# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 3 Different Existing Machine Learning Based Techniques for Plant Disease Detection
### Color-Features-Based Disease Detection  
Disease detection by extracting color features was performed by [PERSON] et al. [45]. In their approach, they implemented YCbCr-, HSI- and CIE L*a*b*-based color models on a leaf image to extract the color features of a diseased leaf and then compared all these methods. From all of these color models, they chose the \"A\" component of the CIE L*A*B model for the initial segmentation of the diseased leaf. A median filter was used for preprocessing purpose. Hence, this method was affected by less noise from different sources. Finally, the diseased area of the leaf was segmented by applying [PERSON]'s threshold on the \"A\" component of the color space.  
[PERSON] [46] used a color slicing technique to detect the blast disease of paddy. In their method, firstly they convert the RGB image to HSI and used color slicing to extract the diseased area and neutralize the rest of the portion. They compared their technique with disease boundary detection using the Sobel and Canny methods and obtained an accuracy rate of 96.6%.  
[PERSON] et al. [47] used different color models to identify diseases in plants. Firstly, they transformed the images into different color models, followed by a noise reduction using a median filtering technique and finally segmented the diseased region from the leaf. They used three different color models: YCbCr, HIS and CIELAB. In their approach, they applied [PERSON]'s threshold on the Cr component of the YCbCr model, the H component of the HIS model and the A component of the CIELAB model to segment the diseased spot.  
[PERSON] et al. [48] identified chili leaf diseases using different color features. They extracted the yellow, green and cyan color components from the leaves and used color matching techniques to identify the diseased and healthy leaves.  
[PERSON] et al. [49] identified two different rice diseases (brown spot, leaf scald) using a color analysis of the image. Threshold segmentation was used to segment the abnormalities, followed by a histogram intersection to isolate the segmented region. K-means clustering was used to assign the pixels into different clusters based on R, G and B color values. The classification of disease was done by comparing and matching the color values. [PERSON] et al. [50] used fuzzy entropy to identify four different paddy (rice) diseases. A PNN was used as the classifier, and it obtained an accuracy rate of 91.1%. One of the major issue in their approach was that in the preprocessing step, they needed to crop the diseased region manually before extracting the features.  
Figure 2: Basic handcrafted-features-based steps for identification.  
[PERSON] et al. [51] identified four different rice plant diseases using 172 color features. In their approach, the authors used 13 different color spaces and from each color channel, they extracted different color features such as mean, standard deviation, kurtosis and skewness. Seven different classifiers SVM, DC (discriminant analysis), KNN, NB, DT, RF and LR were used to classify the diseases and compare the performances. Among all the classifiers, SVM gave better a performance accuracy with 94.65%. The main issue in their work was that the images were already preprocessed and had a black uniform background.  
The main disadvantage of disease detection using color values is that using a color component is not sufficient to detect all types of diseases. Table 2 summarizes the method with segmentation techniques and features used in the detection of diseases based on color features.