Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network

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...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2020-11, Vol.11 (11), p.5703-5711
Main Authors: Sethy, Prabira Kumar, Barpanda, Nalini Kanta, Rath, Amiya Kumar, Behera, Santi Kumari
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Artificial neural networks
Automation
Classification
Classifiers
Computational Intelligence
Crop diseases
Crops
Deep learning
Dietary minerals
Discriminant analysis
Engineering
Farmers
Image classification
Leaves
Machine learning
Medical diagnosis
Nitrogen
Original Research
Phosphorus
Plant diseases
Rice
Robotics and Automation
Statistical analysis
Support vector machines
User Interfaces and Human Computer Interaction
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Summary: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.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-020-01938-8