Drought stress detection technique for wheat crop using machine learning
The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-05-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1268.pdf |
| _version_ | 1827943098895826944 |
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| author | Ankita Gupta Lakhwinder Kaur Gurmeet Kaur |
| author_facet | Ankita Gupta Lakhwinder Kaur Gurmeet Kaur |
| author_sort | Ankita Gupta |
| collection | DOAJ |
| description | The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models. |
| first_indexed | 2024-03-13T10:10:21Z |
| format | Article |
| id | doaj.art-39d22bfbbde7437c953d1a23a45795ef |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-13T10:10:21Z |
| publishDate | 2023-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-39d22bfbbde7437c953d1a23a45795ef2023-05-21T15:05:04ZengPeerJ Inc.PeerJ Computer Science2376-59922023-05-019e126810.7717/peerj-cs.1268Drought stress detection technique for wheat crop using machine learningAnkita Gupta0Lakhwinder Kaur1Gurmeet Kaur2Computer Science and Engineering, Punjabi University, Patiala, Punjab, IndiaComputer Science and Engineering, Punjabi University, Patiala, Punjab, IndiaElectronics and Communication Engineering, Punjabi University, Patiala, Punjab, IndiaThe workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.https://peerj.com/articles/cs-1268.pdfWheatImage processingMachine learningChlorophyll fluoroscenceDroughtTexture analysis |
| spellingShingle | Ankita Gupta Lakhwinder Kaur Gurmeet Kaur Drought stress detection technique for wheat crop using machine learning PeerJ Computer Science Wheat Image processing Machine learning Chlorophyll fluoroscence Drought Texture analysis |
| title | Drought stress detection technique for wheat crop using machine learning |
| title_full | Drought stress detection technique for wheat crop using machine learning |
| title_fullStr | Drought stress detection technique for wheat crop using machine learning |
| title_full_unstemmed | Drought stress detection technique for wheat crop using machine learning |
| title_short | Drought stress detection technique for wheat crop using machine learning |
| title_sort | drought stress detection technique for wheat crop using machine learning |
| topic | Wheat Image processing Machine learning Chlorophyll fluoroscence Drought Texture analysis |
| url | https://peerj.com/articles/cs-1268.pdf |
| work_keys_str_mv | AT ankitagupta droughtstressdetectiontechniqueforwheatcropusingmachinelearning AT lakhwinderkaur droughtstressdetectiontechniqueforwheatcropusingmachinelearning AT gurmeetkaur droughtstressdetectiontechniqueforwheatcropusingmachinelearning |