# 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
Firstly, the classification was achieved using the MDC with a K-Mean clustering and they obtained an accuracy rate of 86.54%. The performance accuracy improved to 93.63% using the GA. Secondly, the classification was done using an SVM where the performance improved to 95.71%. Two tomato diseases (TSWV, TYLCV) were identified in [61] with the help of some geometric and histogram-based features; the authors classified a diseased image using an SVM classifier with different kernel values. The dataset used was 200 captured images of both diseased and healthy leaves and they obtained an accuracy rate of 90%. One of the disadvantages in their approach was that the images containing more than one leaves needed to be cropped manually to extract a single leaf image, which made the process complex. In [37], [PERSON] et al. identified six different types of tomato diseases from images of the leaf and the stem. Using [PERSON]'s segmentation technique, they extracted the diseased area and different color, shape and texture features. They classified the leaves using a decision tree classifier and obtained a classification accuracy rate of 97.3%. Six different tomato diseases were identified by [PERSON] et al. [30] using model-based statistical features. From the preprocessed images, they extracted the SHIFT features and to reduce the computational time and complexity, they reduced the dimensions using a generalized extreme value (GEV) distribution. They used a 10-fold cross-validation to analyze the performance accuracy and reported an accuracy rate of 84.7% using a quadratic SVM. Furthermore, their proposed method took 56.832 s to train the classifier and achieved 12,000 predictions per seconds. The authors extended their work from [30] and identified the diseases using SHIFT features with a Johnson SB distribution as a dimension reduction technique [62]. They achieved a better performance accuracy of 85.1% and it took 33.889 s to train the classifier. In both papers, they used tomato disease images from the PlantVillage dataset, where the images were captured in a laboratory setup condition. Due to the loss of information for the classification, they did not perform a segmentation technique. One more advantage was that the implemented model was robust to various resolutions. Later on, [PERSON] et al. [63] developed an SVM-based multiple-classifier system (MCS) to improve the accuracy of classification, and they used color, shape and texture features for the identification of leaf diseases in wheat plant. They got an accuracy rate of 96.1%. The selection of appropriate features among all extracted features which gave the best classification accuracy was an important challenge in their approach. [PERSON] et al. [1] identified and classified plant diseases using bacterial-foraging-optimization-based radial basis function neural network (BRBFNN). They used the region-growing algorithm for searching and grouping the seeded region having common attributes which were used for the feature extraction. They worked on some fungal diseases such as common rust, cedar apple rust, late blight, leaf curl, leaf spot and early blight. The advantage of using region growing was the grouping of seed points having similar attributes, which increased the efficiency of the network. Bacterial foraging optimization assigned the optimal weight to the radial basis network, which made the network faster and also increased the accuracy of the network. The main issues in their approach were that it worked only for fungal diseases and it could not be extrapolated to identify other diseases in plants. [PERSON] et al. [44] used a color feature, texture feature and a combination of color and texture features for detecting several diseases of soybeans, such as downy mildew, frog eye, septoria leaf blight, etc. They used three clustering techniques for training. In the first cluster, they found whether the leaf was affected by disease or healthy by calculating the number of connecting components.