Application of Machine Learning to Classify Wheat Seeds

We identify the type of wheat seeds into three categories: Kama, Rosa, and Canadian, different species of wheat in the real world. To do this, we analyze features including the wheat seed’s perimeter, area, compactness, length, width, asymmetry coefficient, and groove length. Then, we transform the variables of the features into machine-readable numbers. With the data being correctly altered, Machine learning methods, including Standard Scaler, train_test_split, prepare the data by normalizing the data units, and split the data into training and testing sets to train and test a model. Finally, Categorical Classification is used to create a model that classifies wheat seed varieties. The five hundred times of iterations and tests of the model resulted in a 97% accuracy and a 0.13 loss, indicating a good performance. This model allows farmers without a huge experience in wheat identification to guess if they are examining Kama, Rosa, and Canadian wheat seeds.