Neural network-based leaf classification using machine learning

In this paper, the leaf image data set is taken for classification. The process is done as image pre-processing, image segmentation, feature extraction, and image classification. The segmentation is done on colours. The K-Means clustering is applied to group the similar colour pixels. In total, three sub-images are created as an output of segmentation. In total, three classes are considered for the process. Data set with 70 leaf images is considered for the classification process. The data set are classified as yellow-based leaves, brown-based leaves, and green-dominated leaves. In this, the first two classes are considered as infected, based on the colours, and the third class is an uninfected collection of leaves. Seven different Neural Networks are constructed for classification process using MATLAB. Their performances are evaluated using the confusion matrices. From the outcome of confusion matrix, it is clear that the Regression Neural Network and Radial Bias Neural Network are better classifiers out of the seven architectures.