# 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
If a more-connected component was there, then its four neighbors corresponded to unhealthy leaf. They used an SVM classifier for classification. Training the network with real-time images was an important area that needed to be done. [PERSON] et al. [64] identified the palm oil leaf disease by extracting 13 texture features from the GLCM, and a multiclass SVM was used for classification purpose. First, they converted the RGB image to the L*a*b* color space and for segmentation purpose, they used the K-means clustering algorithm and evaluated the features from the segmented image. The main issues in their method were that it was limited to palm oil leaf disease only and the dataset used was very small. [PERSON] et al. [34] used three different types of features such as shape, color, and texture features in the detection of diseases in apple leaf. Firstly, they removed the background using a histogram, and region growing was applied to separate the diseased leaf spots from the leaf image. They extracted 38 different features and reduced the dimensionality of the feature space by selecting the most valuable features from the combination of a GA and a correlation-based feature selection (CFS). Diseases were classified using an SVM classifier and they obtained an accuracy rate above 94%. One of the advantages of the model was that the dimensionality reduction reduced the time complexity of the model. [PERSON] et al. [43] detected different fungal diseases that occur in different crops such as fruit, vegetables and commercial plants. For each category, they used different segmentation techniques to identify the diseased area, extract different features and also to identify the diseases. For fruit crops, they used K-means clustering, for vegetables, they used Chan-Vese and for commercial crops, they used a GrabCut segmentation technique. In the case of fruit crops, they used the GLCM and GLRLM for feature extraction. For classification, they used a nearest neighbor classifier and obtained an accuracy rate of 91.3%. For the identification of diseases in vegetable crops, they extracted LBP features from the disease-affected leaves and used an ANN for the classification; they obtained 95.1% accuracy. In their method, performance decreased in the case of a high variability among the diseases. [PERSON] et al. [65] designed an automatic system to identify and classify the cucumber leaf diseases. Firstly, a superpixel operation was performed to divide the images into several compact regions. Secondly, logarithmic-frequency pyramid histogram of orientation gradients (PHOG) were extracted as features from the segmented lesion image, which was obtained using an expectation maximization (EM) segmentation algorithm. They achieved an accuracy rate of 91.48% using SVM classifier. Later on [66], the authors extended their work to identify the diseases through the Internet of things (IoT). In that work, the authors combined the architecture of the superpixel clustering and k-means clustering algorithms to segment the diseased regions. PHOG-based image descriptors were used, and they reported an accuracy rate of 92.15%. The running time of the implemented model was less, since the original image was divided into many small compact regions using superpixels and since the number of extracted features were reduced using PCA. [PERSON] et al. [67] proposed another work to segment the diseased region from the image using superpixels and EM techniques. [PERSON] et al. [68] identified soybean plant diseases from mobile captured images. In their paper, a color- and cluster-based segmentation and extracted SHIFT features were used. They recorded an accuracy rate of 93.79% using an SVM classifier. Some of the identification challenges such as background clutter, illumination, shadow, scale and orientation were addressed. One of the issues in their paper was that they did not consider individual disease classes. They considered only two classes, healthy and diseased. [PERSON] et al.