# 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  
In addition, diseases in plant can be detected by extracting the shape features of leaves. [PERSON] et al. [52] used the number of pixels in the disease-affected area to detect the rot disease in betel leaf. Firstly, they converted the acquired RGB image into the HSV color space. Then, a threshold value was calculated by applying [PERSON]'s method on the \"H\" component of the HSV color space for segmentation purpose. The segmented binary image consisted of the rotten area with white pixels. They calculated the total number of pixels in this rotten portion to detect the affected disease. [PERSON] et al. [53] used color and shape features for the detection of rice diseases such as leaf brown spot, rice blast, sheath rot, bacterial blight, etc. For segmentation purpose, they used a Fermi energy based technique and genetic algorithm (GA) for the extraction of a diseased leaf's shape features. Fermi energy based region extraction had an advantage over selecting a proper threshold value. For classification purpose, they used a rule generation technique. The advantages of the implemented method included a smaller computational complexity as it did not require a gain calculation of the rules. [PERSON] et al. [36] used both shape and texture features for detecting diseases in the rice plant. After segmentation, shape features such as area, perimeter, long axis length and width, along with texture features including contrast, uniformity, entropy, inverse difference, linearity and correlation were extracted from the GLCM of each image of a leaf from every orientation angle. For classification, they used an SVM classifier and got an accuracy rate of 97.2%. Though this method was effectively able to identify the diseases, it failed to correctly identify diseases having a similar texture, and thus performance decreased. \begin{table}
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* In this table, NA indicates the information is not available.  
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Table 2: Summary of implemented details along with features based on color based method. The method proposed by [PERSON] et al. [33] aimed to detect and recognize potato diseases (late blight, early blight, healthy leaf). First, they masked out the background as well as the green region of the image using thresholding, by analyzing the color and luminosity component of a different region in L*a*b* color spaces. Then, they extracted the ROI which contained the disease-affected region. They used the GLCM to extract texture features such as correlation, contrast, homogeneity and energy. The mean, entropy, standard deviation, skew and energy were calculated from the histograms of the color planes. A multiclass SVM was used for the classification of potato diseases from the PlantVillage dataset. In the segmentation, it was difficult to set the threshold value in their method and the number of training image was also small. One of the disadvantages included that the images in the dataset were captured on a uniform background. [PERSON] et al. [54] developed a disease segmentation algorithm to identify the diseased area by using the distribution of intensities in the histogram and finding the threshold according to the position in the histogram. Furthermore, they [31] identified the diseases in cotton plants using an SVM classifier. A set of features such as shape, color, texture and fractional dimension was extracted from the diseased area, and they obtained an accuracy rate of 93.1%. One of the disadvantages was that the extraction of features was time-consuming and identifying the proper set of features from a set of 54 features was challenging. The number of images in the dataset was small. In [55; 56], the authors identified different cotton plant diseases using color, shape and texture features. [PERSON] et al. [55] used K-means clustering for the segmentation and a wavelet transform for the feature extraction. To reduce the number of features and speed up the computation, a PCA was used as a feature reduction technique.