## 3 Materials and Method
### SVM-Assisted Recognizing of Sensitive and Insensitive Regions
#### 3.3.2 Practical Implementation of the SVM  
SVM can be useful in precision agriculture for tasks such as crop classification, disease detection, and yield prediction. SVM can handle high-dimensional and non-linear data, which are common in precision agriculture applications. SVM can also provide accurate and reliable predictions, making it useful for decision-making in precision agriculture. Additionally, SVM is relatively insensitive to overfitting, which can be helpful when working with limited or noisy data. This is the primary reason to utilize SVM for this problem.  
The history table is stored in a CSV file. The percentages of training and testing data of the HST table of all CHs are 70% and 30%, respectively. The values of accuracy, precision, and recall for training and testing data are 93.25%, 91.71%, 92.54%, 91.67%, 90.11%, and 90.9%, respectively. All of these values are greater than 80%, which means that construct of the model is fit enough to explore the relationships between sensitive decisions and their factors in the HST table. The parameters of the SVM are shown below in Table 2.  
Parameter C specifies the penalty parameter or the error term. It is nothing but the degree of correct classification and its minimum value is 2 since it is a two-class problem. However, for values higher than this, \"there is no way the SVM classifier can miss classify any single point\". class_weight is the next parameter that depends on possible labels. Here, the labels are 0 to 1 for insensitive and sensitive regions, respectively. This parameter is set to none so that both classes get equal weight. The size of the history of CH is limited to 500. Whenever a new record is inserted, an older record has to be deleted if the no. of records exceeded 500. From [53; 54; 55], values of c higher than two for arbitrary classification problem generates an accuracy of more than 90%. The minimum required sample size is 400 to achieve an accuracy close to 93%. Based on this, we have decided the value of C to be 100 and \(HST\_tablesize\) to be 500. \(coef\) is an independent term that is applicable only in the case of poly and sigmoid types of functions. Therefore, in the present scenario this is set to 0.0, \"our\" or one_verses_rest is set to the shape of decision function, one_verses_rest classifier grabs a class and creates a binary label for whenever a data point belongs to that class or not. The values of degree and gamma are 3 and auto deprecated, respectively. 'degree' is nothing but the degree of the polynomial and its default value is 3. Gamma is set to auto_deprecated meaning that no specific value of it has been passed. The kernel cache size is set to 500. Keeping parity with the table size of his history or \(HST\_tablesize\), rbf is the default kernel type, max_iter specifies the upper limit on iteration, which, if set to \(-1\) specifies no limit on the number of iterations. A probability option is required to compute the probability estimates. By default, it is set to false. The random state is ignored when the probability is false. Similarly, the default value for the shrinking heuristic is true. told specifies the tolerance value for the stopping criterion and we go for a default value of 0.001 for this parameter as well. verbose is a boolean variable that is set to false by default because, otherwise, it may not work in a multi-threaded environment. Default values have been taken from literature on SVM [53; 54; 55].