Deep learning model for improving the rice plant disease detection performance
By: Shrivastava, Gaurav
.
Contributor(s): Barua, Kuntal.
Publisher: Chennai ICT Academy 2022Edition: Vol.13(1), Oct.Description: 2775-2781p.Subject(s): Computer EngineeringOnline resources: Click here
In:
ICTACT Journal on Soft Computing (IJSC)Summary: Rice is one of the most utilized grains in India. It is a seasonal crop
which mostly grows between June to October. This crop mostly grows
in natural conditions and its production has a significant influence on
different diseases in the plant. Early stage detection of diseases can help
in improving the production. In this paper, an analysis and study on
deep learning models for getting accurate rice plant disease detection
is presented. In this context, first the recent contributions on detecting
the diseases by analysing the plant leaf images are reviewed. Then, a
comparison among sequential model and 2D-CNN model has been
performed. The experimental analysis demonstrates that 2D-CNN
outperforms as compared to the simple sequential model. The
experiments are extended by including the different image feature
selection models. In order to extract features, sobel based edge
detection, Local Binary Pattern (LBP) based texture analysis and their
combinations i.e. sobel and LBP, Sobel, LBP and color, and a
combination of color and sobel are used. The experiments are
performed on Kaggle based rice plant disease detection dataset and the
performance in terms of precision, recall, f1-score and accuracy has
been measured. The experimental evaluation highlights two major
points (1) the CNN does not require additional features for better
classification consequences (2) the highly trained models are able to
respond faster as compared to less trained models. Based on the
obtained performance, a more accurate model for plant disease
detection is designed.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0514 |
Rice is one of the most utilized grains in India. It is a seasonal crop
which mostly grows between June to October. This crop mostly grows
in natural conditions and its production has a significant influence on
different diseases in the plant. Early stage detection of diseases can help
in improving the production. In this paper, an analysis and study on
deep learning models for getting accurate rice plant disease detection
is presented. In this context, first the recent contributions on detecting
the diseases by analysing the plant leaf images are reviewed. Then, a
comparison among sequential model and 2D-CNN model has been
performed. The experimental analysis demonstrates that 2D-CNN
outperforms as compared to the simple sequential model. The
experiments are extended by including the different image feature
selection models. In order to extract features, sobel based edge
detection, Local Binary Pattern (LBP) based texture analysis and their
combinations i.e. sobel and LBP, Sobel, LBP and color, and a
combination of color and sobel are used. The experiments are
performed on Kaggle based rice plant disease detection dataset and the
performance in terms of precision, recall, f1-score and accuracy has
been measured. The experimental evaluation highlights two major
points (1) the CNN does not require additional features for better
classification consequences (2) the highly trained models are able to
respond faster as compared to less trained models. Based on the
obtained performance, a more accurate model for plant disease
detection is designed.
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