The SWASTHA, AI-ML-powered quality testing model is a game-changing innovation designed to revolutionize how fruits and vegetables are assessed. By leveraging advanced image processing techniques, this model brings accuracy and transparency to agricultural trade. It plays a vital role in the SWASTHA farmer-to-consumer delivery app, empowering farmers, sellers, and buyers with reliable, objective quality checks and reducing reliance on manual inspections.

Key Features

• Image-Based Quality Analysis: The model uses high-resolution images to identify surface defects, color variations and size inconsistencies in produce.
• AI-Powered Classification: By employing cutting-edge deep learning models (CNN architecture), the system efficiently grades produce into quality categories.
• Real-Time Assessment: Instant analysis enables quick decision-making for both farmers and buyers, minimizing delays.
• User-Friendly Design: With its simple and intuitive interface, the model is accessible even in rural areas, making it farmer-friendly.

Technical Details:

• Frameworks: The model is built using TensorFlow, OpenCV and Keras, ensuring robust performance and adaptability.
• Dataset: A comprehensive dataset featuring a wide variety of fruits and vegetables of varying quality levels was used to train the model.
• Model Architecture:
  • Utilizes a Convolutional Neural Network (CNN) to extract features and classify produce effectively.
  • Supports multi-class classification to categorize produce into quality grades such as export market quality, domestic market quality and rejected.
• Preprocessing Techniques: Input images are resized, normalized, and augmented to enhance the model's performance and generalization capabilities.
• Evaluation Metrics: The model achieved impressive accuracy, with precision and recall tailored to real-world agricultural scenarios.

Use Cases:

1. Farmers: Farmers can assess the quality of their produce on-site, ensuring fair pricing in the market.
2. Buyers: Buyers gain confidence in their purchases, thanks to verified quality grades.
3. Supply Chain Optimization: The model helps identify subpar produce early, reducing waste and improving logistics efficiency.

Conclusion

The SWASTHA quality testing model highlights how AI can transform agriculture. By providing accurate, real-time quality assessments, it strengthens the relationship between farmers and consumers, fostering a sustainable and trustworthy agricultural ecosystem.

Future Scope:

• Drone Integration: Expanding capabilities to include drone-based image capture for large-scale quality assessments.
• Internal Defect Detection: Incorporating spectral imaging to identify internal defects in produce.
• Global Usability: Enriching the dataset to accommodate diverse types of produce worldwide.

This model stands as a testament to how technology can address real-world challenges, ensuring better outcomes for all stakeholders in the agricultural supply chain.