PRUTAS: Proactive Recognition Using Transfer Learning for Assessing Spoilage of Fruits

@article{1705882,
      author = {Justin Ann Hernandez, Johani D. Basaula, Ryrhen Christian Ara?es, Carolyn Banal},
      title = {PRUTAS: Proactive Recognition Using Transfer Learning for Assessing Spoilage of Fruits},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {12},
      pages = {32-39},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705882.pdf},
      abstract = {The increasing global concern over food waste and safety necessitates innovative solutions for the timely detection and management of spoiled fruits. This study introduces Prutas a mobile application that uses advanced image recognition techniques to classify the spoilage of apples, oranges, and bananas. Prutas takes a multi-model approach, combining image classification, detection, and segmentation models based on the MobileNetV3 architecture. A large dataset of fruit images is enhanced and used to train and evaluate the models. The application's effectiveness was evaluated using rigorous testing and user studies, demonstrating its potential impact on reducing food waste and improving food safety. Prutas makes a significant contribution to food technology by providing a scalable and accessible solution for detecting fruit spoilage in both residential and commercial settings.},
      keywords = {Food Waste Reduction, Fruit Spoilage Detection, Image Classification, Image Recognition, Machine Learning, Mobilenetv3.},
      month = {June},
  }