Combined use of spectral resampling and machine learning algorithms to estimate soybean leaf chlorophyll

Gao, Chunrui; Li, Hao; Wang, Jiachen; Zhang, Xin; Huang, Kunming; Song, Xiaoyan; Yang, Wude; Feng, Meichen; Xiao, Lujie; Zhao, Yu; Shafiq, Fahad; Wang, Chao; Qiao, Xingxing; Li, Fangzhou

doi:10.1016/j.compag.2024.108675

Cited by 6 publications

(3 citation statements)

References 55 publications

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“…This finding contrasts with the conclusion of Yuan et al regarding the higher expressive capability of MCARI for predicting SPAD based on multispectral images of Hopea hainanensis [59]. It is known from previous studies on the correlation between SPAD and vegetation indices that the interpretation of SPAD varies among different plant species [60][61][62][63]. The spectral characteristics of plants are influenced by their unique structures, morphologies, and physiological features, and the biochemical composition and structure differ among different plant species, leading to varying absorption and reflection capacities of leaves in each spectral band.…”

Section: Discussioncontrasting

confidence: 95%

“…The results showed that the BPNN model, which uses backpropagation to iteratively adjust model weights, had the best predictive ability in CCS estimation. This conclusion is consistent with previous research indicating that the BPNN model performs better in constructing SPAD inversion models [60,68]. Compared with other machine learning algorithms, BPNN takes the backpropagation algorithm as the core, and adjusts the BPNN model weights and biases by continuously repeating the forward and backward propagation process of data and errors, so as to fit the complex mapping relationship between the input and output data from a large number of training data, and iteratively obtains higher network performance.…”

Section: Discussionsupporting

confidence: 90%

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Cunninghamia lanceolata Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

Zhang,

Su,

Liu

et al. 2024

Forests

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This study aimed to streamline the determination of chlorophyll content in Cunninghamia lanceolate while achieving precise measurements of canopy chlorophyll content. Relative chlorophyll content (SPAD) in the Cunninghamia lanceolate canopy were assessed in the study area using the SPAD-502 portable chlorophyll meter, alongside spectral data collected via onboard multispectral imaging. And based on the unmanned aerial vehicle (UAV) multispectral collection of spectral values in the study area, 21 vegetation indices with significant correlation with Cunninghamia lanceolata canopy SPAD (CCS) were constructed as independent variables of the model’s various regression techniques, including partial least squares regression (PLSR), random forests (RF), and backpropagation neural networks (BPNN), which were employed to develop a SPAD inversion model. The BPNN-based model emerged as the best choice, exhibiting test dataset coefficients of determination (R2) at 0.812, root mean square error (RSME) at 2.607, and relative percent difference (RPD) at 1.942. While the model demonstrated consistent accuracy across different slope locations, generalization was lower for varying slope directions. By creating separate models for different slope directions, R2 went up to about 0.8, showcasing favorable terrain applicability. Therefore, constructing inverse models with different slope directions samples separately can estimate CCS more accurately.

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Section: Discussioncontrasting

confidence: 95%

Section: Discussionsupporting

confidence: 90%

Cunninghamia lanceolata Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

Zhang,

Su,

Liu

et al. 2024

Forests

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“…Compared to models based on specific spectral bands, spectral indices have the advantage of enhancing spectral features according to the research objectives, thereby amplifying the correlation between spectral reflectance and crop biochemical information and reducing the sensitivity of single bands to other influencing factors [14][15][16]. Extensive research has focused on developing spectral indices models to estimate the LCC in crop leaves, showing promising application potential [2,[17][18][19][20][21]. These studies indicate that the spectral information in hyperspectral images performs well in detecting the internal characteristics of crops.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images

Song,

Liu,

et al. 2024

Remote Sensing

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Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy.

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Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV

Li,

Yan,

et al. 2024

J Sci Food Agric

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BackgroundLeaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV‐RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI.ResultsThe results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = −0.783). Furthermore, the visible atmospheric resistance index, the green–red vegetation index, the modified green–red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables.ConclusionThis study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer‐level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry.

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Combined use of spectral resampling and machine learning algorithms to estimate soybean leaf chlorophyll

Cited by 6 publications

References 55 publications

Cunninghamia lanceolata Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

Cunninghamia lanceolata Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images

Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV

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