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Related Experiment Video

Updated: Jul 16, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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Tomato Fruit Detection Using Modified Yolov5m Model with Convolutional Neural Networks.

Fa-Ta Tsai1, Van-Tung Nguyen1, The-Phong Duong2

  • 1Department of Mechanical Engineering, National United University, Miaoli 36002 Taiwan.

Plants (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

Automated harvesting systems are crucial for the farming industry. This study developed an efficient tomato detection model using Yolov5m with advanced neural networks, achieving high accuracy for ripe, immature, and damaged fruits.

Keywords:
Yolov5convolutional neural networktomato fruit detection

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • The agricultural sector faces significant challenges with labor-intensive and inefficient harvesting processes.
  • There is a growing need for automated systems to improve efficiency and reduce costs in fruit harvesting.

Purpose of the Study:

  • To propose and evaluate object classification models for the automatic detection of tomato fruit.
  • To enhance the development of automated harvesting systems for the farming industry.

Main Methods:

  • Three object classification models were developed by integrating Yolov5m with BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs).
  • Models were trained on 1508 normalized images featuring three classes of cherry tomatoes: ripe, immature, and damaged.

Main Results:

  • The Yolov5m + BoTNet model demonstrated high detection accuracies: 94% for ripe, 95% for immature, and 96% for damaged tomatoes.
  • The proposed models show significant potential for real-world application in automated harvesting.

Conclusions:

  • The modified Yolov5m + BoTNet model offers a promising foundation for developing advanced automated tomato harvesting systems.
  • This research contributes to addressing the challenges of manual labor in fruit harvesting through intelligent automation.