Machine learning in plant science and plant breeding

Dijk, Aalt D. J. van; Kootstra, Gert; Kruijer, Willem; Ridder, Dick de

doi:10.1016/j.isci.2020.101890

Cited by 153 publications

(131 citation statements)

References 99 publications

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“…The ability to use these large collections of reflectance-based information increases the chances of finding meaningful signals which contribute to a better interpretation of the data, allowing for the development of more accurate prediction models. To date, several machine learning algorithms have been used to analyze and understand plant spectral data [18][19][20], however, there is no standard consensus on the best technique to use as this depends on a number of factors, such as the type of problem, type and size of the data, and type of output, for example. Therefore, a comparative approach is a conventional method to assess an algorithm's suitability to solve a particular problem.…”

Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana

et al. 2021

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Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines.

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Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana

et al. 2021

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“…This study identified greater allelic diversity in wild soybean and a set of 205,614 SNPs for use in quantitative trait loci (QTL) mapping and association studies. Very recently, Valliyodan et al (2021) analyzed genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild selections and 429 cultivated varieties (landraces and elites). This study identified evidence of distinct, mostly independent selection of lineages by particular geographic location.…”

Section: Genome Sequencing and Resequencingmentioning

confidence: 99%

Systems biology for crop improvement

et al. 2021

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In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species.Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

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“…Hence, the concept of an automated plant disease detection system was proposed [40]. Machine learning has been utilized to analyze and classify the input data for automatically detecting plant disease [41]- [43]. In terms of Ganoderma disease of oil palm, remote sensors with quantifiable input data are utilized and paired with machine learning approaches.…”

Section: Machine Learning For Ganoderma Disease Detectionmentioning

confidence: 99%

Discovering the Ganoderma Boninense Detection Methods Using Machine Learning: A Review of Manual, Laboratory, and Remote Approaches

et al. 2021

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Ganoderma disease is a kind of infection that actuates oil palm death. Early detection of Ganoderma disease is the most recommended strategy for proper treatment and disease control plan to be taken promptly. In this paper, the detection methods for Ganoderma disease were reviewed and categorized accordingly. It was found that the combination of remote sensors and machine learning techniques could identify the disease up to four severity levels, including the early stage of infection. It also significantly reduced the labor and time costs compared to the traditional visual inspection and lab-based approaches. In terms of machine learning, support vector machine (SVM) using the idea of finding a hyperplane was suggested as the best classifier in several studies. Despite only one research was done on ANN and no research evaluating CNN and GAN in Ganoderma disease detection; ANN, CNN and GAN were recognized as the potential machine learning techniques that could enhance the detection system.INDEX TERMS Basal stem rot, Ganoderma, machine learning, oil palm, remote sensors.

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Machine learning in plant science and plant breeding

Cited by 153 publications

References 99 publications

Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana

Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana

Systems biology for crop improvement

Discovering the Ganoderma Boninense Detection Methods Using Machine Learning: A Review of Manual, Laboratory, and Remote Approaches

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