Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing
| Autor: | Juana-Valentina García-Ariza, Marco-Javier Suarez-Barón, Edmundo-Arturo Junco-Orduz, Juan-Sebastián González-Sanabria |
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| Jazyk: | English<br />Spanish; Castilian |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Inge-Cuc, Vol 18, Iss 2, Pp 89-100 (2022) |
| Druh dokumentu: | article |
| ISSN: | 0122-6517 2382-4700 |
| DOI: | 10.17981/ingecuc.18.2.2022.07 |
| Popis: | 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, |
| Databáze: | Directory of Open Access Journals |
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