Spoiled and fresh fruit inspection dataset

Published: 1 November 2020| Version 1 | DOI: 10.17632/6ps7gtp2wg.1
Cesar Giovany Pachon Suescun,


The design of quality control systems in food has become an essential element in research to ensure a state suitable for consumption. It is necessary to develop automatic and efficient systems that can verify their condition before distribution. The proposed dataset can be used with a deep learning-based algorithm for the identification of the fruits and the state in which they are. The data set has 8 different fruits: -banana -lemon -lulo -mango -orange -strawberry -tamarillo -tomato. Two thousand images of each one of the types of fruits are acquired for a total of 16000 samples. Half of them correspond to fresh fruits and the other half to non-fresh or spoiled fruits. For the acquisition of the dataset, changing of backgrounds, rotation of the fruits, distance of capture, and light variations were made, in order to make it robust.



Machine Learning, Deep Learning, Computer Vision Algorithms