ANOVA-Artificial Bee Colony Algorithm-Driven Feature Selection for Classifying Downy Mildew Severity in Melon Leaves

Abstract

Diseases of melon plants can cause losses to farmers, such as reduced productivity or even death of melon plants. Downy mildew (DM) is a well-known fast-spreading disease affecting the leaves of melon plants. It is important to determine the level of severity of DM leaf disease so that farmers can take preventive measures according to the severity of DM disease that infects the leaves. Determining the severity of DM disease can be done with experts, but experts have limitations, namely, the availability of experts, and not all areas have leaf disease experts. The stages of this research were data acquisition, preprocessing, feature extraction, feature selection, and classification. Data were taken directly from farmers' gardens and then pre-processed. Color, texture, edge, and entropy features were extracted to obtain the combined feature values. Combined features are prone to redundant and irrelevant features; therefore, feature selection must be performed to obtain the best features. This research proposes an integration concept between the analysis of variance (ANOVA) and artificial bee colony (ABC) optimization, which is named AVABC, and is used as a feature selection algorithm. The test results for the search process time for the eight best features using the AVABC algorithm took 05 minutes 23 s, whereas the test results for the search process time for eight features using ABC with the accuracy model fitness function took 20 h 08 min 55 s. The AVABC feature selection algorithm has the advantage of faster search time for the eight best features.