Book ChapterDOI
Rice Leaf Diseases Recognition Using Convolutional Neural Networks
Syed Md. Minhaz Hossain,Md. Monjur Morhsed Tanjil,Mohammed Abser Bin Ali,Mohammad Zihadul Islam,Md. Saiful Islam,Sabrina Mobassirin,Iqbal H. Sarker,S. M. Riazul Islam +7 more
- Vol. 12447, pp 299-314
TLDR
In this paper, a CNN-based model was proposed to recognize rice leaf diseases by reducing the network parameters, which achieved the best accuracy of 97.82% with an area under curve (AUC) of 0.99.Abstract:
The rice leaf suffers from several bacterial, viral, or fungal diseases and these diseases reduce rice production significantly. To sustain rice demand for a vast population globally, the recognition of rice leaf diseases is crucially important. However, recognition of rice leaf disease is limited to the image backgrounds and image capture conditions. The convolutional neural network (CNN) based model is a hot research topic in the field of rice leaf disease recognition. But the existing CNN-based models drop in recognition rates severely on independent dataset and are limited to the learning of large scale network parameters. In this paper, we propose a novel CNN-based model to recognize rice leaf diseases by reducing the network parameters. Using a novel dataset of 4199 rice leaf disease images, a number of CNN-based models are trained to identify five common rice leaf diseases. The proposed model achieves the highest training accuracy of 99.78% and validation accuracy of 97.35%. The effectiveness of the proposed model is evaluated on a set of independent rice leaf disease images with the best accuracy of 97.82% with an area under curve (AUC) of 0.99. Besides that, binary classification experiments have been carried out and our proposed model achieves recognition rates of 97%, 96%, 96%, 93%, and 95% for Blast, Brownspot, Bacterial Leaf Blight, Sheath Blight and Tungro, respectively. These results demonstrate the effectiveness and superiority of our approach in comparison to the state-of-the-art CNN-based rice leaf disease recognition models.read more
Citations
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Journal ArticleDOI
Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction
Prabhjot Kaur,Shilpi Harnal,Rajeev Jain D C Tiwari,Shuchi Upadhyay,Surbhi Bhatia,Arwa Adulwahab Mashat,Aliaa Mahfooz Alabdali +6 more
TL;DR: In this paper , the authors used transfer learning to retrain the EfficientNet B7 deep architecture and then used Logistic Regression to down-sampled the collected features using a logistic regression technique.
Journal ArticleDOI
A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment
Vinay Kumar Gautam,Naresh Kumar Trivedi,Aman Singh,Heba G. Mohamed,I. Noya,Preet Bandhan Kaur,Nitin Goyal +6 more
TL;DR: A Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research is proposed, concerned with the biotic diseases of paddy leaves due to fungi and bacteria.
Journal ArticleDOI
Custom Convolutional Neural Network for Detection and Classification of Rice Plant Diseases
TL;DR: In this paper , a custom CNN architecture was proposed for detecting and classifying common diseases found in rice plants by reducing the number of parameters associated with the network and the proposed CNN architecture has been trained using a dataset of four types of common rice plant diseases.
Journal ArticleDOI
Rice Leaf Disease Detection and Classification Using a Deep Neural Network
TL;DR: In this paper , the authors used an open dataset with 4 types of leaf infections, namely brown spot, blast, bacterial blight, and tungro, and compared their suggested model (custom-CNN) against pre-trained deep CNN models, and with a learning rate of 0.001, the custom-CNN model obtained greater accuracy of 97.47%.
References
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Proceedings Article
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