Francisco Chadid is a qualified engineer with more than 5 years’ experience in the oil & gas, solar and crop protection industries. Working full time and returning to study as a business analytics student, Francisco has applied multiple techniques to approach and analyse different data types in efficient ways. Francisco has extended family connections in Columbia and during COVID-19 Francisco considered several agricultural challenges seen in Columbia that would benefit from AI/ML and identified Palm Oil Bud Rot as a significant problem requiring a modern technical solution.

During COVID-19 Francisco was exposed to the use of artificial Intelligence (AI) and Machine Learning (ML) and its uses in healthcare and diagnostics. Utilising his understanding of techniques to analyse different data types, Francisco developed an idea for an application of ML for use in agricultural decision making that could accelerate remedial actions and better define timely outcomes for oil palm bud rot disease detection. This led to a concept of utilising image recognition technology overlaid on machine learning and applying it to disease prediction and detection at a much earlier stage. The primary concept is that the technology utilises ML, drawing data sources from existing systems, sensors and rich data sources whereby it is then analysed, and paired with drone imaging to assist further decision making by farmers. It’s expected to significantly lowers the cost of production and remediation whilst having significant environmental benefits via reduced use of crop protection agents and reduced use of fertilizer and other inputs.