Fruits Classification Using Convolutional Neural Network

Due to the increased global food waste that has resulted from careless production,processing,and distribution methods,food security has become an increasingly hot topic of discussion in modern society.Statistics gathered from surveys conducted by institutions around the world,such as the Food Bureau of the US,have also made it clear that an increase in food production is required to meet the needs of a rapidly expanding population.All of this highlights the growing importance of optimising available resources to aid in the effective production and management of our food supply.The agricultural sector has realised the importance of detecting spoiled produce.Fruit farmers often waste time and energy using humans to determine which fruits are fresh and which are rotten.Machines don’t get tired of doing the same thing over and over again like humans do.As a result,the project proposes a method for reducing human labour,as well as production costs and lead times,in the agricultural sector through the detection of fruit flaws.Those flawed fruits could taint healthy ones if we don’t catch them properly.Because of its usefulness in automating routine tasks and improving the quality of the results,deep learning has found application in many different areas.As part of efforts to ensure that people always have access to safe and nutritious food,deep learning is being put to use both in the field and in the marketplace to boost crop yields and protect fruit quality.To guarantee reliable reports on the quality of fruit sold to consumers,scientists are investigating the feasibility of using deep learning for in-store inspection and grading systems.Moreover,since visual evaluation is the primary basis for a purchase decision in the market,it is important to ensure the visual quality of fruits in order to drive sales.The automatic feature learning capabilities of CNNs make them ideal for image classification tasks like distinguishing between fresh and rotten fruits.The pooling layers of a CNN can reduce the spatial dimensions of the features,while the convolutional layers can detect patterns and edges in an image.A straightforward CNN model for visual fruit recognition is something I plan to work on.The proposed model is able to identify specific types of fruits in input images,as well as distinguish between them,and use this information to make predictions about whether the fruits are fresh or rotten.A variety of fruits,including apples,bananas,and oranges,will be used in this project.This would serve as the groundwork for the creation of a fruit classification system,the purpose of which would be to detect rotten produce.Because of this system,humans won’t have to spend as much time or energy sorting fruits at grocery stores,and they won’t have to handle as much farm produce along the supply chain.Eliminating unnecessary and inappropriate handling of farm products by non-professionals through automation could help to solve problems of food poisoning and others in a world where new strains of viruses and bacteria cause health issues.The proposed CNN model successfully differentiates between fresh and decayed fruit.