Deep feature based rice leaf disease identification using support vector machine

By Dr. Prabira Kumar Sethy, N. Barpanda, A. Rath, Santi Kumari Behera. Computers and Electronics in Agriculture, 2020

519 citations recorded by the source corpus.

Abstract Features are the vital factor for image classification in the field of machine learning. The advancement of deep convolutional neural network (CNN) shows the way for identification of rice diseases using deep features with the expectation of high returns. This paper introduced 5932 on-field images of four types of rice leaf diseases, namely bacterial blight, blast, brown spot and tungro. In addition, the performance evaluation of 11 CNN models in transfer learning approach and deep feature plus support vector machine (SVM) was carried out. The simulation results show the deep feature plus SVM perform better classification compared to transfer learning counterpart. Also, the performance of small CNN models such as mobilenetv2 and shufflenet was examined. The performance evaluation was carried out in terms of accuracy, sensitivity, specificity, false positive rate (FPR), F1 Score and training time. Again, the statistical analysis was performed to choose the better classification model. The deep feature of ResNet50 plus SVM performs better with F1 score of 0.9838. The fc6 layer of vgg16, vgg19 and AlexNet have better contribution towards classification compared to fc7 and fc8. Further, the F1 score of CNN classification models was compared with other traditional image classification models such as bag-of-feature, local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM.

Subfields: Analytical Chemistry, Atmospheric Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/ae2190e83f17cca145c004ba512a22eae31a0c0f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Arpit Jain
2. Abhinav Sharma
3. Prateek Gupta
4. V. Chowdary

In Plant Science

1. Arpit Jain
2. Abhinav Sharma
3. Prateek Gupta
4. V. Chowdary
5. Wubetu Barud Demilie

Citations in the corpus

• Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, background): “Sethy et al. [88] identified the rice leaf disease based on a hybrid CNN and SVM.”
• Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, methodology): “Moreover, the early and timely identification of plant diseases positively impacts crop yield and quality [1].”