Melon Ripeness Determination Using K-nearest Neighbor Algorithm

This paper presents a method for determining the ripeness of Cantaloupe using a K-Nearest Neighbors (KNN) Algorithm on a Raspberry PI. One of the most common problems is determining fruit ripeness purely by visual inspection and traditional methods, such as relying on touch, which is challenging to implement. The Color Segmentation Algorithm used in the study operates in the HSV color space. The Canny Edge detection technique utilizes a region-growing approach, region merging, and initial seed selection. Following the segmentation process, the ripeness of the Cantaloupe is determined using the K-Nearest Neighbors (KNN) Algorithm based on its features, where accuracy reports from the dataset determine the best value of K. The proposed Color Segmentation Algorithm successfully segments the captured Cantaloupe images without any errors and determines their ripeness in most cases based on the KNN Algorithm. However, there are instances where the KNN algorithm incorrectly predicts ripeness from uneven lighting and objects detected in the image, resulting in an accuracy of 80 percent. In general, the system's accuracy based on the Confusion Matrix testing dataset is 95 percent, and as for actual testing, it's 80 percent, as stated before.