Early Cocoa Blackpod Pathogen Prediction with Machine Learning Ensemble Algorithm based on Climatic Parameters

• Published in: Journal of Information and Organizational Sciences Vol. 46; no. 1; pp. 1 - 14
• Main Author: Samuel, Olofintuyi Sunday
• Format: Journal Article Paper
• Language: English
• Published: Varazdin Faculty of organization and informatics, University of Zagreb 01.01.2022; Fakultet organizacije i informatike, Sveučilište u Zagrebu; Sveuciliste u Zagrebu, Fakultet Organizacije i Informatike; Fakultet organizacije i informatike Sveučilišta u Zagrebu; University of Zagreb, Faculty of organization and informatics
• Subjects: Algorithms; Blackpod Disease; CCT; Cocoa; Data Mining; Datasets; ICT Information and Communications Technologies; Machine Learning; Mathematical models; Parameters; Programming languages; Rainfall; SA-CCT Model; SARIMA; Data Mining; Blackpod Disease; SACCT Model; SARIMA; CCT; Machine Learning
• ISSN: 1846-3312; 1846-9418; 1846-9418
• DOI: 10.31341/jios.46.1.1

Machine learning has been useful for prediction in the various sectors of the economy. The research work proposed an ensemble SA-CCT machine learning algorithm that gives early and accurate prediction of blackpod disease to farmers and agricultural extension officers in South-West, Nigeria. Since data mining put into consideration the types of pattern in a given dataset, the study considered the pattern in climatic dataset retrieved from Nigeria Meteorological agency (NIMET). The proposed model uses climatic parameters (Rainfall and Temperature) to predict the outbreak of blackpod disease. The ensemble SA-CCT model was formulated by hybridizing a linear algorithm Seasonal Auto Regressive Integrated Moving Average (SARIMA) and a nonlinear algorithm Compact Classification Tree (CCT), the implementation was done with python programming. The proposed SA-CCT model gives the following results after evaluation. Precision: 0.9429, Recall 0.9167, Mean Square Error: 0.2357, Accuracy: 0.9444