Winter wheat chlorophyll content retrieval based on machine learning using in situ hyperspectral data

Wang, Tianli; Gao, Maofang; Cao, Chunling; You, Jiong; Zhang, Xiwang; Shen, Lanzhi

doi:10.1016/j.compag.2022.106728

Cited by 29 publications

(15 citation statements)

References 32 publications

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“…PCA usually serves as an important tool for feature dimensionality reduction, which can preserve as much effective parts of the data as possible. At the same time, VIs have great potential to expand the difference between observation and background, but cannot retain sufficient information from the original data [39]. If VIs with well correlation to chlorophyll content and PCA results are combined, canopy phenotype information of crops can be characterized in great detail, and sufficient information from the original data can be retain as much as possible.…”

Section: Characteristic Parameter Constructionmentioning

confidence: 99%

A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm

Hu,

Yang,

Shi

et al. 2024

Preprint

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Using UAV-based multispectral images for quickly and accurately monitoring chlorophyll content is critical for field management and yield estimation. However, the lower model accuracy and the poor robustness of the estimation models are still preventing the widespread application of UAV-based multispectral images. We carried out two field trials at various experimental sites to further enhance the precision and applicability of the model used to estimate the chlorophyll content of potato plants. Firstly, the texture features and vegetation indices derived from multispectral images were screened using the Pearson correlation coefficient method, and Normalized difference red edge (NDRE) performed the best over the two growth periods. Secondly, principal component analysis (PCA) was applied to recombine five bands of multispectral images, and third PCA results (PCA3) was selected to combined with NDRE according to the construction principle of NDRE, and the newly constructed parameter was named improved NDRE (INDRE). Finally, INDRE was used to establish a chlorophyll content estimation model of potato plants, and compared with some traditional parameters. The results demonstrated that the INDRE had the maximum accuracy (R2 = 0.7865, RMSE = 2.1378), and corresponding R2 increased by 0.1481 and RMSE decreased by 1.2994 than NDRE. Additionally, the model was validated using independent data from Experiment 2, and INDRE considerably increased estimation accuracy compared to other factors. In conclusions, the INDRE suggested in this study significantly enhances the accuracy and applicability of the chlorophyll content inversion model and can serve as an additional reference for fertilization management.

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Section: Characteristic Parameter Constructionmentioning

confidence: 99%

A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm

Hu,

Yang,

Shi

et al. 2024

Preprint

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“…Near-infrared reflectance spectroscopy (NIRS), Fourier transform infrared attenuated total reflectance (FTIR/ATR), and nuclear magnetic resonance (NMR) spectroscopy were employed for sex differentiation in immature date palm leaves [ 19 ]. Hyperspectral techniques have been shown to be capable of characterizing material contents like peroxidase [ 20 ], water [ 21 ], chlorophyll [ 22 ], and lignin [ 23 ] within plants. The spectral absorption bands in the wavelength range of 400–1200 nm are associated with multiple overtones and combinations of the fundamental vibrations of chemical bonds between light atoms [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning

Chen,

Lin,

Sun

et al. 2024

Plants

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Ginkgo biloba L. is a rare dioecious species that is valued for its diverse applications and is cultivated globally. This study aimed to develop a rapid and effective method for determining the sex of a Ginkgo biloba. Green and yellow leaves representing annual growth stages were scanned with a hyperspectral imager, and classification models for RGB images, spectral features, and a fusion of spectral and image features were established. Initially, a ResNet101 model classified the RGB dataset using the proportional scaling–background expansion preprocessing method, achieving an accuracy of 90.27%. Further, machine learning algorithms like support vector machine (SVM), linear discriminant analysis (LDA), and subspace discriminant analysis (SDA) were applied. Optimal results were achieved with SVM and SDA in the green leaf stage and LDA in the yellow leaf stage, with prediction accuracies of 87.35% and 98.85%, respectively. To fully utilize the optimal model, a two-stage Period-Predetermined (PP) method was proposed, and a fusion dataset was built using the spectral and image features. The overall accuracy for the prediction set was as high as 96.30%. This is the first study to establish a standard technique framework for Ginkgo sex classification using hyperspectral imaging, offering an efficient tool for industrial and ecological applications and the potential for classifying other dioecious plants.

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“…Artificial Neural Networks (ANNs) are among the non-linear non-parametric regression models frequently applied to retrieve surface biophysical/biochemical properties such as biomass (see review in ( Ali et al, 2015 )), fractional vegetation cover (fCover) ( Bacour et al, 2006 ), LAI (see review in ( Fang et al, 2019 )), chlorophyll content ( Wang et al, 2022 ), nitrogen content (see review in ( Berger et al, 2020b )), and water content ( Neinavaz et al, 2017 ; Mirzaie et al, 2014 ; Trombetti et al, 2008 ). Verrelst et al (2019) reviewed successful studies applying ANNs to retrieve different vegetation properties.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network

Hajeb

Hamzeh

Alavipanah

et al. 2023

International Journal of Applied Earth Observation and Geoinfor

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Winter wheat chlorophyll content retrieval based on machine learning using in situ hyperspectral data

Cited by 29 publications

References 32 publications

A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm

A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm

Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning

Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network

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