Investigating the Use of Machine Learning for South African Edible Garnish Yield Prediction

Abstract

This paper focuses on the specific scenario of capturing data in the South African agricultural industry; an industry where it can be difficult, expensive and time consuming to gather information, yet the need for information is critical. The aim is to conduct an introductory study into determining which aspects: location, irrigation, fertilizer application, temperature, or type of growing medium, has the most significant impact on the yield of edible garnish and then to predict the yield of a specific plant. A dataset collected over a three year period and supplemented with empirical knowledge and expert opinion, is analysed and a number of classifiers are applied to select the best strategy for predicting future yield of edible garnish. A random forest classifier showed the most promise and location on the farm was shown to have the largest influence on yield.