Use of Support Vector Machines and Artificial Neural Network Methods in Variety Improvement Studies: Potato Example

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Introduction
Today, intelligent machines and production systems that control machines have begun to take over traditional production methods.
almost every region of Turkey. Its yearly production is 376 million tons in 19 million hectares of land in the world [15] and 4.8 million tons in the 143 thousand hectares of land in Turkey [16]. Potato is a widely used plant in human nutrition and industry because of its carbohydrate, protein, mineral and vitamin content. Therefore, there are 100-400 commercially registered potato varieties in the countries where intensive potato farming is carried out. Although Turkey is among the most important potato producing countries, the number of native varieties is quite less. Therefore, Turkey is dependent on foreign resources in terms of basic seed varieties [17,18]. Various enhancement works are carried out for the solution of this problem. In potato, variety improvement studies usually start with crossing and continue with clonal selection [19,20]. Table 1: Summary data on some studies conducted using data mining methods in agriculture.

Authors
Year Number of data Method Accuracy (%) Camargo

Materials and Methods
Part of the clones grown under the polycarbonic greenhouse  Table 2.  In this method, a randomly selected cross validation method is used that allows the model to be tested neutrally. In the study, the data sets were divided into 40% training, 20% validity and 40% test data, and the average success rate was determined. Gauss function was used as the kernel function in SVM classification.

Support Vector Machine
Since classification is the most basic operation in the estimation part of the data mining, an important part of the problems is encountered in this step. One of the various algorithms used for accurate classification of data sets in the data mining is SVM.
Proposed for the first time in 1995 [23], SVM is a supervised learning model in data mining used for classifying binary or multiple data sets of linear separable or linear non-separable type.
Since the SVM approaches the classification problem as a quadratic optimization problem, it saves the number of operations in the training process and provides a considerable speed advantage compared to other algorithms [24]. Thus, SVM is successful in highvolume data sets, as well as in high-dimensional problems with few data [25]. While hyperplane based support vectors are constructed for linear-separable data, kernel functions and support vectors are formed for linearly non-separable data. These functions are usually polynomial and Gaussian kernels [26]. SVM is used in many areas of classification problem, such as image processing, financial estimation, biological species detection, medical examination [27][28][29][30][31]. The hyperplane in Figure 1 is determined by the decision function estimated for the linear-separable data using the SVM method. wx+b=-1 y = −1 for class (2) Where y is the class label, w is the weight vector, and b is the approximation value. The minimizing process of the w value required for increasing the optimum plane is given in Equation 3 [23].
Equation 3 gives the following: The solution of Equation 4 with Lagrange equations gives Equation 5.
The decision function of the support vector machine for a twoclass problem is given in Equation 6 [32].  Figure 2 shows a sample neuron used in MLPNN. All the i1, i2, ..., in-1, in dimensions of the data are multiplied by weights w1, w2, ..., wn-1, wn respectively before reaching the neuron, and the result is primarily collected in the linear processing unit. The output of the linear processing unit is passed to the output layer through the activation function in the non-linear processing unit [33]. Operations in the hidden layer including linear and non-linear processing units are given in Equation 7 and Equation 8 [33].
The error between the output value y' and the actual value y is used for updating weights. In the updating process, usually the slope minimization method is used that provides a convergent approach to the goal [36]. Updated value of each weight (Δw) is found by distributing the calculated error energy, inversely proportional to the present w weights, to all the weights coming to the corresponding neuron [37]. The process is repeated for every point of the data and the average of the solutions found through the parameter η can be calculated.     Table 3, 39 clones were misclassified. As seen in the confusion matrix obtained with SVM classifier in Table 4 class, which is the positive selection class. As seen in the confusion matrix obtained with MLPNN classifier in Table 3, 22 clones were misclassified. As seen in the confusion matrix obtained with SVM classifier in Table 4, 25 clones were misclassified. As a result of the experimental studies, MLPNN classifier was found to be more successful than SVM for Model-3. For the selection of potato clones that should be eliminated by negative selection for model-4, 21 out of 703 clones were class 3 (tuber shape), 39 were class 2 (stem scar depth), 22 were class 1 (eyes depth) clones, which were among the clones to be eliminated, and 621 were in the other class, which is positive selection class. As shown in the confusion matrix resulting from the MLPNN classifier in Table 3, 12 out of 21 clones, which needs to be eliminated in terms of tuber shape, 29 out of 39 clones, which needs to be eliminated in terms of eyes pit depth, 12 out of 22 clones, which needs to be eliminated in terms of eyes depth, and 20 out of 621 clones, which were actually in the other class, were found to be misclassified (73 clones were misclassified in total). As seen in the confusion matrix resulting from the SVM classifier in Table 4, 8 out of 21 clones, which needs to be eliminated in terms of tuber shape, 23 out of 39 clones, which needs to be eliminated in terms of eyes pit depth, 7 out of 22 clones, which needs to be eliminated in terms of eyes depth, and 24 out of 621 clones, which were actually in the other class, were found to be misclassified (62 clones were misclassified in total). As a result of the experimental studies, it was observed that SVM classifier was more successful for

Model-2 Predict
Model-4. The sensitivity, specificity, and accuracy values obtained with the classifiers used for 4 different models are given in Table 5.
As shown in  In this study, a new early generation selection method is proposed using the models formed by applying SVM and ANN data mining methods to the selection criteria data sets that have been the success rates were found as 94.2% and 96.9% respectively. In the 4-class models (Model-2 and Model-4), the SVM method was found to be more successful and the success rates were found to be 88.5% and 91.2%, respectively. Based on these results, it was concluded that data mining approaches can be used, in addition to classical methods, when deciding positive or negative selections of the clones in the early selection stages of potato improvement.
And, it was decided to continue in future studies by taking other criteria into account with the selected clones in the next generation.
Furthermore, it is anticipated that the success rates can be increased by considering different data mining methods, increased number of clones and different attributes in future studies.