Abstract
Nitrogen (N) concentration is a significant parameter to check the status of health in rice crop. Nitrogen (N) plays an essential role in the growth and productivity of rice plant. This paper proposes a convolutional neural network (CNN) based approach for prediction of rice nitrogen deficiency. The pre-trained CNN architecture is modified to improve the classification accuracy with the inclusion of pre-eminent classifier like support vector machine (SVM) by replacing the last output layer of CNN. Here, six leading deep learning architectures such as ResNet-18, ResNet-50, GoogleNet, AlexNet, VGG-16 and VGG-19 with SVM are used for prediction of nitrogen deficiency with 5790 number image samples. The performance of each classifier is measured and compared in terms of accuracy, sensitivity, specificity, false positive rate (FPR) and F1 score. Again, the statistical analysis is performed to choose the better classification model considering the results of 100 independent simulations. The statistical analysis confirmed the superiority of ResNet-50+SVM than the other five CNN-based classification models with an accuracy of 99.84%. Besides, the accuracy score of CNN classification models is compared with other traditional image classification models such as bag-of-feature, colour feature + SVM, local binary patterns (LBP) + SVM, histogram of oriented gradients (HOG)+SVM and Gray Level Co-occurrence Matrix (GLCM)+SVM.
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Data availability
The nitrogen deficient rice leaf image dataset used for training and testing CNN model is available in “https://data.mendeley.com/datasets/gzm5pxntyv/draft?a=68bc492f-89ce-4c5c-9bb5-73f2bf528f4a”, and all the data generated during and/or analysed during the current study are included in the manuscript.
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We are thankful and express our sincere regards to the editors and reviewers for their constructive suggestion towards the improvement of the manuscript to a high mark.
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Sethy, P.K., Barpanda, N.K., Rath, A.K. et al. Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network. J Ambient Intell Human Comput 11, 5703–5711 (2020). https://doi.org/10.1007/s12652-020-01938-8
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DOI: https://doi.org/10.1007/s12652-020-01938-8