Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing
DOI:
https://doi.org/10.17981/ingecuc.18.2.2022.07Keywords:
machine learning, unsupervised learning, K-Means, hierarchical, clustering, late blightAbstract
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,
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