Grain Quality Testing Using Neural Networks

Abstract

Grain quality plays a crucial role in agriculture, affecting health, economic efficiency, and market pricing. Traditional manual testing methods are often slow, inconsistent, and labour-intensive. This project presents an automated system that leverages image processing and machine learning to transform grain quality assessment and price estimation, offering direct benefits to farmers and stakeholders throughout the supply chain. High-resolution images of grains, captured under controlled lighting conditions, are processed to extract essential features such as size, color, shape, and texture. These are the key features for identifying grain types and evaluating quality metrics like moisture content, protein levels, and impurities. The system utilizes a Multilayer Perceptron (MLP) as its final model for classification and analysis. Trained on extensive datasets of labelled images, the MLP ensures precise grain classification, impurity detection, and quality evaluation. Furthermore, it estimates grain prices based on these quality metrics, enabling farmers to receive fair compensation for their produce and make informed decisions regarding market timing and quality enhancement. By automating grain testing and pricing, this non-destructive approach eliminates human error, preserves grain integrity, and offers a reliable, efficient, and scalable solution. Farmers benefit from quicker quality assessments, enhanced pricing transparency, and reduced reliance on manual testing, empowering them to optimize productivity and profitability in a competitive market.