ICTACT Journal on Soft Computing

Gaurav Shrivastava ¹, Harish Patidar ¹

Affiliations
1 Department of Computer Science and Engineering, Sage University, IN

Abstract

In this paper, we propose a decision support system for Indian rice farmers for identifying diseases. In a country like India, food security is an essential concern. Additionally, diseases in plants can cause a significant loss. Early-stage detection of diseases can help in improving the production of rice. In this context, first we investigate the recent contributed efforts in the field of plant disease detection by analysing plant leaves using machine learning and image processing techniques. Next, the datasets and relevant algorithms are concluded. Then, a machine learning model has been presented. The model includes the edge feature extraction using canny edge detection technique, colour features are extracted using grid colour movement, and the texture analysis is performed using Local Binary Pattern (LBP). In the next step, using the extracted features, we have prepared a combined feature vector to train the Machine Learning (ML) algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). These machine learning algorithms are organized in such a manner that the proposed decision support model can identify and differentiate the leaf plants. Additionally, it also recognizes the rice plants when we query. Secondly, the model is also able to recognize rice plant diseases. The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five different diseases as class labels. The experimental study with the implemented system confirms the superiority of ANN to be used with the proposed decision support system as compared to the SVM algorithm in terms of accuracy and time consumption. Finally, future work has also been highlighted.

Keywords

Plant Disease Detection, Machine Learning, Image Processing, Food Security, Early Disease Detection

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Gaurav Shrivastava ¹, Kuntal Barua ¹

Affiliations
1 Department of Computer Science and Engineering, Sage University, IN

Abstract

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.

Keywords

Plant Disease Detection, Machine Learning, Image Processing, Food Security, Early Disease Detection

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