# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Shape Features  
The shape is a basic characteristic of a leaf used in feature extraction of leaf images during image processing [66]. The primary shape parameters include the length (\(L\)), which  
Figure 9: Watershed image segmentation example [61]. is the displacement between the two points in the longest axis; the width (\(W\)), which denotes the displacement between the shortest axis; the diameter (\(D\)), which denotes the maximum distance between the points; the area (\(A\)), which denotes the surface area of all the pixels found within the margin of a leaf picture; and the perimeter (\(P\)), which denotes the accumulative length of the pixels around the margin of a leaf picture [55; 58; 62; 64]. From the 5 defined primary characteristics of shape features, 11 distinct secondary features are formed by mathematical definitions involving 2 or more primary variables [59]. These 11 features are called the morphological features of a plant. The morphological features are as follows:  
* Circularity (\(C\))--a feature defining the degree to which a leaf conforms to a perfect circle. It is defined as [60]: \[C=\frac{4\pi A}{P}\] (3)
* Rectangularity (\(R\))--a feature defining the degree to which a leaf conforms to a rectangle. It is defined as [55]: \[R=\frac{LW}{A}\] (4)
* Aspect ratio (\(AS\))--ratio of width to length of a leaf. It is defined as [55]: \[AS=\frac{W}{L}\] (5)
* Smooth factor (\(SF\))--ratio of leaf picture area when \(5\times 5\) and \(2\times 2\) regular smoothing filters have been used [58]. * Perimeter-to-diameter ratio (\(PDr\))--ratio of the perimeter to the diameter of a leaf. It is defined as [64]: \[PDr=\frac{P}{D}\] (6)
* Perimeter to length plus width ratio (\(PLWr\))--ratio of the perimeter to length plus width of a leaf. It is defined as [64]: \[PLWr=\frac{P}{L+W}\] (7)
* Narrow factor (\(NFr\))--ratio of diameter to length of a leaf [60]:
* Area convexity (\(ACr\))--area ratio between the area of a leaf and the area of its convex hull [59]. * Perimeter convexity (\(ACr\))--the ratio between the perimeter of a leaf to that of its convex hull [60]. * Eccentricity (\(Ar\))--the degree to which a leaf shape is a centroid [64]. * Irregularity (\(Ir\))--ratio of the diameters of an inscribed to the circumscribed circles on the image of a leaf [59]. Other researchers and scholars chose to implement the color features as the pivotal features during the extraction process [67]. The color features normally cited in the literature on leaf feature extraction include the following:  
* Color standard deviation (\(\sigma\))--a measure of how much the different colors found in an image match one another or are rather different from one another [60]. If an image is differentiated into an array of its basic building blocks (the pixels), then i is a pointer moving across the rows of pixels in an array from the origin to the very last row M, while j is a pointer moving across the columns of pixels in an array from the origin to the very last column N. At any point, a pixel color intensity is defined by p(i, j), where i and j denote the coordinate position of a pixel in an image array. Therefore, the color standard deviation is mathematically defined as follows:\[\sigma=\frac{1}{MN}\sqrt{\sum_{i=1}^{M}\sum_{j=1}^{N}}p(i,j) \tag{8}\]
* Color mean (\(\mu\))--a measure to identify a dominant color in a leaf image. This feature is normally used to identify the leaf type [63]. It is mathematically defined as follows: \[\mu=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}}p(i,j)\] (9)
* Color skewness (\(\varphi\))--a measure to identify a color symmetry in a leaf image [21; 46]: \[\varphi=x=\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\left[p(i,j)-\mu\right]^{3}}{MN \sigma^{3}}\] (10)
* Color kurtosis (\(\varphi\))--a measure to identify a color shape dispersion in a leaf image [65]: \[\varphi=x=\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\left[p(i,j)-\mu\right]^{4}}{MN \sigma^{4}}\] (11)  
Texture Features  
There are also several textural features referenced by authors such as [PERSON] [68], [PERSON] [69], and [PERSON] [70].