Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics
Abstract Background Specific detection of the type and severity of plant abiotic stresses helps prevent yield loss by considering timely actions. This study introduces a novel method to detect the type and severity of stress in cucumber plants under salinity and drought conditions. Various features,...
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-023-01095-x |
| _version_ | 1797630184108589056 |
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| author | Parvin Mohammadi Keyvan Asefpour Vakilian |
| author_facet | Parvin Mohammadi Keyvan Asefpour Vakilian |
| author_sort | Parvin Mohammadi |
| collection | DOAJ |
| description | Abstract Background Specific detection of the type and severity of plant abiotic stresses helps prevent yield loss by considering timely actions. This study introduces a novel method to detect the type and severity of stress in cucumber plants under salinity and drought conditions. Various features, i.e., morphological (image textural features), physiological/biochemical (relative water content, chlorophyll, catalase activity, anthocyanins, phenol content, and proline), as well as miRNA characteristics (the concentration of miRNA-156a, miRNA-166i, miRNA-399g, and miRNA-477b) were extracted from plant leaves, and machine learning methods were used to predict the type and severity of stress by having these features. Support vector machine (SVM) with parameters optimized by genetic algorithm (GA) and particle swarm optimization (PSO) was used for machine learning. Results The coefficient of determination of predicting the stress type and severity in plants under both stresses was 0.61, 0.82, and 0.99 using morphological, physiological/biochemical, and miRNA characteristics, respectively. This reveals machine learning methods optimized by metaheuristic optimization techniques can provide specific detection of salt and drought stresses in cucumber plants based on miRNA characteristics. Among the study miRNAs, miRNA-477b and miRNA-399g had the highest and lowest contribution to salt and drought stresses, respectively. Conclusions Comapred to conventional plant traits, miRNAs are more reliable features for providing us with valuable information about plant abiotic diseases at early stages. Using an electrochemical miRNA biosensor similar to one used in this work to measure the miRNA concentration in plant leaves and using a machine learning algorithm such as SVM enable farmers to detect the salt and drought stress at early stages in cucumber plants with very high accuracy. |
| first_indexed | 2024-03-11T11:04:35Z |
| format | Article |
| id | doaj.art-a84aa3c76d65404db1ea67738a39a9df |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-03-11T11:04:35Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-a84aa3c76d65404db1ea67738a39a9df2023-11-12T12:18:58ZengBMCPlant Methods1746-48112023-11-0119111210.1186/s13007-023-01095-xMachine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristicsParvin Mohammadi0Keyvan Asefpour Vakilian1Department of Agrotechnology, College of Abouraihan, University of TehranDepartment of Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural ResourcesAbstract Background Specific detection of the type and severity of plant abiotic stresses helps prevent yield loss by considering timely actions. This study introduces a novel method to detect the type and severity of stress in cucumber plants under salinity and drought conditions. Various features, i.e., morphological (image textural features), physiological/biochemical (relative water content, chlorophyll, catalase activity, anthocyanins, phenol content, and proline), as well as miRNA characteristics (the concentration of miRNA-156a, miRNA-166i, miRNA-399g, and miRNA-477b) were extracted from plant leaves, and machine learning methods were used to predict the type and severity of stress by having these features. Support vector machine (SVM) with parameters optimized by genetic algorithm (GA) and particle swarm optimization (PSO) was used for machine learning. Results The coefficient of determination of predicting the stress type and severity in plants under both stresses was 0.61, 0.82, and 0.99 using morphological, physiological/biochemical, and miRNA characteristics, respectively. This reveals machine learning methods optimized by metaheuristic optimization techniques can provide specific detection of salt and drought stresses in cucumber plants based on miRNA characteristics. Among the study miRNAs, miRNA-477b and miRNA-399g had the highest and lowest contribution to salt and drought stresses, respectively. Conclusions Comapred to conventional plant traits, miRNAs are more reliable features for providing us with valuable information about plant abiotic diseases at early stages. Using an electrochemical miRNA biosensor similar to one used in this work to measure the miRNA concentration in plant leaves and using a machine learning algorithm such as SVM enable farmers to detect the salt and drought stress at early stages in cucumber plants with very high accuracy.https://doi.org/10.1186/s13007-023-01095-xSupport vector machineOptimization algorithmsmiRNA biosensorImage textural features |
| spellingShingle | Parvin Mohammadi Keyvan Asefpour Vakilian Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics Plant Methods Support vector machine Optimization algorithms miRNA biosensor Image textural features |
| title | Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics |
| title_full | Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics |
| title_fullStr | Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics |
| title_full_unstemmed | Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics |
| title_short | Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics |
| title_sort | machine learning provides specific detection of salt and drought stresses in cucumber based on mirna characteristics |
| topic | Support vector machine Optimization algorithms miRNA biosensor Image textural features |
| url | https://doi.org/10.1186/s13007-023-01095-x |
| work_keys_str_mv | AT parvinmohammadi machinelearningprovidesspecificdetectionofsaltanddroughtstressesincucumberbasedonmirnacharacteristics AT keyvanasefpourvakilian machinelearningprovidesspecificdetectionofsaltanddroughtstressesincucumberbasedonmirnacharacteristics |