Evaluating accuracy in detecting and tracking wild animals to protect crop lands using template matching over regression algorithm.

The introduction of wild animals into agricultural areas is a problem that is becoming more widespread due to the passage of time. This problem has surfaced as a consequence of the reduction in the habitat that wild animals reside in. The incursion of wild animals onto agricultural land results in significant economic harm from the invasion. When the animals arrive at the farm, the researchers use two ways to improve the accuracy of animal placement. These methods include logistic regression and template matching. Methodologies and Instruments for Research: An investigation is being conducted on the process of data extraction and classification, in addition to data collecting. All of the steps necessary to train and test the sample using both approaches have been finished. For the purpose of the SPSS study, approximately ten samples were gathered so that we could investigate, evaluate, and comprehend the appropriateness of the procedures that were offered. By employing a G power of 0.00.95, the SPSS software guarantees that the prediction will be accurate. According to the findings, the degree of confidence (CI) was 0.015 (p<0.05), and the alpha was also 0.015 (p<0.05). Our observations have led us to the conclusion that the SVM algorithm is superior to the earlier method in terms of animal detection and tracking. Furthermore, we are aware that the threshold for the SVM algorithm ought to be slightly stronger. The Accuracy of Regression for elephants is reported to be 97.045 percent, and the Accuracy of Template Matching is said to be 92.797 percent. The conclusion is that the suggested method is superior to the usual approach, as evidenced by the fact that it has a higher accuracy rate. Additionally, the performance of the logistic regression algorithm is examined and compared with that of the conventional method. [ABSTRACT FROM AUTHOR]