Apple Ripeness Identification Using Deep Learning

Deep learning models assist us in fruit classification, which allow us to use digital images from cameras to classify a fruit and find its class of ripeness automatically. Apple ripeness classification is a problem in computer vision and deep learning for pattern classification. In this paper, the ripeness of apples in digital images will be classified by using convolutional neural networks (CNN or ConvNets) in deep learning. The goal of this project is to verify the capability of deep learning models for fruit classification so as to lessen our human labor. Our experiments consist of four parts, namely, image preprocessing, apple detection, ripeness classification, and resultant evaluations. The contribution of this paper is that the classifiers are able to achieve the best result, i.e., the ripeness class of an apple from a given digital image is able to be precisely predicted. We have optimized the deep learning models and trained the classifiers so as to achieve the best outcome.