Apple and Cherry Classification using Convolutional Neural Network (CNN)

Agriculture now plays a significant role in daily life. Among these, fruits are fantastic in daily living. Classifying fruits according to their accuracy is a good strategy for all fruit vendors. The classification is crucial since there are many similarities between the apple and the cherry and across a wide range of fruit species. However, utilizing machine learning methods like Support Vector Machine (SVM) and Convolutional Neural Network, there are issues with fruit classification (CNN). In order to solve the issues, CNN, pooling layers, and fully connected networks have been used. The features of the fruits have been extracted using the CNN and pooling layers. Several fruits, including Apple, Blueberry, Cherry, Grape Blue, Guava, Kiwi, Lemon, Papaya, Strawberry, Plum, Tomato, and Mango are taken into consideration in order to expose this scheme. This project's implementation improves fruit classification's precision