Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.)

Abstract

According to data from BPS Kabupaten Jember, the amount of cucumber production fluctuated from 2013 to 2017. Some literature also mentions that one of the causes of the amount of cucumber production is disease attacks on these plants. Most of the cucumber plant diseases found in the leaf area such as downy mildew and powdery mildew which are both caused by fungi (fungal diseases). So far, farmers check cucumber plant diseases manually, so there is a lack of accuracy in determining cucumber plant diseases. To help farmers, a computer vision system that is able to identify cucumber diseases automatically will have an impact on the speed and accuracy of handling cucumber plant diseases. This research used 90 training data consisting of 30 healthy leaf data, 30 powdery mildew leaf data and 30 downy mildew leaf data. while for the test data as many as 30 data consisting of 10 data in each class. To get suitable parameters, a feature selection process is carried out on color features and texture features so that suitable parameters are obtained, namely: red color features, texture features consisting of contrast, Inverse Different Moment (IDM) and correlation. The K-Nearest Neighbor classification method is able to classify diseases on cucumber leaves (Cucumis sativa L.) with a training accuracy of 90% and a test accuracy of 76.67% using a variation of the value of K = 7.

Implementing K-Nearest Neighbor to Classify Wild Plant Leaf as a Medicinal Plants

“…K-Nearest Neighbor (KNN) algorithm is a method for classifying objects based on the training data closest to the object [10]. KNN algorithm is a method that uses a supervised learning algorithm [12].…”

“…In addition to morphological features, GLCM texture features can be used in classifying leaf images by using the adjacency value of K = 1 to get an accuracy rate of 98% [8]. The K-Nearest Neighbor method was also used to identify Siamese citrus leaf disease with an accuracy of 70% [9], identify cucumber leaf disease with an accuracy of 90% [10], classify leaf images of sweet potato varieties with an accuracy of 95% [11] and classifying tomato quality damage with an accuracy of 86.6% [12].…”