Abstract: Grading of pulses is a critical step in maintaining quality control within the agricultural and food processing industries. Pulses, which include various legumes like lentils, chickpeas, and beans, are widely consumed for their high nutritional value. Ensuring the quality of these products before they reach the consumer is essential. Traditionally, the grading process has been carried out manually by experts who visually inspect the pulses for features such as size, color, shape, and presence of defects. However, manual grading is often laborintensive, time-consuming, inconsistent, and susceptible to human error and fatigue.
To overcome these limitations, the use of automated grading systems based on image processing techniques has gained significant attention. Image processing offers a non-invasive, efficient, and repeatable method for analyzing the physical characteristics of pulses. High-resolution images of the pulses are captured using cameras, and advanced digital image processing algorithms are applied to extract features such as area, aspect ratio, perimeter, color histogram, and surface texture. These features are then analyzed using rule-based systems or machine learning models to classify the pulses into different quality grades, commonly labeled as Grade A, B, and C.
Grade A pulses typically exhibit uniform size, regular shape, consistent color, and minimal surface defects. Grade B may show minor irregularities, while Grade C usually includes broken or discolored grains and visible defects. Automated systems can be trained to recognize these patterns with high precision, reducing variability and enhancing the objectivity of the grading process.
In addition to improving accuracy and consistency, automated pulse grading significantly reduces the time and manpower required for large-scale inspections. This is particularly beneficial for industries handling vast quantities of pulses where rapid and reliable quality assessment is crucial for productivity and profitability. Furthermore, digital records of graded batches can be maintained for traceability and quality auditing.
The integration of image processing in pulse grading not only boosts operational efficiency but also supports farmers and suppliers by ensuring fair pricing based on product quality. It aligns with modern agricultural practices that emphasize technology-driven solutions for quality assurance and sustainability.
In conclusion, automated grading of pulses using image processing is a transformative innovation in agri-tech. It addresses the limitations of manual grading by offering a faster, more consistent, and accurate method for quality assessment. As technology advances, such systems are expected to become more accessible and widely adopted across the pulse processing industry.
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DOI:
10.17148/IJARCCE.2025.14693
