Comparing the Performance of Classification Algorithms in the Conservation of Plant Genetic Resources for Agriculture and Food Security

Plant germplasm for agriculture and eatable safety are essential for ensuring food security and sustainable agriculture. It is crucial to conserve these resources for future generations. Conservationist's often classify plant genetic resources into long-term, medium-term and short-term conservation categories, which helps them prioritize their efforts and allocate resources effectively. Data mining has become increasingly useful for identifying useful genetic traits and optimizing conservation strategies. Leveraging the power of data mining, conservationists can make informed decisions that will help safeguard our planet's plant genetic resources for generations to come. The performance of decision tree, support vector machine, naive Bayes, and K-nearest neighbour are measured and compared to each other. The comparison involves measuring accuracy as well as the error values. The Decision tree algorithm achieved a highest accuracy value with 95%, while the lowest is achieved by SVM algorithm with 77.50<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>. This prediction can help conservationists in taking necessary measures for future.