Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

As a vital pigment for photosynthesis in rice, chlorophyll content is closely correlated with growth status and photosynthetic capacity. The estimation of chlorophyll content allows for the monitoring of rice growth and facilitates precise management in the field, such as the application of fertilizers and irrigation. The advancement of hyperspectral remote sensing technology has made it possible to estimate chlorophyll content non-destructively, quickly, and effectively, offering technical support for managing and monitoring rice growth across wide areas. Although hyperspectral data have a fine spectral resolution, they also cause a large amount of information redundancy and noise. This study focuses on the issues of unstable input variables and the estimation model’s poor applicability to various periods when predicting rice chlorophyll content. By introducing the theory of harmonic analysis and the time-frequency conversion method, a deep neural network (DNN) model framework based on wavelet packet transform-first order differential-harmonic analysis (WPT-FD-HA) was proposed, which avoids the uncertainty in the calculation of spectral parameters. The accuracy of estimating rice chlorophyll content based on WPT-FD and WPT-FD-HA variables was compared at seedling, tillering, jointing, heading, grain filling, milk, and complete periods to evaluate the validity and generalizability of the suggested framework. The results demonstrated that all of the WPT-FD-HA models’ single-period validation accuracy had coefficients of determination (R2) values greater than 0.9 and RMSE values less than 1. The multi-period validation model had a root mean square error (RMSE) of 1.664 and an R2 of 0.971. Even with independent data splitting validation, the multi-period model accuracy can still achieve R2 = 0.95 and RMSE = 1.4. The WPT-FD-HA-based deep learning framework exhibited strong stability. The outcome of this study deserves to be used to monitor rice growth on a broad scale using hyperspectral data.

Spectral-Frequency Conversion Derived from Hyperspectral Data Combined with Deep Learning for Estimating Chlorophyll Content in Rice

Du,

Luo

2024

Coupling Image-Fusion Techniques with Machine Learning to Enhance Dynamic Monitoring of Nitrogen Content in Winter Wheat from UAV Multi-Source

Li,

Su,

et al. 2024

Plant nitrogen concentration (PNC) is a key indicator reflecting the growth and development status of plants. The timely and accurate monitoring of plant PNC is of great significance for the refined management of crop nutrition in the field. The rapidly developing sensor technology provides a powerful means for monitoring crop PNC. Although RGB images have rich spatial information, they lack the spectral information of the red edge and near infrared bands, which are more sensitive to vegetation. Conversely, multispectral images offer superior spectral resolution but typically lag in spatial detail compared to RGB images. Therefore, the purpose of this study is to improve the accuracy and efficiency of crop PNC monitoring by combining the advantages of RGB images and multispectral images through image-fusion technology. This study was based on the booting, heading, and early-filling stages of winter wheat, synchronously acquiring UAV RGB and MS data, using Gram–Schmidt (GS) and principal component (PC) image-fusion methods to generate fused images and evaluate them with multiple image-quality indicators. Subsequently, models for predicting wheat PNC were constructed using machine-selection algorithms such as RF, GPR, and XGB. The results show that the RGB_B1 image contains richer image information and more image details compared to other bands. The GS image-fusion method is superior to the PC method, and the performance of fusing high-resolution RGB_B1 band images with MS images using the GS method is optimal. After image fusion, the correlation between vegetation indices (VIs) and wheat PNC has been enhanced to varying degrees in different growth periods, significantly enhancing the response ability of spectral information to wheat PNC. To comprehensively assess the potential of fused images in estimating wheat PNC, this study fully compared the performance of PNC models before and after fusion using machine learning algorithms such as Random Forest (RF), Gaussian Process Regression (GPR), and eXtreme Gradient Boosting (XGB). The results show that the model established by the fusion image has high stability and accuracy in a single growth period, multiple growth periods, different varieties, and different nitrogen treatments, making it significantly better than the MS image. The most significant enhancements were during the booting to early-filling stages, particularly with the RF algorithm, which achieved an 18.8% increase in R2, a 26.5% increase in RPD, and a 19.7% decrease in RMSE. This study provides an effective technical means for the dynamic monitoring of crop nutritional status and provides strong technical support for the precise management of crop nutrition.

Winter Wheat SPAD Prediction Based on Multiple Preprocessing, Sequential Module Fusion, and Feature Mining Methods

Nian,

Su,

Yue

et al. 2024

Chlorophyll is a crucial indicator for monitoring crop growth and assessing nutritional status. Hyperspectral remote sensing plays an important role in precision agriculture, offering a non-destructive approach to predicting leaf chlorophyll. However, crop canopy spectra often face background noise and data redundancy challenges. To tackle these issues, this study develops an integrated processing strategy incorporating multiple preprocessing techniques, sequential module fusion, and feature mining methods. Initially, the original spectrum (OS) from 2021, 2022, and the fusion year underwent preprocessing through Fast Fourier Transform (FFT) smoothing, multiple scattering correction (MSC), the first derivative (FD), and the second derivative (SD). Secondly, feature mining was conducted using Competitive Adaptive Reweighted Sampling (CARS), Iterative Retention of Information Variables (IRIV), and Principal Component Analysis (PCA) based on the optimal preprocessing order module fusion data. Finally, Partial Least Squares Regression (PLSR) was used to construct a prediction model for winter wheat SPAD to compare the prediction effects in different years and growth stages. The findings show that the preprocessing sequential module fusion of FFT-MSC (firstly pre-processing using FFT, and secondly secondary processing of FFT spectral data using MSC) effectively reduced issues such as noisy signals and baseline drift. The FFT-MSC-IRIV-PLSR model (based on the combined FFT-MSC preprocessed spectral data, feature screening using IRIV, and then combining with PLSR to construct a prediction model) predicts SPAD with the highest overall accuracy, with an R2 of 0.79–0.89, RMSE of 4.51–5.61, and MAE of 4.01–4.43. The model performed best in 2022, with an R2 of 0.84–0.89 and RMSE of 4.51–6.74. The best prediction during different growth stages occurred in the early filling stage, with an R2 of 0.75 and RMSE of 0.58. On the basis of this research, future work will focus on optimizing the data processing process and incorporating richer environmental data, so as to further enhance the predictive capability and applicability of the model.