Fruit Classification Using Optimized CNN

In many commercial contexts, fruit classification is vital. Such as factories, supermarkets, and other settings. Fruit classification is significant for a variety of reasons, including for persons with dietary restrictions to help them choose the appropriate fruit categories. There have been numerous machine learning methods for classifying fruits in the past. With its recognition and classification capabilities, deep learning has the potential to be a potent engine for generating results that can be put into practice. Deep learning approaches like CNNS showed tremendous promise for precisely categorised fruits. Basic fruit characteristics including colour (RGB Colour space), size, height, and width were retrieved from its image in order to use machine learning methods. KNN traditional machine learning methods were used on the features that were extracted. Convolutional neural network (CNN) technology offers a viable alternative to conventional machine learning techniques, according to the results. The TensorFlow backend was used to create the model. It received a 96.88% accuracy rating after 40 training epochs.