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A Convolution Neural Network-Based Wheat Grain Classification System

: In most developing countries, agriculture is the primary economic sector. Seed categorization and evaluation could provide detailed information for seed germination, seed quality management, and impurity detection. Many academics are working on a range of seed image analysis systems using several categorization algorithms. India is the world's second-largest wheat grower. The importance of detecting the wheat quality cannot be overstated. Manually determining wheat quality necessitates skilled knowledge and is time demanding. The healthy and poor wheat growth is a complete indication of biological changes as well as a detecting factor for quality and yield. Hence detection of quality of Wheat Seed can be achieved through advanced technologies like Image Processing, machine learning, deep learning etc, our proposed paper includes various phases of implementation namely image processing techniques, and Deep neural network. In our model there are four classes considered they are Good grain, Damaged Grain, Broken Grain and Foreign Particles. where a convolution system is used to classify the wheat seeds and detect status of 4 distinct classes.