Enhancing Tomato Fruit Detection and Counting Through AI-Enabled Agricultural Innovations

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Author(s): S Gandhimathi Alias Usha (Velammal College of Engineering and Technology, Madurai, India)
Copyright: 2023
Pages: 13
Source title: Machine Learning and Deep Learning for Smart Agriculture and Applications
Source Author(s)/Editor(s): Mohamamd Farukh Hashmi (National Institute of Technology, Warangal, India)and Avinash G. Kesakr (Visvesvaraya National Institute of Technology, India)
DOI: 10.4018/978-1-6684-9975-7.ch005

Keywords: Agricultural and Food Sciences / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

This chapter aims to enhance tomato fruit detection and counting in agricultural practices through AI-enabled innovations. Traditional manual methods for fruit detection and counting are labor-intensive and time-consuming. By exploiting AI technologies, such as computer vision and machine learning algorithms, this research proposes an automated system to accurately detect and count tomato fruits in real-time. The system utilizes image processing techniques and trained models to analyze images or videos captured in the field. The proposed approach has the potential to significantly improve efficiency, reduce costs, and increase accuracy in tomato fruit detection and counting, thereby benefiting the agricultural industry.

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Enhancing Tomato Fruit Detection and Counting Through AI-Enabled Agricultural Innovations

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