Paper Title
A Machine Learning Approach for Non-Leafy Vegetables Detection Using Perceptron Method

Utilization of image processing technique is expanding step by step in all fields. In horticulture it is additionally utilized to check the nature of vegetables and organic products. Shape, shading and size are the picture quality which helps in quality identification of vegetables. In this paper proposed strategy is utilized to expand the precision of the vegetable quality discovery by utilizing shading, shape, and size based technique with blend of Artificial neural network(ANN). It reviews and group’s vegetable pictures dependent on acquired element esteems by utilizing fell forward network. The proposed framework begins the cycle by catching the vegetable's picture. Then, at that point, the picture is communicated to the preparing level where the vegetable quality like tone, shape and size of vegetable examples are separated. After that by utilizing artificial neural network vegetable pictures are going through the preparation and testing. Artificial neural network recognize the nature of vegetables by utilizing the shape tone and size quality gave at the hour of preparing and furthermore the extricated quality of vegetables and gives the outcome by looking at these quality. In this proposed paper neural organization is utilized to identify shape, size and shade of vegetable and with the mix of these three qualities the outcomes got are exceptionally encouraging accuracy of 94.08% using KNN & 97.60% using PNN. Keywords - Fractal features, Color correlogram, Classifiers, Perceptron, RGB, HSV.