Deep chemometrics for nondestructive photosynthetic pigments prediction using leaf reflectance spectra

Prilianti, Kestrilia Rega; Setiyono, Edi; Kelana, Oesman Hendra; Brotosudarmo, Tatas Hardo Panintingjati

doi:10.1016/j.inpa.2020.02.001

Cited by 7 publications

(8 citation statements)

References 15 publications

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“…This phenomenon exhibited that complex CNN architectures are unsuitable for the Chl detection model transfer tasks of cotton leaves between different cultivars. A similar phenomenon that which highly complex architectures had poor performance in regression tasks also occurred in the previous study [51].…”

Section: Comparison Between the Effect Of Different Cnnsupporting

confidence: 85%

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: Comparison Between the Effect Of Different Cnnsupporting

confidence: 85%

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

et al. 2022

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“…Ng et al [ 6 ] have also used spectral information from combined sources like Vis–NIR and MIR to predict several soil properties. Prilianti et al [ 8 ] have successfully used a 1D CNN to predict pigment concentration in leaves from the reflectance spectra. Bjerrum et al [ 9 ] have developed methods like data augmentation and scatter correction specially for 1D CNNs and successfully predicted drug content in tablets from NIR spectra.…”

Section: Methodologiesmentioning

confidence: 99%

“…Previous studies [ 8 ] that used 1D CNNs on spectra suggest that shallow networks perform much better than deep networks, and hence, we decided to employ shallow networks for our models. Using shallow networks also helps us with reduced computational load and aids real‐time applications.…”

Section: Methodologiesmentioning

confidence: 99%

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Deep chemometrics using one‐dimensional convolutional neural networks for predicting crude oil properties from FTIR spectral data

Ta,

Alizadeh,

Samavedham

et al. 2023

Can J Chem Eng

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The determination of physicochemical properties of crude oils is a very important and time‐intensive process that needs elaborate laboratory procedures. Over the last few decades, several correlations have been developed to estimate these properties, but they have been very limited in their scope and range. In recent years, methods based on spectral data analysis have been shown to be very promising in characterizing petroleum crude. In this work, the physicochemical properties of crude oils using Fourier transform infrared (FTIR) spectrums are predicted. A total of 107 samples of FTIR spectral data consisting of 6840 wavenumbers is used. One dimensional convolutional neural networks (CNNs) were used employing FTIR spectral data as the one‐dimensional input and Keras and TensorFlow were used for model building. The Root Mean Square Error decreased from 160 to around 60 for viscosity when compared to previous machine learning methods like partial least squares (PLS), principal component regression (PCR), and partial least squares regression with genetic algorithm (PLS‐GA) on the same data. The important hyper‐parameters of the CNN were optimized. In addition, a comparison of results obtained with different neural network architectures is presented. Some common preprocessing techniques were also tested on the spectral data to determine their impact on model performance. To increase interpretability, the intermediate neural network layers were analyzed to reveal what the convolutions represented, and sensitivity analysis was done to gather key insights about the wavenumbers that were the most important for prediction of the crude oil properties using the neural network.

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“…and Graptophyllum pictum. Each species was chosen carefully to be able to represent the diversity of the chlorophyll, carotenoid, and anthocyanin [17]. These pigments have a major role in the photosynthesis process.…”

Section: A the Samplementioning

confidence: 99%

Non-destructive Photosynthetic Pigments Prediction using Multispectral Imagery and 2D-CNN

Prilianti¹,

Anam²,

Brotosudarmo³

et al. 2021

IJC

Self Cite

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Rapid assessment of plant photosynthetic pigments content is an essential issue in precise management farming. Such an assessment can represent the status of plants in their stages of growth. We have developed a new 2 Dimensional-Convolutional Neural Network (2D-CNN) architecture, the P3MNet. This architecture simultaneously predicts the content of 3 main photosynthetic pigments of a plant leaf in a non-destructive and real-time manner using multispectral images. Those pigments are chlorophyll, carotenoid, and anthocyanin. By illuminating with visible light, the reflectance of individual plant leaf at 10 different wavelengths – 350, 400, 450, 500, 550, 600, 650, 700, 750, and 800 nm – was captured in a form of 10 digital images. It was then used as the 2D-CNN input. Here, our result suggested that P3MNet outperformed AlexNet and VGG-9. After undergoing a training process using Adadelta optimization method for 1000 epochs, P3MNet has achieved superior MAE (Mean Absolute Error) in the average of 0.000778 ± 0.0001 for training and 0.000817 ± 0.0007 for validation (data range 0-1).

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Deep chemometrics for nondestructive photosynthetic pigments prediction using leaf reflectance spectra

Cited by 7 publications

References 15 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

Deep chemometrics using one‐dimensional convolutional neural networks for predicting crude oil properties from FTIR spectral data

Non-destructive Photosynthetic Pigments Prediction using Multispectral Imagery and 2D-CNN

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