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

Abstract

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.

Introduction

• Global demand for fresh, high-quality fruits has increased due to rising consumer expectations, export standards, and retail requirements.

• Traditional manual fruit grading by human inspectors is inconsistent, subjective, and unsustainable for large-scale operations.

• Inconsistencies in grading lead to mispricing, disputes, and post-harvest losses.

2. AI-Based Solution

• Advances in computer vision and deep learning (especially Convolutional Neural Networks or CNNs) enable accurate, automated fruit quality assessment.

• AI-driven systems can outperform humans in speed, consistency, and scalability.

• Challenges in earlier AI systems included limited datasets, lack of accessibility, poor scalability, and absence of user-friendly interfaces.

3. Proposed System: Smart Fruit Quality Evaluation Portal

A cloud-based, AI-powered web platform built using:

• CNN models for image classification (e.g., Fresh, Ripening, Spoiled)

• Flask for backend API

• Responsive frontend for easy use on mobile and desktop

Key Features

• Upload fruit images via any device

• Real-time classification with confidence scores

• Trained on diverse fruit images under real-world conditions

• Scalable cloud deployment (Docker, TensorFlow)

• Compatible with IoT and supply chain systems

4. Literature Survey Highlights

• Early methods: Thresholding and grayscale image processing (Sharma & Kumar)

• SVM-based texture analysis (Patel & Roy)

• Color histogram approaches (Thomas & Reddy)

• Deep learning with CNNs (Verma & Tiwari) marked a turning point

• Cloud-hosted APIs, IoT sensor integration, and offline mobile apps evolved the field further

• Recent innovations include multispectral imaging, MobileNet models for edge use, and supply-chain-focused AI systems

5. Methodology

A. Dataset

• Sources: Kaggle, public archives, and custom smartphone captures

• Labels: Fresh, Ripening, Overripe, Spoiled

• Balanced dataset with diverse fruits (e.g., apples, bananas, oranges, mangoes)

B. Preprocessing & Augmentation

• Image resizing (224x224), color normalization, background segmentation

• Augmentations: rotation, flip, brightness adjustment, zoom

C. CNN Model Architecture

• Layers: Convolution + Max pooling + Fully connected

• Training with cross-entropy loss, Adam optimizer

• Achieved 94% accuracy on test set

D. Backend (Flask + Docker)

• Handles image upload, preprocessing, prediction

• Deployed on cloud with auto-scaling and low latency

E. Frontend Interface

• Built using HTML5, Bootstrap, JavaScript

• Features:

  • Simple upload (drag/drop, camera capture)

  • Color-coded results

  • Downloadable reports

6. Evaluation & Results

A. Classification Accuracy

• 94% accuracy across fruit types and quality levels

• Ensures consistent, scalable grading and fair pricing

B. Precision, Recall, F1-Score

• High scores across classes like Spoiled and Fresh

• Reduces false positives/negatives—vital for reducing food waste

C. Inference Time

• Average of 1.8 seconds per image

• Supports real-time use in farms, markets, and supply chains

D. Usability Testing

• Tested by 20+ non-technical users

• 93% user satisfaction on ease of use, mobile compatibility, and clarity

7. Key Benefits

• ? Objective and consistent grading

• ? Reduces post-harvest losses and disputes

• ? Accessible to non-technical users (e.g., farmers, vendors)

• ? Scalable and real-time—ideal for integration with supply chain systems

Conclusion

This research presented the development and evaluation of the Smart Fruit Quality Evaluation Portal, an internet-based platform that employs deep learning and cloud technologies for automating fruit grading. The portal addresses key limitations of traditional manual grading methods, including subjectivity, time consumption, and inconsistency particularly when deployed at scale. Through incorporating a convolutional neural network (CNN) for classifying images, a Flask-based backend for secure and scalable processing, and a responsive frontend interface, the system enables real-time, accurate, and accessible fruit quality evaluation. The portal allows users to upload images of fruits through a mobile-friendly web interface. These images undergo preprocessing and are analyzed by a trained CNN model that categorizes them into quality classes such as Fresh, Ripening, or Spoiled. Results are returned with confidence scores, and every prediction is logged with metadata for future analysis. The evaluation of the system demonstrated a 94% classification accuracy, high F1-scores across quality categories, fast response times (average inference time of 1.8 seconds), and strong user satisfaction, confirming its practical effectiveness in both technical and non-technical environments. In solving the core problem of inconsistency in manual grading, the portal offers a scalable and objective approach to post-harvest fruit quality control. Its accessibility, responsiveness, and modular architecture make it deployable across farms, sorting centers, and marketplaces, contributing to improved traceability, better pricing accuracy, and reduced waste in the agricultural supply chain.

References

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Copyright

Copyright © 2025 Archana Nadagouda, Dr. Girish Kumar D. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.