Modeling of volume and surface area of apple from their geometric characteristics and artificial neural network

Abstract

ABSTRACTGeometric parameters and physical properties of agricultural products are widely used in designing and manufacturing of harvesting devices. These features are highly useful for drying and sorting processes. This can lead to determine the major and minor diameters in this regard. As such, the current study applied machine vision, and image processing technology to identify the major and minor diameters of the Golden Delicious apple. Through applying apple diameters, the actual surface area and real volume of apples were measured by peeling method and water displacement method, respectively. Finally, mathematical modeling, and feed-forward artificial neural network allowed for estimation of the surface area and volume of Golden Delicious apple. The results revealed that the correlation coefficient (R2) of the mathematical model, for the volume and surface area were 0.9394 and 0.9291, respectively. In the neural network, R2-values for the volume and surface area in the most appropriate topology were 0.99991 and 0.99995, respectively. Moreover, study findings indicate that predicting the volume and surface area of fruit can be determined better using artificial neural network than using mathematical model. The proposed artificial neural network procedure applied in this study even minimized the complex calculations for estimating volume and surface area of fruit.