How Well Can MobileNetV3 Perform in Detecting Diseases in Tomato Plants

Yan, Ziyi (2024) How Well Can MobileNetV3 Perform in Detecting Diseases in Tomato Plants. Masters thesis, Dublin, National College of Ireland.

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Abstract

In this study, we used the PlantVillage dataset to assess the performance of the MobileNetV3 model in identifying diseases in tomato plants. Our foremost objective is to develop a model that can not only achieve high accuracy but also operates systematically, making it practical for deployment in agricultural sectors with limited hardware resources. We’re aiming to enhance the models diagnostic capabilities by employing transfer learning optimisation. We evaluated the performance of this model by using recall, accuracy, precision and F1 score. This research aims to highlight and prove the potential for integrating deep learning with agriculture technology to promote sustainable farming practices through better disease detection.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Basilio, Jorge UNSPECIFIED
Uncontrolled Keywords:	MobileNetV3; Transfer learning; Plant disease Detection; Convolutional Neural Networks
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science S Agriculture > SB Plant culture H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Agriculture Industry S Agriculture > S Agriculture (General) > Farming Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 Aug 2025 12:27
Last Modified:	26 Aug 2025 12:27
URI:	https://norma.ncirl.ie/id/eprint/8650

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