2024
DOI: 10.46481/jnsps.2024.2058
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Identifying heterogeneity for increasing the prediction accuracy of machine learning models

Paavithashnee Ravi Kumar,
Majid Khan Majahar Ali,
Olayemi Joshua Ibidoja

Abstract: In recent years, the significance of machine learning in agriculture has surged, particularly in post-harvest monitoring for sustainable aquaculture. Challenges like heterogeneity, irrelevant variables and multicollinearity hinder the implementation of smart monitoring systems. However, this study focuses on investigating heterogeneity among drying parameters that determine the moisture content removal during seaweed drying due to its limited attention, particularly within the field of agriculture. Additionall… Show more

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