A Case Study toward Apple Cultivar Classification Using Deep Learning

Machine Learning (ML) has enabled many image-based object detection and recognition-based solutions in various fields is the state-of-the-art method for these tasks currently. Therefore, it of interest to apply this technique different questions. In paper, we explore whether possible classify apple cultivars based on fruits using ML methods images question. The goal develop a tool that able cultivar could be used field. This helps draw attention variety diversity fruit growing contribute its preservation. Classifying certain challenge itself, as all apples are similar, while within one class can high. At same time, there potentially thousands indicating task becomes more challenging when added dataset. first question approach extract enough information correctly apples. focus technical requirements prerequisites verify approaches fulfill with limited number proof concept. We transfer learning popular image processing convolutional neural networks (CNNs) by retraining them custom Afterward, analyze classification results well problems. Our show classified correctly, but system design requires some extra considerations.