India is among the largest cultivators and consumers of wheat grains leading to apparent demand for identifying the quality and varietal distribution of wheat to fulfill the specific requirements of food industries. Moreover, with the variations in prices of distinct varieties in different parts of the country, it becomes a vital need for the customers as well as for the cultivators to identify and classify the grains based upon specific end products, demand, and prices of individual variety. The growth of Machine Learning and Computer Vision in agriculture, facilitate the development of such techniques that can successfully identify the classes based on visual features and representation. In this paper, a model has been developed from scratch for the classification of fifteen different varieties of wheat consists of 15000 images based on their visual traits using Convolutional Neural Network. The model has been produced under a different set of hyper-parameters tuned to develop the best model that can classify the varieties of wheat grains with high accuracy and minimum loss. The performance of the different models are compared in terms of classification accuracy and categorical cross-entropy loss. The model which is found best, successfully classifies the wheat varieties with 94.88% training accuracy and 97.53% test accuracy while on the other side reduces loss to 15% for training and 8% for the test set. Hence, the developed model can be deployed for the classification of different grain varieties, plant diseases, plant varieties, and several other fields under agriculture.

印度是小麦谷物的最大的种植者和消费国之一，因此对识别小麦的质量和品种分布以满足食品工业的特定需求提出了明显的需求。此外，随着该国不同地区不同品种价格的变化，迫切需要客户和中耕者根据特定最终产品，需求和单个品种的价格对谷物进行识别和分类。 。农业中机器学习和计算机视觉的发展促进了此类技术的发展，这些技术可以成功地根据视觉特征和表示来识别类别。在本文中，从头开始开发了一个模型，用于使用卷积神经网络基于其视觉特征对15种不同小麦品种进行分类，该模型包含15000张图像。该模型是在一组不同的超参数下生成的，这些超参数经过调整可开发出最佳模型，该模型可以高精度，最小损失地对小麦籽粒进行分类。根据分类精度和分类交叉熵损失比较了不同模型的性能。最佳模型可以成功地对小麦品种进行分类，训练准确度为94.88％，测试准确度为97.53％，另一方面，将训练中的损失减少到15％，将测试组的损失减少到8％。因此，开发的模型可以用于对不同的谷物品种，植物病害，植物品种，