Aiml-based Quality Assurance System For Agricultural Products Using Tensorflow

• Author(s):

  Aditya Kundlik Hande | Aditya Hande | OMKAR PALAVE | VISHWAJEET JAGTAP | VINIT CHAKANE

• Keywords:

  Agricultural Automation, Computer Vision, Convolutional Neural Networks (CNN), Deep Learning, Fertilizer Optimization, Spatial Feature Extraction, TensorFlow.

• Abstract:

  Agricultural Product Quality Assessment Plays A Critical Role In The Global Food Supply Chain, Directly Impacting Market Pricing, Consumer Safety, And Post-harvest Optimization. Traditional Methods Rely Heavily On Human Vision And Manual Sorting, Which Are Inherently Subjective, Slow, Prone To Error, And Difficult To Scale. To Overcome These Deep-seated Inefficiencies, This Research Presents A Comprehensive, End-to-end Multi-modular Artificial Intelligence And Machine Learning (AIML) Quality Assurance Platform Leveraging The TensorFlow Deep Learning Engine. For Seed Grading, High-resolution Spatial Feature Vectors Are Extracted From Digital RGB Images And Processed Through A Custom Multi-layer Convolutional Neural Network (CNN) Alongside Parallelized Support Vector Machines (SVM) To Classify Essential Staple Grains, Including Maize And Corn Samples, Into Definitive, Industry-standard Quality Tiers (Grade A, B, And C). Concurrently, The Architectural Framework Implements An Interactive Smart Analytical Engine Using Pythonic Data Mining Structures To Process Regional Context Parameters—such As Soil PH, Micro-climate Averages, Kahrif/rabi Seasonality, And Localized Rain Statistics—enabling Precise, Site-specific Chemical And Organic Fertilizer Recommendations (e.g., Target Phosphorus Formulations). Benchmarked Against Standard Datasets, The Deep Feature Extraction Model Achieves Cross-validated Training Accuracies Exceeding 90%, Outperforming Archaic Manual Pipelines. The Complete Infrastructure Is Integrated Within An Optimized, Cross-platform Architecture Deployable Across High-availability Web Clients And Responsive Standalone Desktop Wrappers, Running Model Inferences Smoothly In Less Than 0.65 Seconds Without Absolute Remote Server Dependencies. This Technical Ecosystem Offers An Immediate, Production-ready, Grassroots Solution To Reinforce Precision Agriculture, Limit Harvest Wastage, And Augment Supply Chain Valuation Metrics.

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Other Details

• Paper id:

  IJSARTV12I6105600

• Published in:

  Volume: 12 Issue: 6 June 2026

• Publication Date:

  2026-06-03

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