Maize crop disease detection using NPNet-19 convolutional neural network

Convolutional neural network, a strong deep learning technique, is used to detect diseases and perform image processing, recognition, and disease classification. The neural network is a breakthrough in technology that can process large sets of images in both 2D and 3D. In this paper, a novel Deep CNN model named NPNet-19 is proposed to determine maize crop infections. The model's accuracy and robustness are tested using an enhanced dataset of 15,960 images to classify six disease classes and one healthy class. The primary data source for testing the model has been acquired from the maize fields situated in the state of Telangana. The model was trained using images from the Plant Village and Kaggle repositories. On training datasets, the learning model showed an accuracy of 97.51%, and on testing datasets, it achieved an accuracy of 88.72%. The proposed model is empirically compared with pre-trained models such as DenseNet-121, Inception V2, ShallowNet-8, and CNN-SVM, and it showed a 10.57, 1.74, 2.15, and 1.1% improvement in classification accuracy, respectively. When compared to transfer learning models including Modified LeNet, DICNN, SoyNet, Adaptive CNN, 9-layer, and 13-layered architectures, the proposed model showed a 10.12, 14.52, 8.17, 3.88, 7.25, and 3.39% improvement in classification accuracy, respectively. Classification Prediction Analysis also has been used for statistical analysis of prediction data on testing and training images using error metrics such as RMSE, MSE, and MAE. It is observed that the performance of the proposed model is better in the detection of real-time diseases and their classification.