Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features

Abstract

Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.

Maize Production under Drought Stress: Nutrient Supply, Yield Prediction

“…The correct timing of N application improves yield parameters, which increases grain yield [ 29 , 30 ]. However, improper optimisation of the rate and proportion of nitrogen application causes a number of problems in cultivation [ 31 ], slows growth rate, reduces grain yield and increases nitrogen loss, thereby affecting environmental quality [ 32 , 33 , 34 , 35 ].…”

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery

“…Features fusion strategy involve merging different types of features into a more optimal feature set, combining features with different features to construct models, and have the potential to mitigate the impact of spectral saturation effects, enhance model performance and increase the interpretability of prediction models (Zhou et al, 2022;Sun et al, 2023). This explains why the optimal SPAD estimation models in the late growth stage of winter wheat are constructed using features fusion strategy.…”

“…Val increased by 0.092 to 0.202, RMSE Val decreased by 0.076 to 4.916, and RPD Val increased by 0.237 to 0.960. Sun et al (2023) developed maize LAI estimation model using SVM, RF, BPNN, and PLSR algorithms by selecting five spectral features and three texture indices through correlation analysis. The results showed that the SVM algorithm with multi-variable fusion achieved the highest accuracy.…”

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

“…(celery) and Spinacia oleracea Linn. (spinach) and N. tabacum (tobacco) can be monitored using the sensors for high product yield ( Minervini et al., 2015 ; Jahnke et al., 2016 ; Minervini et al., 2017 ; Ubbens and Stavness, 2017 ; Zheng et al., 2019 ; Maimaitijiang et al., 2020 ; Fu et al., 2021 ; Sangjan et al., 2021 ; Sarkar et al., 2021 ; Yang et al., 2021a ; Banerjee et al., 2022 ; Bai et al., 2023a ; Bai et al., 2023b ; Sun et al., 2023 ). A detailed list of automation and AI-based tools used in plant monitoring is listed in Table 2 .…”