G. Holmes, S.J. Cunningham, B.T. Dela Rue, A.F. Bollen
Many models have been used to describe the influence of internal or external factors on apple bruising. Few of these have addressed the application of derived relationships to the evaluation of commercial operations. From an industry perspective, a model must enable fruit to be rejected on the basis of a commercially significant bruise and must also accurately quantify the effects of various combinations of input features (such as cultivar, maturity, size, and so on) on bruise prediction. Input features must in turn have characteristics which are measurable commercially; for example, the measure of force should be impact energy rather than energy absorbed. Further, as the commercial criteria for acceptable damage levels change, the model should be versatile enough to update bruise thresholds from existing data.

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 [1]. 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 [2] 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 [3]. The experiment described in this paper uses the machine learning programs C4.5 [4] and M5' [5] to determine the influence of impact energy, radius of curvature and impact site location on bruise area.

Holmes, G., Cunningham, S.J., Dela Rue, B.T. and Bollen, A.F. (1998). PREDICTING APPLE BRUISING USING MACHINE LEARNING. Acta Hortic. 476, 289-298
DOI: 10.17660/ActaHortic.1998.476.33
Apples, bruising, machine learning

Acta Horticulturae