Tomato Classification Using EfficientNet-B7

Tomato quality analysis is a complex task in the agricultural market for ensuring quality of products in consumer markets. In this study, we propose tomato classification using EfficientNet-B7, a pretrained model. The framework was built to categorize tomatoes into three categories that are ripe, unripe, and reject with an accuracy of 98.4%. Previous research has investigated hybrid approaches such as CNN-SVM and Efficient-NetB0, for the classification of agricultural products. This study builds on this foundation by employing EfficientNet-B7 to develop a more streamlined end-to-end solution for tomato classification. The results demonstrated the potential of deep learning models to transform quality control systems in the food industry.

• Publication year

  2025

• Venue

  2025 IEEE Madhya Pradesh Section Conference (MPCON)

• Publication date

  2025-08-29

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/MPCON66082.2025.11256563
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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