# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### SVM Classifier
Whether k-NN is applied for classification or regression determines the results [79]. The outcome of the k-NN classifier is a class of belonging [74; 78; 79]. Based on the predominant kind of its neighborhood, the given data point is classed [79]. The input point is awarded to the category that has the highest frequency among its k-closest neighbors [78]. In most cases, k is a small positive integer such as 1. The result of a k-NN regression is just a value of the property for the attribute. The aggregate of the variables of the k-closest neighbors constitutes this number [79]. Figure 12 shows a space with numerous data points or vectors that can be classified into two classes: the red class and the green class. Now, assume there exists a data point at any location in the space shown in Figure 12 that is unknown whether it belongs to either the red or green class. The k-NN will then proceed through the following computational steps to assign that point a class of belonging:  
* Take the uncategorized data point as input to a model. * Measure the spatial distance between this unclassified point to all the other already classified points. The distance can be computed via Euclidean, Minkowski, or Manhattan formulae [80]. * Check the points with the shortest displacement from the unknown data point to be classified for a certain K value (K is defined by the supervisor of the algorithm) and separate these points by the class of belonging [80]. * Select the correct class of membership as the one with the most frequent vectors as the neighbors of the unknown data point [80]. Figure 11: ANN model architecture [77]. The most cited method of computing the spatial distance between the data point p to be classified and its neighbors qn is the Euclidean Formulae (18) [74; 80]:  
\[\mathrm{d}(\mathrm{p},\mathrm{q})=\mathrm{d}(\mathrm{q},\mathrm{p})=\sqrt{\left( q_{1}-p_{1}\right)^{2}+\left(q_{2}-p_{2}\right)^{2}+\ldots+\left(q_{n}-p_{n} \right)^{2}}=\sqrt{\sum_{i=1}^{n}(q_{i}-p_{i})^{2}} \tag{18}\]  
The fuzzy classifier system is a supervised learning model that enables computational variables, outputs, and inputs to assume a spectrum of values over predetermined bands [81]. By developing fuzzy rules that connect the values of the input variables to internal or output variables, the fuzzy classifier system is trained [82]. It has mechanisms for credit assignment and conflict resolution that combine elements of typical fuzzy classifier systems [81]. A genetic algorithm is used by the fuzzy classifier system to develop suitable fuzzy rules [83]. As shown in Figure 13, fuzzy sets display a continuous membership, and a data point membership classification can be ruled as the extent (\(\upmu\)) to which it belongs to a certain fuzzy set. For example, 690 mm in Figure 13 has a degree of membership \(\upmu\)(960) on the close fuzzy set that is 0.7. It can also be seen in Figure 9 that a data point can belong to multiple fuzzy sets, and the degrees of membership to each set may or may not (in the intersection points) differ since some fuzzy sets overlap with each other. Table 3 summarizes the advantages and disadvantages of all the classification techniques discussed in this section. Figure 12: Classification principle of a k-NN model [80]. Figure 13: Example of fuzzy sets for classification [83].