Hyperspectral characteristics and quantitative analysis of leaf chlorophyll by reflectance spectroscopy based on a genetic algorithm in combination with partial least squares regression

Chen, Xiaowan; Dong, Zhenyu; Liu, Jinbao; Wang, Huanyuan; Zhang, Yang; Chen, Tianqing; Du, Ying; Shao, Li; Xie, Jiancang

doi:10.1016/j.saa.2020.118786

Cited by 37 publications

(21 citation statements)

References 38 publications

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“…There are two main approaches for the research on the detection of leaf Chl based on hyperspectral technology: building models based on direct spectral data or vegetation index. For the former, the model is established based on the full spectra or a few bands with characteristic spectral responses [7,[9][10][11]. For the latter, the model is constructed based on multispectral vegetation indices established according to the characteristic bands [12,13].…”

Section: Introductionmentioning

confidence: 99%

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

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Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

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Section: Introductionmentioning

confidence: 99%

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

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“…Chlorophyll content became higher along with higher nitrogen strength, and that led to a lower reflectance, due to the strong absorption of chlorophyll-a and b under blue light (410-470 nm) and red light (644.8-670 nm), respectively (X. Chen et al, 2020;Navarro-Cerrillo et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral wavelength selection for estimating chlorophyll content of muskmelon leaves

Sonobe

Sugimoto

Kondo

et al. 2021

European Journal of Remote Sensing

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Quantifying chlorophyll content, an effective indicator of disease as well as nutritional and environmental stresses on plants, may enable optimal fertilization while managing crops. Hyperspectral remote-sensing is commonly used to estimate chlorophyll content. In this context, the process of variable selection is crucial since it is necessary to identify variables relevant to chlorophyll and eliminate redundant variables. In this study, 14 wavelength selection methods based on partial least squares (PLS; namely, backward variable elimination, backward and forward interval-PLS, competitive adaptive reweighted sampling, genetic algorithm, iterative predictive weighting, loading-weights, PLS with Martens' uncertainty test, regression coefficient, regularized elimination procedure, sparse-PLS, sub-window permutation analysis, uninformative variable elimination and variable importance in projection) were combined with one of five machine learning algorithms (Cubist, deep belief nets, random forests, stochastic gradient boosting and support vector machine) and then evaluated. According to the ratio of performance to deviation (RPD), the best combination of variable selection method and machine learning algorithm was regularized elimination procedure and Cubist achieving an RPD of 1.76 and an RMSE of 2.42 μg cm −2 .

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“…Specifically, samples in the original dataset with the largest distance from the others and as far as possible from the candidate subset are selected to the candidate subset until the division ratio is reached ( Morais et al, 2019 ). For the same dataset, the sample partition results obtained by KS algorithm are the same each time ( Chen et al, 2020 ). Besides, limited by the size of the dataset, the division ratio of the training set and test set was 4:1.…”

Section: Methodsmentioning

confidence: 99%

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Tao

Bai

et al. 2022

Front. Plant Sci.

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Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other’s advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.

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Hyperspectral characteristics and quantitative analysis of leaf chlorophyll by reflectance spectroscopy based on a genetic algorithm in combination with partial least squares regression

Cited by 37 publications

References 38 publications

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Hyperspectral wavelength selection for estimating chlorophyll content of muskmelon leaves

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

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