Multiclass classification of nutrients deficiency of apple using deep neural network

Abstract

Agriculture industry is the foundation of Indian economy where quality fruit production plays an important role. Apple or pome fruits are always in demand because of rich nutrients in it. Hence, to analyze and recognize the nutrients deficiency in fruits, a deep neural-based model is being proposed. This model automatically classifies and recognizes the type of deficiency present in apple. In this paper, a database has been created for four major types of nutrients deficiency in apples and used for training and validation of the proposed deep convolutional network. The model is tuned with k-fold cross-validation. The hyper-parameters such as epoch are set at 100 and batch size kept at 5. Finally, the model is tested with the testing data and achieved an average accuracy of 98.24% with k-fold cross-validation set to 15. The model accuracy depends on the hyper-parameters. The process of features optimization reduces the risk of overfitting of the model. Hence, careful selection of hyper-parameters is important for the convergence of cost function to the global minima that results in minimum misclassification.