Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory
| dc.contributor.advisor | Er, Sebnem | |
| dc.contributor.author | Harris, Jessica | |
| dc.date.accessioned | 2022-01-27T07:58:04Z | |
| dc.date.available | 2022-01-27T07:58:04Z | |
| dc.date.issued | 2021 | |
| dc.date.updated | 2022-01-26T13:36:37Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Harris, J. (2021). <i>Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/35596 | en_ZA |
| dc.identifier.chicagocitation | Harris, Jessica. <i>"Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2021. http://hdl.handle.net/11427/35596 | en_ZA |
| dc.identifier.citation | Harris, J. 2021. Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/35596 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Harris, Jessica AB - 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. DA - 2021_ DB - OpenUCT DP - University of Cape Town KW - Statistical Sciences LK - https://open.uct.ac.za PY - 2021 T1 - Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory TI - Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory UR - http://hdl.handle.net/11427/35596 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/35596 | |
| dc.identifier.vancouvercitation | Harris J. Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory. []. ,Faculty of Science ,Department of Statistical Sciences, 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/35596 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Statistical Sciences | |
| dc.title | Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |