Deep Learning with Data Augmentation for Fruit Counting

Pornntiwa Pawara*, Alina Boshchenko, Lambert R. B. Schomaker, Marco A. Wiering

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


Counting the number of fruits in an image is important for orchard management, but is complex due to different challenging problems such as overlapping fruits and the difficulty to create large labeled datasets. In this paper, we propose the use of a data-augmentation technique that creates novel images by adding a number of manually cropped fruits to original images. This helps to increase the size of a dataset with new images containing more fruits and guarantees correct label information. Furthermore, two different approaches for fruit counting are compared: a holistic regression-based approach, and a detection-based approach. The regression-based approach has the advantage that it only needs as target value the number of fruits in an image compared to the detection-based approach where bounding boxes need to be specified. We combine both approaches with different deep convolutional neural network architectures and object-detection methods. We also introduce a new dataset of 1500 images named the Five-Tropical-Fruits dataset and perform experiments to evaluate the usefulness of augmenting the dataset for the different fruit-counting approaches. The results show that the regression-based approaches profit a lot from the data-augmentation method, whereas the detection-based approaches are not aided by data augmentation. Although one detection-based approach finally still works best, this comes with the cost of much more labeling effort.

Original languageEnglish
Title of host publication19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020
Subtitle of host publicationProceedings, Part I
Editors Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada
ISBN (Electronic)978-3-030-61401-0
ISBN (Print)978-3-030-61400-3
Publication statusPublished - 2020
Event International Conference on Artificial Intelligence and Soft Computing

ICAISC 2020: Artificial Intelligence and Soft Computing
- Zakopane, Poland
Duration: 12-Oct-202014-Oct-2020

Publication series

Name Lecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference International Conference on Artificial Intelligence and Soft Computing


Cite this