Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing
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| Natura: | Artículo científico |
| Lingua: | en |
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Corporación Universitaria de la Costa
2022
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| _version_ | 1876472672393625600 |
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| author | Juana-Valentina García-Ariza |
| author_facet | Juana-Valentina García-Ariza |
| contents | Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing Juana-Valentina García-Ariza Marco-Javier Suarez-Barón Edmundo-Arturo Junco-Orduz Juan-Sebastián González-Sanabria Ingeniería Means late blight Machine learning unsupervised learning hierarchical clustering Introduction— Automatic detection can be useful in the search of large crop fields by simply detecting the disease with the symptoms appearing on the leaf. Objective— This paper presents the application of machine learning techniques aimed at detecting late blight disease using unsupervised learning methods such as K-Means and hierarchical clustering. Method— The methodology used is composed by the following phases— acquisition of the dataset, image processing, feature extraction, feature selection, implementation of the learning model, performance measurement of the algorithm, finally a 68.24% hit rate was obtained being this the best result of the unsupervised learning algorithms implemented, using 3 clusters for clustering. Results— According to the results obtained, the performance of the K-Means algorithm can be evaluated, i.e. 202 hits and 116 misses. Conclusions— Unsupervised learning algorithms are very efficient when processing a large amount of data, in this case a large amount of images without the need for predefined labels, its use to solve local problems such as late blight affectations in potato crops are novel. 2022 artículo científico 0122-6517 https://www.redalyc.org/articulo.oa?id=497779342019 en http://www.redalyc.org/revista.oa?id=4977 INGE CUC application/pdf Corporación Universitaria de la Costa INGE CUC (Colombia) Num.2 Vol.18 |
| format | Artículo científico |
| id | redalyc_497779342019 |
| institution | Redalyc |
| language | en |
| publishDate | 2022 |
| publisher | Corporación Universitaria de la Costa |
| spellingShingle | Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing Juana-Valentina García-Ariza Ingeniería Means late blight Machine learning unsupervised learning hierarchical clustering Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing Juana-Valentina García-Ariza Marco-Javier Suarez-Barón Edmundo-Arturo Junco-Orduz Juan-Sebastián González-Sanabria Ingeniería Means late blight Machine learning unsupervised learning hierarchical clustering Introduction— Automatic detection can be useful in the search of large crop fields by simply detecting the disease with the symptoms appearing on the leaf. Objective— This paper presents the application of machine learning techniques aimed at detecting late blight disease using unsupervised learning methods such as K-Means and hierarchical clustering. Method— The methodology used is composed by the following phases— acquisition of the dataset, image processing, feature extraction, feature selection, implementation of the learning model, performance measurement of the algorithm, finally a 68.24% hit rate was obtained being this the best result of the unsupervised learning algorithms implemented, using 3 clusters for clustering. Results— According to the results obtained, the performance of the K-Means algorithm can be evaluated, i.e. 202 hits and 116 misses. Conclusions— Unsupervised learning algorithms are very efficient when processing a large amount of data, in this case a large amount of images without the need for predefined labels, its use to solve local problems such as late blight affectations in potato crops are novel. 2022 artículo científico 0122-6517 https://www.redalyc.org/articulo.oa?id=497779342019 en http://www.redalyc.org/revista.oa?id=4977 INGE CUC application/pdf Corporación Universitaria de la Costa INGE CUC (Colombia) Num.2 Vol.18 |
| title | Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing |
| topic | Ingeniería Means late blight Machine learning unsupervised learning hierarchical clustering |
| url | https://www.redalyc.org/articulo.oa?id=497779342019 |