Data for Deep Learning for Noninvasive Classification of Clustered Horticultural Crops – A Case for Banana Fruit Tiers

Published: 4 February 2019| Version 1 | DOI: 10.17632/xpz3d7jhbp.1
Eduardo Jr Piedad, Tuan-Tang Le, Chyi-Yeu Lin


This real data has 194 collected banana tier images. It has two parts – the normal and the abnormal (reject) classes, having 139 and 55 samples, respectively. It can be observed the difficulty of classifying banana tiers based only on human recognition due to their clustered and varying physical structures. There are two excel files attached, the data_partition_normal_bananas and the data_partition_reject_bananas, where one can see the train-test data partitions for machine learning or deep learning application. In each file, there are five train-test sets for stratified sampling cross-validation purpose.



National Taiwan University of Science and Technology, University of San Jose Recoletos


Horticulture, Agricultural Engineering, Machine Learning, Fruit, Applied Computer Science, Classification System, Banana, Horticultural Crops, Deep Learning