A hybrid non-invasive method for internal/external quality assessment of potatoes

Consumers purchase fruits and vegetables based on its quality, which can be defined as a degree of excellence which is the result of a combination of characteristics, attributes and properties that have significance for market acceptability. In this paper, a novel hybrid active imaging methodology for potato quality inspection that uses an optical colour camera and an infrared thermal camera is presented. The methodology employs an artificial neural network (ANN) that uses quality data composed by two descriptors as input. The ANN works as a feature classifier so that its output is the potato quality grade. The input vector contains information related to external characteristics, such as shape, weight, length and width. Internal characteristics are also accounted for in the input vector in the form of excessive sugar content. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The validation of the methodology was made through experimentation which consisted in fusing both, external and internal characteristics in the input vector to the ANN for an overall quality classification. Results using internal data as obtained from an infrared camera and fused with optical external parameters demonstrated the feasibility of the method since the prediction accuracy increased during potato grading.