Image-based wheat grain classification using convolutional neural network - Lingwal, Surabhi; Bhatia, Komal Kumar; Tomer, Manjeet Singh (Multimedia tools and applications, 2021)
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.