Detection and Classification of Rice Diseases: An Automated Approach Using Textural Features

Abstract

Image processing techniques are widely used for the detection and classification of diseases for various plants. The structure of the plant and appearance of the disease on the plant pose a challenge for image processing. This research implements SVM (Support Vector Machine) based image-processing approach to analyze and classify three of the rice crop diseases. The process consists of two phases, i.e. training phase and disease prediction phase. The approach identifies disease on the leaf using trained classifier. The proposed research work optimizes SVM parameters (gamma, nu) for maximum efficiency. The results show that the proposed approach achieved 94.16% accuracy with 5.83% misclassification rate, 91.6% recall rate and 90.9% precision. These findings were compared with image processing techniques discussed in review of literature. The results of comparison conclude that the proposed methodology yields high accuracy percentage as compared to the other techniques. The results obtained can help the development of an effective software solution by incorporating image processing and collaboration features. This may facilitate the farmers and other bodies in effective decision making to efficiently protect the rice crops from substantial damage. While considering the findings of this research, the presented technique may be considered as a potential solution for adding image processing techniques to KM (Knowledge Management) systems.

Diagnosis of Leaf Surface Disease Using Two Datasets of Tomato and Rice Obtained from Image Processing Techniques

“…In this section, the proposed method is compared with other methods. The accuracy of the proposed method using data sets of rice has been compared with CNN methods [13], Decision Tree-based Machine Learning Algorithms [14] and improved random forest [15], Detection and Classification of Rice Plant Diseases [26], Deep Neural Network Algorithm [27], An Approach Using Textural Features [28], and tomato data set has been compared with Computer Vision Based Detection and Classification [16], Automatic Tomato Plant Leaf Disease and pests [17], Deep Neural Network-Based Tomato Plant Diseases [18],Tomato plant disease classification methods in digital images [29], Automatic Tomato Plant Leaf Disease [30].…”

“…It should be noted that the data sets of the methods compared for both rice and tomato diseases are the same as the proposed method, and therefore the experiments are the same in terms of data sets. 93.3% [14] 76.19% [15] 97.80% [26] 93.33% [27] 96% [28] 94.16% Proposed Method 99.12%…”

Multi-Granularity Neural Network Encoding Method for Land Cover and Land Use Image Classification

“…There are many applications for this on earth and another common application for regression or estimating physical quantities from remote sensing. An example very commonly used in agriculture for estimating yields [35] for different types of crops from remote sensing data such as maize yield being assessed. The second example is Greg Adler's lab [36], where they evaluate the growth carbon density directly from remote sensing data.…”

A Novel IoT Enabled System for detection of Infected Leaf for Smart Agricultural

“…The all around high achievement rate makes the model an incredibly obliging rebuke or early advice instrument, and a way of thinking that could be moreover loosened up to help an arranged plant sickness perceiving proof framework to work in guaranteed improvement conditions. Komal [18], This paper presents, Image dealing with strategies are broadly utilized for the exposure and strategy of diseases for different plants. The structure of the plant and proximity of the contamination on the plant address a test for picture arranging.…”