Monitoring leaf nitrogen content in rice based on information fusion of multi-sensor imagery from UAV

Abstract

Timely and accurately monitoring leaf nitrogen content (LNC) is essential for evaluating crop nutrition status. Currently, Unmanned Aerial Vehicles (UAV) imagery is becoming a potentially powerful tool of assessing crop nitrogen status in fields, but most of crop nitrogen estimates based on UAV remote sensing usually use single type imagery, the fusion information from different types of imagery has rarely been considered. In this study, the fusion images were firstly made from the simultaneously acquired digital RGB and multi-spectral images from UAV at three growth stages of rice, and then couple the selecting methods of optimal features with machine learning algorithms for the fusion images to estimate LNC in rice. Results showed that the combination with different types of features could improve the models’ accuracy effectively, the combined inputs with bands, vegetation indices (VIs) and Grey Level Co-occurrence Matrices (GLCMs) have the better performance. The LNC estimation of using fusion images was improved more obviously than multispectral those, and there was the best estimation at jointing stage based on Lasso Regression (LR), with R2 of 0.66 and RMSE of 11.96%. Gaussian Process Regression (GPR) algorithm used in combination with one feature-screening method of Minimum Redundancy Maximum Correlation (mRMR) for the fusion images, showed the better improvement to LNC estimation, with R2 of 0.68 and RMSE of 11.45%. It indicates that the information fusion from UAV multi-sensor imagery can significantly improve crop LNC estimates and the combination with multiple types of features also has a great potential for evaluating LNC in crops.

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

“…VIs derived from band operations moderately correlate with LNC because the spectral bands extracted from multispectral images responded differently to LNC (Figure 5). In contrast to previous research [14,53], our study's correlation between NDVI and LNC is not optimal. In addition, the results indicate that, like NDVI, GCVI, which represents the amount of chlorophyll and nitrogen in crops, has the lowest correlation with LNC across multiple growth stages.…”

“…Although ground-based hyperspectral platforms may collect high-precision remote sensing data [13], their limited sampling points and high costs make them unsuitable for large area applications. UAVs are commonly used for crop LNC monitoring due to their flexibility, convenience, and low cost compared to satellite or ground-based hyperspectral systems [6,14].…”

“…Although combining texture metrics with VIs improved the estimation accuracy of crop LNC [14], most studies have relied on pre-existing texture metrics and VIs constructed from specific spectral bands and combination formulas [26], ignoring the potential of fea-ture combination applications. VIs such as Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI), which are constructed using simple formulas, offer advantages like reducing the impact of background noise and effectively mitigating the interference from solar zenith angle, thereby enhancing their responsiveness to vegetation features [27,28].…”

Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images

“…The soybean samples were then put into paper bags, numbered and placed in the oven. The samples were first dried at 105 • C for 30 min (called blanching), and then dried at 80 • C for approximately 72 h until the weight no longer changed [49]. The dry matter of the soybean samples was weighed and recorded.…”