Challenges and opportunities in Machine learning for bioenergy crop yield Prediction: A review

Bioenergy offers a sustainable alternative to fossil fuels, addressing energy security and climate change concerns. This paper reviews the current landscape of machine learning (ML) applications in predicting bioenergy crop yields. It explores the potential of ML models, such as random forests, support vector machines, and neural networks, to improve yield predictions by analyzing complex agricultural datasets, including soil quality, weather conditions, and crop characteristics. The review highlights the challenges of implementing ML in bioenergy systems, such as data limitations, model interpretability, and scalability. Key findings indicate that integrating ML with traditional agricultural practices can optimize resource allocation, enhance yield predictions, and promote more sustainable bioenergy production. The paper also discusses future research directions for improving ML techniques to advance bioenergy crop yield prediction and sustainability.