spectral vegetation indices and texture features. The main reason was that the comprehensive index contained both spectral information and image texture information. Multispectral information and model accuracy had also been significantly improved. In the bud period, compared with the vegetation indices based model and the texture feature based model, the determination coefficient (R2) of the comprehensive index model increased 0.104 and 0.136, while the normalized root mean squared error (NRMSE) reduced 1.3% and 1.6%. During the tuber formation period, the determination coefficient of comprehensive index model was increased 0.04.and 0.101, while the NRMSE was decreased 0.5% and 1.2%, compared with the other 2 models. In the tuber growth period, the determination coefficient of comprehensive index model increased 0.075 and 0.111, and the NRMSE decreased 0.9% and 1.3% compared with the vegetation index model and the texture feature model. During the starch accumulation period, the R2 of comprehensive index model increased 0.017 and 0.046, and the NRMSE decreased 0.2% and 0.6%, compared with vegetation index model and texture feature model. In the maturity period, the determination coefficient of comprehensive index model increased 0.092 and 0.057, and the NRMSE decreased 2.4% and 1.5%.Therefore, the effect of comprehensive index estimation model was the best followed by multi-spectral vegetation indices model and the texture feature model was the worst. The starch accumulation period was the best growth period for estimating chlorophyll content by multispectral vegetation index and texture characteristics, while bud period was the best growth period for estimating chlorophyll content by comprehensive index. Estimating potato chlorophyll content based on multi-spectral image from UAV platform can provide a feasible method for potato growth nutrition monitoring. It realizes low-cost, fast and high-throughput monitoring of potato growth and nutrition information, as well as provides guarantee for fine management of farmland irrigation, variable fertilization and so on. [ABSTRACT FROM AUTHOR]