A Deep Learning Approach to Detect the Spoiled Fruits

Abstract

Fruits are one of the vital sources of nutrients for the mankind and their life span is very less. The fruit spoilage may occur at various stages such as, at the harvest time, during transportation, during storage etc. Freshness is a parameter used for accessing the quality of the fruit. About 20% of the harvested fruits are spoiled due to many factors, before consumption by humans. The spoilage of one fruit has a direct impact on the neighboring fruits. It is also a one of the indicators that gives an estimation of number of days that a fruit can be preserved. Early identification of the spoilage helps in taking the appropriate measures for the removal of spoiled fruits from the whole lot. So that it helps in preventing the spread of spoilage to its adjacent fruits. Deep learning based technological advancements helps in automatically identifying the spoiled fruits. In this work, internal quality attributes of the fruit are not taken into consideration for spoilage detection, only the external attributes are considered. The supervised learning technique is employed for the freshness analysis of two different types of fruits, Apple and Banana. As the 2 varieties are involved, it is a multiclass classification model with 4 classes. One shot detection technique is employed to accurately classify among the good fruit and spoiled fruit. Few images in the dataset are obtained from the kaggle.com and the rest are self - captured images. The dataset is balanced to avoid the biasness in the model. The model is implemented using Yolov4 and tiny Yolov4 frame works. These are one shot detection techniques, can be used for real time deployment. The inferences were obtained on the real time images and video. Confusion matrix is tabulated the performance metrics such as accuracy, F1 Score and recall are discussed with respect to these two techniques.