Smart Fruit Quality Evaluation Portal Using Deep Neural Vision and Flask Framework

Traditional fruit quality grading is manual, slow, and prone to errors. With the rise of agri-tech, AI and computer vision have emerged as powerful tools to automate this process. This research presents a Smart Fruit Quality Assessment Portal that employs convolutional neural networks (CNNs) and a Flask-based cloud backend to categorize fruit images into groups such as "Fresh," "Ripening," or "Spoiled." Users upload images via a mobile-friendly web interface, and the system trained on diverse fruit datasets analyzes quality features like color and defects. Containerized backend services ensure fast, scalable performance. The model achieved 94% accuracy, with precision and recall over 90%. Designed for ease of use across devices, this platform provides a reliable, real-time solution for modernizing post-harvest quality assessment.