Tomato Feature Analysis and Detection in Horticulture using Deep Learning Algorithms
| dc.contributor.author | Rahman, Miskatur | |
| dc.date.accessioned | 2024-06-11T08:13:51Z | |
| dc.date.available | 2024-06-11T08:13:51Z | |
| dc.date.issued | 2024-06-11 | |
| dc.description | This internship report is submitted in partial fulfillment of the requirements for the degree of BSc. in Electronic & Telecommunication Engineering Cataloged from the PDF version of the internship report | |
| dc.description.abstract | Tomatoes fruits, a pivotal constituent of tomato plants, with a primary emphasis on elucidating the mechanisms governing their quality formation during the ripening process. Against the backdrop of heightened interest in the tomato industry, the research endeavours to augment the efficacy and success of automatic detection under greenhouse tomato conditions, a pivotal facet for the progression of contemporary agricultural practices. The paper introduces a ground-breaking method using convolutional neural networks to accurately classify tomato fruits based on their ripeness and overall condition. The study adopts a modified ResNet50V2 architecture as the underpinning framework for the CNN model, renowned for its effectiveness in image classification tasks. The outcomes demonstrate a commendable 95.36% accuracy in categorizing tomato fruits into four distinct classes: unripe, ripe, old, and damaged | |
| dc.identifier.uri | http://dspace.iiuc.ac.bd/handle/123456789/8244 | |
| dc.language.iso | en | |
| dc.publisher | International Islamic University Chittagong | |
| dc.title | Tomato Feature Analysis and Detection in Horticulture using Deep Learning Algorithms | |
| dc.type | Thesis |