Abstract
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
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-sa. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 4
Claim · model-written summary
The SA-CCT model predicted blackpod disease with precision of 0.9429.
Not reportedTaxon
Verbatim
e0“The proposed SA-CCT model gives the following results after evaluation. Precision: 0.9429”
Claim · model-written summary
The SA-CCT model predicted blackpod disease with recall of 0.9167.
Not reportedTaxon
Verbatim
e0“Recall 0.9167”
Claim · model-written summary
The SA-CCT model had mean square error of 0.2357 for blackpod prediction.
Verbatim
e0“Mean Square Error: 0.2357”
Claim · model-written summary
The SA-CCT model predicted blackpod disease with accuracy of 0.9444.
Verbatim
e0“Accuracy: 0.9444”