Deep Learning based Fruit Quality Inspection

Abstract

Abstract: Digital images and computer sciences have become two powerful tools in several areas, such as astronomy, medicine, forensics, etc. In the last few years, computer sciences are getting involved in agricultural and food science to decide based on estimated or actual parameters named features. Rottenness is the state of decomposing or decaying the quality of the fruit, which not only affects the taste and appearance but also modifies its nutritional composition, causing the presence of mycotoxins dangerous for humans. Detecting rotten fruits has become significant in the agricultural industry. Usually, the classification of fresh and rotten fruits carried by humans is not effective for the fruit farmers. Human beings will become tired after doing the same task multiple times, but machines do not. Thus, the project proposes an approach to reduce human efforts, reduce the cost and time for production by identifying the defects in the fruits in the agricultural industry. If we do not detect those defects, those defected fruits may contaminate good fruits. Hence, we proposed a model to avoid the spread of rottenness. The proposed model classifies the fresh fruits and rotten fruits from the input fruit images. Here, we use a trained deep learning model i,e sequential model to detect whether a fruit is fresh or rotten. In this work, three types of fruits, such as apple, banana, and oranges are used as a dataset. The experiments were done using a dataset composed of around 12000 images divided by 6 classes, 3 fresh fruits, and 3 rotten fruits