An extensive dataset for successful recognition of fresh and rotten fruits.

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Abstract

Detection of rotten fruits is very crucial for agricultural productions and fruit processing as well as packaging industries. Usually, the detection of fresh and rotten fruits is done manually which is an ineffective and lengthy process for farmers. For this reason, the development of a new classification model is required which will reduce human effort, cost, and production time in the agriculture industry by recognizing defects in the fruits. This article offers a major dataset to the researchers to develop effective algorithms for recognizing more variety of fruits and overcome the limitations by increasing accuracy as well as decreasing computation time. This dataset contains sixteen types of fruit classes, namely fresh grape, rotten grape, fresh guava, rotten guava, fresh jujube, rotten jujube, fresh pomegranate, rotten pomegranate, fresh strawberry, rotten strawberry, fresh apple, rotten apple, fresh banana, rotten banana, fresh orange, rotten orange. We collected various fresh and rotten fruit images from 16th to 31st March 2022 from different fruit shops and real fields with the help of a domain specialist from an agricultural organization. The dataset is hosted by the Department of Computer Science and Engineering, Jahangirnagar University, and is freely available at https://data.mendeley.com/datasets/bdd69gyhv8/1.© 2022 The Author(s). Published by Elsevier Inc.