Automated Grape Counting with Deep Learning on Big Data

Kesavan, Naveen (2023) Automated Grape Counting with Deep Learning on Big Data. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research paper introduces an approach to address the challenge of efficient grape counting in extensive datasets using computer vision and deep learning techniques. The primary motivation behind this study is to develop a system capable of accurately counting individual grapes within images, with specific emphasis on aiding grape crop management in large agricultural areas within developing nations. Leveraging convolutional neural networks and a meticulously annotated dataset, our proposed system demonstrates remarkable proficiency in grape detection and categorisation. The system operates within a cloud-based infrastructure, ensuring accessibility to users across diverse geographical locations. The real-time processing capability of the cloud-based setup is particularly crucial for precision agriculture applications, offering a feasible solution for managing extensive datasets. Notably, this automated grape counting mechanism is uniquely positioned to benefit the farming community in resource-constrained settings. Rather than a universal solution, it is tailored to address the needs of impoverished farmers in developing nations who collaboratively share computing resources within a communal framework. By embracing deep learning for automatic fruit counting, this research presents a promising avenue for enhancing agricultural practices. The system’s ability to provide reliable and time-efficient grape counting methods holds significant potential for driving improvements within the farming sector. This work underscores the value of community-oriented solutions in addressing agricultural challenges while harnessing the power of emerging technologies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Lugones, Diego UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Agriculture Industry T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Tamara Malone
Date Deposited:	20 Sep 2024 17:18
Last Modified:	20 Sep 2024 17:18
URI:	https://norma.ncirl.ie/id/eprint/7064

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