Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology
Automated monitoring of plant health is becoming a crucial component for optimizing agricultural production. Recently, several studies have shown that plant electrophysiology could be used as a tool to determine plant status related to applied stressors. However, to the best of our knowledge, there...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/12/5640 |
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| author | Elena Najdenovska Fabien Dutoit Daniel Tran Antoine Rochat Basile Vu Marco Mazza Cédric Camps Carrol Plummer Nigel Wallbridge Laura Elena Raileanu |
| author_facet | Elena Najdenovska Fabien Dutoit Daniel Tran Antoine Rochat Basile Vu Marco Mazza Cédric Camps Carrol Plummer Nigel Wallbridge Laura Elena Raileanu |
| author_sort | Elena Najdenovska |
| collection | DOAJ |
| description | Automated monitoring of plant health is becoming a crucial component for optimizing agricultural production. Recently, several studies have shown that plant electrophysiology could be used as a tool to determine plant status related to applied stressors. However, to the best of our knowledge, there have been no studies relating electrical plant response to general stress responses as a proxy for plant health. This study models general stress of plants exposed to either biotic or abiotic stressors, namely drought, nutrient deficiencies or infestation with spider mites, using electrophysiological signals acquired from 36 plants. Moreover, in the signal processing procedure, the proposed workflow reuses information from the previous steps, therefore considerably reducing computation time regarding recent related approaches in the literature. Careful choice of the principal parameters leads to a classification of the general stress in plants with more than 80% accuracy. The main descriptive statistics measured together with the Hjorth complexity provide the most discriminative information for such classification. The presented findings open new paths to explore for improved monitoring of plant health. |
| first_indexed | 2024-03-10T10:17:19Z |
| format | Article |
| id | doaj.art-284c0368c60b4cbc92dcca39907f3b17 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:17:19Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-284c0368c60b4cbc92dcca39907f3b172023-11-22T00:41:42ZengMDPI AGApplied Sciences2076-34172021-06-011112564010.3390/app11125640Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant ElectrophysiologyElena Najdenovska0Fabien Dutoit1Daniel Tran2Antoine Rochat3Basile Vu4Marco Mazza5Cédric Camps6Carrol Plummer7Nigel Wallbridge8Laura Elena Raileanu9School of Engineering and Management of the Canton of Vaud (HEIG-VD), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1401 Yverdon-les-Bains, SwitzerlandSchool of Engineering and Management of the Canton of Vaud (HEIG-VD), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1401 Yverdon-les-Bains, SwitzerlandAGROSCOPE, Institute for Plant Production Systems, 1964 Conthey, SwitzerlandSchool of Engineering and Management of the Canton of Vaud (HEIG-VD), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1401 Yverdon-les-Bains, SwitzerlandSchool of Engineering and Management of the Canton of Vaud (HEIG-VD), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1401 Yverdon-les-Bains, SwitzerlandSchool of Engineering and Architecture Friborg (HEIA-Fr), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1700 Fribourg, SwitzerlandAGROSCOPE, Institute for Plant Production Systems, 1964 Conthey, SwitzerlandVIVENT SA, 1196 Gland, SwitzerlandVIVENT SA, 1196 Gland, SwitzerlandSchool of Engineering and Management of the Canton of Vaud (HEIG-VD), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1401 Yverdon-les-Bains, SwitzerlandAutomated monitoring of plant health is becoming a crucial component for optimizing agricultural production. Recently, several studies have shown that plant electrophysiology could be used as a tool to determine plant status related to applied stressors. However, to the best of our knowledge, there have been no studies relating electrical plant response to general stress responses as a proxy for plant health. This study models general stress of plants exposed to either biotic or abiotic stressors, namely drought, nutrient deficiencies or infestation with spider mites, using electrophysiological signals acquired from 36 plants. Moreover, in the signal processing procedure, the proposed workflow reuses information from the previous steps, therefore considerably reducing computation time regarding recent related approaches in the literature. Careful choice of the principal parameters leads to a classification of the general stress in plants with more than 80% accuracy. The main descriptive statistics measured together with the Hjorth complexity provide the most discriminative information for such classification. The presented findings open new paths to explore for improved monitoring of plant health.https://www.mdpi.com/2076-3417/11/12/5640plant’s general stresscrop monitoringmachine learningdiscriminative featuresplant electrophysiology |
| spellingShingle | Elena Najdenovska Fabien Dutoit Daniel Tran Antoine Rochat Basile Vu Marco Mazza Cédric Camps Carrol Plummer Nigel Wallbridge Laura Elena Raileanu Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology Applied Sciences plant’s general stress crop monitoring machine learning discriminative features plant electrophysiology |
| title | Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology |
| title_full | Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology |
| title_fullStr | Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology |
| title_full_unstemmed | Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology |
| title_short | Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology |
| title_sort | identifying general stress in commercial tomatoes based on machine learning applied to plant electrophysiology |
| topic | plant’s general stress crop monitoring machine learning discriminative features plant electrophysiology |
| url | https://www.mdpi.com/2076-3417/11/12/5640 |
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