PREDICTING APPLE BRUISING USING MACHINE LEARNING
Machine learning is a burgeoning technology with a vast range of potential applications particularly in agriculture where large amounts of data can be readily collected . The main advantage of using a machine learning method in an application is that the models built for prediction can be viewed and understood by the owner of the data who is in a position to determine the usefulness of the model, an essential component in a commercial environment.
Machine Learning software recently developed at Waikato University  offers potential as a prediction tool for the classification of bruising based on historical data. It gives the user the opportunity to select any number of measured input attributes and determine the influence of that combination on a range of bruise size categories. The user may require a high degree of accuracy in the classification and therefore prune the attributes or bruise classes accordingly, or alternatively seek to discover trends in the dataset (in which case a lower level of accuracy often clarifies implicit structures in the data).
Models such as the theory of elasticity suggest that impact energy and radius of curvature will have a significant effect on the bruise surface area. Cell structure is also thought to contribute to variation in bruise size . The experiment described in this paper uses the machine learning programs C4.5  and M5'  to determine the influence of impact energy, radius of curvature and impact site location on bruise area.