A generic workflow combining deep learning and chemometrics for processing close-range spectral images to detect drought stress in Arabidopsis thaliana to support digital phenotyping

Mishra, Puneet; Sadeh, Roy; Ryckewaert, Maxime; Bino, Ehud; Polder, G.; Boer, Martin P.; Rutledge, Douglas N.; Herrmann, Ittai

doi:10.1016/j.chemolab.2021.104373

Cited by 12 publications

(3 citation statements)

References 40 publications

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“…In the current state of the art, recent applications of HTPP suggest that automation can be performed for a wide variety of traits including drought tolerance [2,32], salt tolerance [33], and biotic stress [34,35] as well as for assessing the efficiency of plant protection agents [30]. Furthermore, facilities are not only used for phenotyping small model plants such as Arabidopsis thaliana [36,37] but also can be used for large plants such as Zea mays [2,32] and even, tree species [38]. Currently, the main sensors integrated into HTP setups are vision-based sensors for tasks such as 3D shape estimation which allows morphological monitoring of features such as height, width, leaf area, and leaf development, along with general plant growth parameters.…”

Section: Current State Of the Art: Automated Plant Phenotypingmentioning

confidence: 99%

High-throughput plant phenotyping: a role for metabolomics?

Hall

D’Auria

Ferreira

et al. 2022

Trends in Plant Science

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Section: Current State Of the Art: Automated Plant Phenotypingmentioning

confidence: 99%

High-throughput plant phenotyping: a role for metabolomics?

Hall

D’Auria

Ferreira

et al. 2022

Trends in Plant Science

Self Cite

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“…CNN-ConvLSTM achieved 97.97% accuracy with very few trainable parameters. Mishra et al [16] used a combined deep learning and chemometrics approach for processing close-range spectral images to detect drought stress in Arabidopsis thaliana to support digital phenotyping. Xu et al [17] proposed a localization and classification method based on a two-level variable domain fusion network to detect tea sprout detection.…”

Section: Introductionmentioning

confidence: 99%

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning

Liu,

Zhu,

Liu

et al. 2024

Plants

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The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis’s 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model’s classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model’s performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model’s generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.

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“…A deep learning technique was used to identify the plant stress level due to nitrogen deficiency, in which the CNN outperformed machine learning algorithms and had an accuracy of approximately 75% [ 25 ]. A digital plant phenotyping platform for early-stage drought detection and quantification in Arabidopsis was designed using deep learning and chemometrics [ 26 ]. The researchers processed close range spectral images with deep learning techniques and validated its feasibility based on an experiment for drought stress quantification in semi-controlled environments.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea (Pisum sativum L.)

Divyanth

Marzougui

González-Bernal

et al. 2022

Sensors

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Aphanomyces root rot (ARR) is a devastating disease that affects the production of pea. The plants are prone to infection at any growth stage, and there are no chemical or cultural controls. Thus, the development of resistant pea cultivars is important. Phenomics technologies to support the selection of resistant cultivars through phenotyping can be valuable. One such approach is to couple imaging technologies with deep learning algorithms that are considered efficient for the assessment of disease resistance across a large number of plant genotypes. In this study, the resistance to ARR was evaluated through a CNN-based assessment of pea root images. The proposed model, DeepARRNet, was designed to classify the pea root images into three classes based on ARR severity scores, namely, resistant, intermediate, and susceptible classes. The dataset consisted of 1581 pea root images with a skewed distribution. Hence, three effective data-balancing techniques were identified to solve the prevalent problem of unbalanced datasets. Random oversampling with image transformations, generative adversarial network (GAN)-based image synthesis, and loss function with class-weighted ratio were implemented during the training process. The result indicated that the classification F1-score was 0.92 ± 0.03 when GAN-synthesized images were added, 0.91 ± 0.04 for random resampling, and 0.88 ± 0.05 when class-weighted loss function was implemented, which was higher than when an unbalanced dataset without these techniques were used (0.83 ± 0.03). The systematic approaches evaluated in this study can be applied to other image-based phenotyping datasets, which can aid the development of deep-learning models with improved performance.

show abstract

A generic workflow combining deep learning and chemometrics for processing close-range spectral images to detect drought stress in Arabidopsis thaliana to support digital phenotyping

Cited by 12 publications

References 40 publications

High-throughput plant phenotyping: a role for metabolomics?

High-throughput plant phenotyping: a role for metabolomics?

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning

Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea (Pisum sativum L.)

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