# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 3 Different Existing Machine Learning Based Techniques for Plant Disease Detection
### Shape- and Texture-Based Disease Detection
[69] identified three different rice plant diseases using color, shape and texture features. Before extracting the features, they used different background removal and segmentation techniques and for the classification they used an SVM and achieved an accuracy rate of 83.80% and 88.57% using 5- and 10-fold cross-validations, respectively. Three different segmentation techniques were used, namely, [PERSON]'s segmentation, LAB-color-space-based K-means clustering, and HSV-color-space-based K-means clustering. Among the used techniques, the HSV-color-space-based K-means clustering performed better. To evaluate the model performance, they extracted 88 features and built three different models with different feature combinations. [PERSON] et al. [32] proposed a novel approach to identify the plant diseases using mobile devices. They used CIE's L*a*b*-color-based unsupervised segmentation technique. A Gabor wavelet transform (GWT) and the GLCM were used to represent the image mathematically and they achieved a maximum accuracy of 96.3% using a KNN classifier. The issues regarding their paper were that they considered only uniform background images. Segmenting the leaf images captured in a complex background with different lighting conditions may be a challenging issue for their method. The feature vector used was very dense and the computation cost was high. [PERSON] et al. [60] identified the diseases in five steps as follows. First, they took the input image, preprocessed the image followed by masking the green pixel, then segmented the diseased area using GA; finally, they computed the feature using the co-occurrence matrix and classified using an SVM classifier. We summarize the shape- and texture-based methods as follows:  
* Before the extraction of features, a lot of preprocessing is required, which makes the model complex. * Segmenting the diseased region from the images with a background object is challenging. * The extraction of features and selecting the proper set of feature set giving the optimal result is an important issue. * The dataset used in the majority of the paper is small and only a few disease categories is considered. * The extraction of features in a large dataset is time-consuming as well as a laborious task. Table 3 summarize the different segmentation techniques that were used to segment the diseased region along with their advantages and disadvantages. Table 4 summarizes the detection of diseases in plants using shape- and texture-based features with the preprocessing techniques, classifier and dataset used. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Preprocessing** & **Features** & **Classifier** & **Dataset** & **Accuracy (\%)** \\ \hline [PERSON] et al. [56] (2009) & Resizing, [PERSON]’s segmentation method, fill the hole & Area, perimeter, & & 216 & \\  & & GLCM, & & captured & 97.2 \\ \hline [PERSON] et al. [31] (2009) & Color transformation, Gaussian filter, thresholding-based segmentation & Color features, & & \(117\) & \\  & & & shape features, & captured & 93.1 \\ \hline [PERSON]’s [35] (2009) & Thresholding, Sobel Edge detection & Color differences, area, bandpass, shape complexity, length and concavity, longer axis, shorter axis & Membership function (MF) & 50 & 70 \\ \hline [PERSON] et al. [38] (2010) & Color transformation, K-Means clustering-based segmentation & Angular moment, & & \\  & & mean intensity level variation, & & \\  & & correlation, contrast, & & captured & 93 (precision) \\ \hline [PERSON] et al. [56] (2011) & Thresholding-based segmentation & Color features, shape features, texture features & NA & 200 & 95.16 \\ \hline [PERSON] et al. [70] (2013) & Color transformation, masking