Summer Maize Growth Estimation Based on Near-Surface Multi-Source Data

Abstract

Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulative temperature data for multiple growth stages of summer maize, respectively. Secondly, the effective plant height (canopy height model (CHM)), effective accumulation temperature (growing degree days (GDD)), canopy vegetation index (mainly spectral vegetation index) and canopy hyperspectral features of maize were extracted, and sensitive features were screened by correlation analysis. Then, based on single-source and multi-source data, multiple linear regression (MLR), partial least-squares regression (PLSR) and random forest (RF) regression were used to construct LAI and SPAD inversion models. Finally, the distribution of LAI and SPAD prescription plots was generated and the trend for the two was analyzed. The results were as follows: (1) The correlations between the position of the hyperspectral red edge and the first-order differential value in the red edge with LAI and SPAD were all greater than 0.5. The correlation between the vegetation index, including a red and near-infrared band, with LAI and SPAD was above 0.75. The correlation between crop height and effective accumulated temperature with LAI and SPAD was above 0.7. (2) The inversion models based on multi-source data were more effective than the models made with single-source data. The RF model with multi-source data fusion achieved the highest accuracy of all models. In the testing set, the LAI and SPAD models’ R2 was 0.9315 and 0.7767; the RMSE was 0.4895 and 2.8387. (3) The absolute error between the extraction result of each model prescription map and the measured value was small. The error between the predicted value and the measured value of the LAI prescription map generated by the RF model was less than 0.4895. The difference between the predicted value and the measured value of the SPAD prescription map was less than 2.8387. The LAI and SPAD of summer maize first increased and then decreased with the advancement of the growth period, which was in line with the actual growth conditions. The research results indicate that the proposed method could effectively monitor maize growth parameters and provide a scientific basis for summer maize field management.

Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning

“…The RF model demonstrated the highest degree of accuracy (RMSE = 14.66 cm) compared to other algorithms, highlighting the necessity of fomenting a diversified dataset to enhance the model's robustness and generalizability. This study revealed that both the RF and KNN algorithms yielded precise outcomes for height estimation, which aligns with findings from other scholarly inquiries that harnessed these algorithms to estimate the heights of maize and soybean crops using LiDAR and UAV data [2], maize height estimation via a three-dimensional model [9], estimation of summer maize growth using digital models [53], biomass estimation based on UAV data [54], determination of leaf area index [1], and estimation of bean production using UAV RGB images [8]. Furthermore, in addition to the RF algorithm, the integration of the multilayer perceptron (MLP) shows promise in estimating plant height based on UAV RGB images [7].…”

Regional Monitoring of Leaf ChlorophyII Content of Summer Maize by Integrating Multi-Source Remote Sensing Data

“…Songtao Ban et al found that partial least squares perform better than support vector machines and artificial neural networks for rice LCC monitoring in application in different regions and cultivars, with validation data set R 2 range from 0.76 to 0.81 and RMSE range from 2.24 to 4.00 in different places [49]. Random forest performs the best when multi-source data were introduced in wheat LCC monitoring, with R 2 and RMSE of test data set reach 0.7767 and 2.8387, respectively [47]. In this manuscript, the optimal algorithm and scheme for UAV LCC inversion is the ET model with the combination of 16 features at the 20-pixel scale, the R 2 and RMSE of the test data set reach 0.8872 and 3.0968, respectively, similar to previous research.…”

“…The introduction of multi-source data (ground hyperspectral data, UAV visible-light data, and environment temperature data, etc.) [47] and texture indices can obviously improve simulation accuracy of LCC [48]. But in the comparison of different machine learning algorithms, different researchers may find different optimal models for LCC inversion under different conditions.…”

Monitoring of a Productive Blue-Green Roof Using Low-Cost Sensors

“…To monitor crop growth, various investigations on nondestructive techniques have been investigated, some utilizing the 2D or 3D image-based system [ 24 , 30 , 31 ]. Stereo vision [ 32 ], LiDAR [ 33 ], multispectral [ 34 , 35 , 36 , 37 , 38 ], and hyperspectral [ 37 , 38 , 39 , 40 ] imaging have also been explored by the mentioned research and applications, although their expenses are quite high in terms of instrumentation costs. Depth cameras have also been recently used by studies [ 28 , 41 , 42 , 43 ] in laboratory settings to estimate plant height and measure leaf area, leaf length, or plant biomass.…”