Vegetable Plant Leaf Disease Image Classification Using Convolutional Neural Network
DOI:
https://doi.org/10.69980/ajpr.v28i4.221Keywords:
Convolutional Neural Networks (CNN), Vegetative Leaf dataset; Machine Learning, RGB Images, Classification, Performance-orientedAbstract
Vegetables have a wealth of minerals, vitamins and calcium, hence numerous health benefits. In order to fully exploit such advantages, Different sorts of vegetables must be differentiated from one another. One of the means of doing this is to examine their images. In this paper, a total of 7226 RGB images composed of 25 kinds of vegetable leaf samples have been gathered. These images were used through the machine learning process specifically through the Convolutional Neural Networks (CNN) in order to build different models. Neural networks such as CNN, which learns a hierarchy of features as it sees more and more of the images, thus can classify images. Subsequently, the different models constructed were evaluated for four performance metrics, namely, precision (Pr), recall (Re) and F1 score (F1-S). The result revealed that CNN outperformed another classifiers with accuracy performance of 93.66%.
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