Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory

The aim of this thesis is to develop a method for determining pineapple fruit size from images. This was achieved by first detecting pineapples in each image using Mask Region-based Convolutional Neural Network (Mask R-CNN) and then extracting the pixel diameter and length measurements, and the projected areas, from the detected mask outputs. Various Mask R-CNNs were considered for the task of pineapple detection. The best-performing detector made use of MS COCO starting weights, a ResNet50 CNN backbone, and horizontal flipping data augmentation during the training process. This model (Model 4: COCO Fliplr Res50) achieved an average precision of 91.4% on the validation set and an average precision of 90.1% on the test set, and was used to predict masks for an unseen dataset containing images of pre-measured pineapples. The distributions of measurements extracted from the detected masks were compared to those of the manual measurements using two-sample Z-tests and Kolmogorov–Smirnov (KS) tests. There was sufficient similarity between the distributions, and it was therefore established that the reported method is appropriate for pineapple size determination in this context. All the data and code is available in a GitHub repository for reproducible research.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2021

Author dc:creator

• Harris, Jessica

Advisor dc:contributor.advisor

• Er, Sebnem

• Statistical Sciences

Handle dc:identifier.uri

http://hdl.handle.net/11427/35596

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/35596