Prince Michael Akwabeng

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

Prince Michael Akwabeng · Journal of Agriculture and Ecology Research International · 2026

This paper fills in the key gaps in the field of agricultural machine learning by comparing the performance of the Support Vector Machine (SVM), the Random Forest (RF) and the Decision Tree (DT) algorithms to predict the yield of maize in various locations in the U.S. Corn Belt....

Open access Research Article 10.9734/jaeri/2026/v27i1733

Artificial Intelligence Techniques for Monitoring Carbon Emissions and Supporting Green Investment Decisions

Emmanuel Ohimai Ojo, Prince Michael Akwabeng, Gloria Opoku Darkoh & Adetomiwa Adesokan · International Journal of Environment and Climate Change · 2026

Artificial Intelligence (AI) has been introduced as a revolutionary tool for carbon emission monitoring, and helps in making investment decisions that are both environmentally friendly and sustainable. The systematic review summarises the existing evidence regarding AI applicatio...

Open access Research Article 10.9734/ijecc/2026/v16i25278

Smart Agricultural Technologies for Sustainable Food Production under Climate Change Pressures

Manfred Obinwanne Igwenagu, Prince Michael Akwabeng, Gloria Opoku Darkoh, Yusuf Kolawole Adebakin, Emmanuel Ohimai Ojo & Phebian Odufuwa · Journal of Agriculture and Ecology Research International · 2025

Climate change is a major challenge to international food security, and hence, new strategies need to be developed to ensure sustainable food production. This paper comprehensively examines how artificial intelligence (AI) and Internet of Things (IoT) technologies have been appli...

Open access Research Article 10.9734/jaeri/2025/v26i6722