Rice-Grain-Images-Classification-with-CNN

In my Rice Image Classification Project, I explored the capabilities of neural networks to tackle the intricate task of categorizing various images of rice into distinct subgroups. By utilizing advanced machine learning techniques and the power of convolutional neural networks (CNNs), I engineered a model capable of discerning subtle differences between rice grain images. The project involved preprocessing a vast dataset of rice images to ensure optimal model training conditions. I then carefully designed and tuned a CNN to accurately identify and classify the rice images into their respective subgroups based on characteristics such as size, shape, and texture. The outcome was a robust classification system which had 99.3% accuracy that could potentially streamline quality control processes in the agriculture industry, showcasing my skills in both machine learning model development and practical application to real-world challenges.