Convolutional Neural Network for Fruit Image Classification

Fruit image classification is one of the many things we can achieve when dealing with an artificial neural network, as it is very handy in helping many groups in society. This technology can improve the productivity rate, save a lot of expenses, and improve the competitivity of the worldwide fruit producer’s market. We use various technologies such as deep convolutional neural networks (DCNN) for deep learning, which is the pinnacle of technology (Sharma, 2021). The problem with DCNNs is that they require the necessities of high calculation and capacity assets, thus deny the utilizations of DCNNs on asset-restricted conditions like programmed reaping robots. Consequently, we want to pick a lightweight neural network to accomplish the equilibrium of asset limits and recognition precision. This paper will dive into the details of the processes that were done to showcase how the system is able to recognize fruits with the results.