Disease prediction and knowledge extraction in banana crop cultivation using decision tree classifiers. (17th December 2021)

• Record Type: Journal Article
• Authors: Anitha, A.
• Is Part Of: International journal of business intelligence and data mining. Volume 20:Number 1(2022)
• Page Start: 107
• Page End: 120
• Publication Date: 2021-12-17
• Subjects: attribute selection -- decision tree -- classification -- accuracy; 006.312
• Languages: English
• ISSNs: 1743-8187

Abstract

Agriculture plays a vital role in determining the economic status of a country. To meet the growing needs of society and to improve crop productivity, researchers are focusing on the development of various technologies. In India, bananas are one of the leading crops with high demand. To improve the yield of bananas, it is necessary to detect diseases at an early stage. Also, in order to acquire new farmers and to retain existing banana farmers, it is essential to extract knowledge about hidden causes for various diseases in the banana crop. This work aims to apply data mining techniques like decision tree classifiers on banana cultivation dataset. Agricultural dataset used for experimentation is collected from farmers cultivating bananas in regions fed by the Thamirabharani River such as Kanyakumari, Tirunelveli and Tuticorin districts of Tamil Nadu. The higher the disease detection accuracy, the greater will be the crop productivity. Performance of classifiers such as J48, REP tree and random forest are compared based on classification accuracy, precision, recall and F-measure. Among various classification techniques applied over agricultural dataset, it has been identified that random forest algorithm outperforms other techniques with respect to classification accuracy.