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

Abstract

Geometric 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 (R 2) of the mathematical model, for the volume and surface area were 0.9394 and 0.9291, respectively. In the neural network, R 2-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.

Real-Time Size and Mass Estimation of Slender Axi-Symmetric Fruit/Vegetable Using a Single Top View Image

“…Due to the limitation of the load cell, many researchers try to find other methods to estimate weight/volume without using a load cell [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. One of the most accurate ways is to estimate the volume of the product and using a pre-determined “density” to calculate the actual weight of the object because mass and volume are correlated to a certain degree.…”

“…Another method is a 3D reconstruction using 3D scanning from multiple 3D views [ 22 , 23 , 24 , 25 , 26 , 27 , 39 ]. The 3D construction returns highly accurate results, but it requires many cameras.…”

“…In addition, the Artificial Neural Network (ANN) has also been used for volume estimation [ 14 , 26 , 37 ]. One of the most popular ways to estimate the volume is to use a single image and measure the widths and/or lengths to feed into their model [ 14 , 37 ].…”

“…One of the most popular ways to estimate the volume is to use a single image and measure the widths and/or lengths to feed into their model [ 14 , 37 ]. There are many types of ANNs in the literature [ 26 , 37 , 42 ]. However, the Levenberg-Marquardt algorithm (LM) still provides sufficiently accurate results in volume estimation [ 26 , 37 ].…”

Clustered ANFIS weighing models for sweet lime (Citrus limetta) using computer vision system

“…The authors (Utai et al, 2019) have evaluated the classification and mass estimation model for the mango fruits by linking image processing with ANN and achieving the higher classification accuracy of 97%. The authors (Ziaratban, Azadbakht, & Ghasemnezhad, 2017) have modeled the volume and surface area of apple using its major and minor diameter. The investigative result of this study shows a computer vision system along with ANN provides a better solution as compared to the mathematical model.…”