Good practices of artificial intelligence in biosystems engineering research

The use and integration of Artificial Intelligence (AI) and machine learning technologies into biosystems engineering create unprecedented opportunities for modelling, optimisation, and decision support across agriculture, livestock, food systems, environmental management, and related domains. However, the increasing complexity and often opacity of these methods is raising concerns regarding scientific rigour, reproducibility, transparency, generalisation and ethical responsibility. This letter establishes a set of good practice principles for authors submitting AI-driven research to Biosystems Engineering journal. The guidelines outline essential requirements for data quality, documentation of methodologies, experimental protocols, model selection, evaluation, interpretability, and reproduction of results. They emphasise the importance of open datasets and code availability, appropriate validation strategies, meaningful novelty, and clear evidence of relevance to the Biosystems Engineering scope.