# 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
A backpropagation neural network was used for the classification, and it obtained an accuracy rate of 97%. The advantage of using a wavelet transform was that it worked well on low-frequency components and also in high-frequency transients. [PERSON] et al. [56] identified three different types of cotton plant diseases using image processing. A K-means clustering technique was used to segment the images into three clusters as background, foreground and diseased regions. Different color, shape and texture features were extracted from the segmented images and an SVM was used to classify the images and it achieved an accuracy rate of 98.46%. The number of images used to train and test the model was small. The selection of a proper set of features by the classifier was an important issue in the identification. [PERSON] et al. [57] identified two different grape diseases (grape downy mildew and grape powdery mildew) and two different wheat diseases (wheat stripe rust and wheat leaf rust) using a backpropagation (BP) network. In their approach, K-means clustering algorithm was used to segment the images and they extracted 21 color features, 4 shape features and 25 texture features from the segmented image, which were used for classification. One of the major issues in their paper was that the images were captured in a fixed condition. Seven different groups of feature combinations were used for the identification and therefore, finding the proper set of features combinations to get the optimal result was an important challenge. Two different diseases in grapes, namely, downy and powdery mildew diseases, were identified by [PERSON] et al. [41]. In that paper, firstly, the diseased region was extracted using K-means clustering techniques with three clusters. After the segmentation, nine color features and nine texture features were extracted from each cluster and an SVM was used for the classification. To train and test the model, they used 137 captured grape leaf images and achieved an accuracy rate of 88.89%. Later on, the authors [42] extended their work identifying the same diseases using both SVM and ANN classifiers. In order to improve the classification, a fusion classification was performed, which created an ensemble of classifiers from the SVM and ANN and reported a recognition accuracy of 100% for both downy and powdery mildew diseases. The feature set used for the classification consisted of nine color and nine texture features, as used in [41]. One of the issues was that only two different disease categories were considered, and the dataset used was too small. The extraction of features was time-consuming as the system used 54 different features for the identification. [PERSON] et al. [58] identified three pest insects damages (leaf miners, thrips and Tuta absoluta) and three pathogen symptoms (early blight, late blight and powdery mildew) using a serial combination of SVM classifiers. In that paper, the authors used a K-means clustering technique for segmenting the diseased area. From the segmented image, they extracted 18 color, 30 texture and 11 shape features. In their system, they used two SVM classifiers where the first SVM took the color features and the second SVM took the texture and shape features to classify the diseases. To train and test the system, they used 284 images captured on a uniform background and achieved an accuracy rate of 87.8%. Issues regarding their model were that the images were captured on a uniform background and the dataset size was very small. In [59; 60], the authors used a genetic algorithm (GA) to segment the diseased area from the leaf image. They showed that the GA had advantages over a simple thresholding-based segmentation or K-means clustering segmentation technique as the GA did not require user input or the number of clusters at the time of segmentation. In [60], the authors identified five different plant diseases using the minimum distance criterion (MDC) and an SVM classifier.