Decision Tree Algorithms and their Applicability in Agriculture for Classification
Journal of Experimental Agriculture International · pp. 20–27 · Published 3 May 2022
10.9734/jeai/2022/v44i730833Abstract
Machine learning approaches has the advantage that in most of machine leaning algorithms data transformation is unnecessary, can handle missing predictor variables, success of prediction is not dependent on data meeting normality conditions or covariance homogeneity, variable selection is intrinsic to the methodology and provides good accuracy over the traditional methods. The decision tree is one of such machine learning algorithms which is capable of handling both complete and incomplete data, so it can be applied in the field of agriculture where such data occurs frequently. The algorithms which were considered for this study includes ID3 (Iterative Dichotomizer 3), Classification and Regression Tree (CART) and C4.5. This paper provides a detailed approach on the development of decision tree using its various algorithms. It is anticipated that this study will be a layman guide to all agriculture researchers towards enhancing awareness of the potential advantages of using decision tree in agriculture, and contributing to its wide applicability in the agriculture data. The secondary data of cotton genotypes was used for classifying the genotypes into two classes, and hold out method was used for cross validation for checking the performance of the algorithms.
Cited by 32
Wei Song, Lina Wang · SICE Journal of Control Measurement and System Integration · 2026
Mayang Modelina Cynthia, S. Prabowo, Jheki Pranta Singarimbun · International Journal of Health Engineering and Technology · 2026
Afsaneh Soleimani, Mohammad Boghrati, Hossein Damavandi · International Conference on Computer and Knowledge Engineering · 2025
Wilma Latuny, V. Lawalata, G. B. Imasuly · Decision Analytics Journal · 2025
Roopa Hegde, Vidya Kudva, Sneha Nayak · Discover Applied Sciences · 2025
Filiz Yetiz, Aysegul Ciger, Mehmet Kayakuş · SAGE Open · 2025
Md Benozir Hossain, Tim Leonhardt, Partha Sengupta · International Conference on Multimodal Interaction · 2025
Inna Sabily Karima · Format : Jurnal Ilmiah Teknik Informatika · 2025
Abdul Rachman Manga, As'syahrin Nanda, A. Handayani · International Conference on Ubiquitous Information Management and Communication · 2025
M. Agelli, Nicola Corona, Fabio Maggio · Machines · 2024
Related research
- Testing the Probability of Heart Disease Using Classification and Regression Tree Model — shares topic coverage
- A Novel Approach to Predict the Performance of Student and Knowledge Discovery Based on Previous Record — shares topic coverage
- An Assessment Framework to Delineate Between Dietary Foods for Special Medical Purposes and Normal Foods — shares topic coverage
- Analysis of Indian Food Based on Machine learning Classification Models — shares topic coverage
- A Comparative Machine Learning Framework for Location-Specific Maize Yield Prediction in the U.S. Corn Belt Using SVM, Random Forest, and Decision Tree Models: Integrating Agronomic, Genetic, and Spatial Factors — shares topic coverage
Article metrics
Real usage data collected on this platform.
20
Page views
0
PDF downloads
0
Outbound clicks
32
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.