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

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