Identification of variables affecting production outcome in prawn ponds: A machine learning approach

•A new set of machine learning approaches to analyse senor data from prawn ponds.•Identification of relationships between water quality variables and yield.•A novel water quality variable data set from a prawn farm in South East Asia. A number of variables can affect the harvest yield in prawn ponds including dissolved oxygen, ammonia, pH, nitrite, and so on. A set of industry standards are there to maintain these variables within specific ranges for maintaining ideal growing environments for the prawns. However recent harvest results in a prominent prawn farm in South East Asia have shown different performance across ponds even after maintaining these variables within the industry standard ranges. An experiment was conducted recently to collect data on different influence variables (mentioned above) by measuring them at different times over the whole prawn growing season. We have conducted a set of analytical experiments on this data set using machine learning methods to answer three questions: (1) What level of predictive power do the influence variables have i.e. how well they can differentiate between good and bad performing ponds, (2) What is the relative importance of influence variables in predicting pond performance, and (3) How the perceived variables influence the harvest metrics. The paper presents a set of machine learning based analytical approaches undertaken to answer these questions.