Deep Learning-Based Fruit Detection and Ripeness Assessment

Authors

  • Amruta A. Mali PG -ENTC Engineering, Ashokrao Mane College of Engineering, Vathar, Maharashtra India. Author
  • Dr. Seema Patil Associate Professor-ENTC Engineering, Ashokrao Mane College of Engineering, Vathar, Maharashtra India. Author
  • Suraj S. Shinde Assistant Professor- Electrical Engineering, Sahakar Maharshi Shankarrao Mohite Patil Institute of Technology and Research Shankarnagar, Akluj, Maharashtra, India. Author

DOI:

https://doi.org/10.47392/IRJAEM.2025.0402

Keywords:

Fruit Detection, Ripeness Classification, Deep Learning, YOLOv5, Convolutional Neural Networks (CNNs), Smart Agriculture

Abstract

Ensuring the quality and freshness of fruits is a critical challenge in modern agriculture and the post-harvest supply chain. Traditional manual methods of fruit detection and ripeness assessment are often labor-intensive, subjective, and prone to inconsistencies, leading to significant post-harvest losses and inefficiencies. In recent years, advancements in artificial intelligence, and intense learning, have shown promising capabilities in automating these processes with high precision and consistency. This paper presents a robust deep learning-based system for automated fruit detection and ripeness classification using image data. The proposed approach employs convolutional neural networks (CNNs) for ripeness classification and a YOLOv5 model for real-time fruit detection. A comprehensive dataset comprising various fruits at different ripeness stages and will be augmented to improve model generalization. The system train and validate using a stratified dataset split, and evaluates the performance using standard metrics such as accuracy, precision, recall, F1-score, and Mean Average Precision (mAP). The results demonstrate the effectiveness of deep learning in accurately identifying fruit types and determining their ripeness stages, offering a scalable solution for smart farming and quality control in the agricultural supply chain.

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Published

2025-07-25