Rice Grain Quality Analysis

The Story

The client aimed to introduce an innovative solution to the rice industry to simplify the process of analyzing rice grain quality. Their goal was to reduce the time and effort required to obtain accurate lab results by using a more efficient, automated method, replacing the traditional labor-intensive procedures.

The client specializes in providing a wide range of pumps for domestic and industrial use, offering fast delivery services worldwide and partnering with top brands like Dab, Lowara, Ebara, and Grundfos.

Amit Jangid

Founder & CTO

Challenges

• > Developing a machine learning model that accurately analyzes rice grains from scanned images.

• > Extracting multiple detailed parameters like grain height, width, color, and location from the images.

• > Capturing and saving individual grain images.

• > Ensuring quick and reliable report generation with high accuracy.

The Solution

To address the challenges, we implemented the Mask R-CNN model, known for its strength in object detection and segmentation. We fine-tuned the model's parameters to optimize its performance. A desktop application was developed using Python, .NET Core 6, and SQLite, which scans rice grains and generates a CSV file with comprehensive metadata, including grain parameters and test reports, within 1-2 minutes.

The Outcome

The solution successfully reduced the time required for quality analysis, providing detailed reports within 1-2 minutes. This enhanced efficiency in the rice industry, minimizing the labor-intensive efforts previously needed to obtain accurate results and significantly improving the overall quality measurement process.