Online weighing of kiwifruit using impact method

ماشین های کشاورزی

Article 15, Volume 6, Issue 1 - Serial Number 11, 2016, Pages 163-175   

Document Type: Research Article

DOI: 10.22067/jam.v6i1.33683

Authors

S. M. Mir-ahmadi; S. A. Mireei^* ; M. Sadeghi; A. Hemmat

Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran

Abstract

Introduction: Iran is one of the main producers of kiwifruit in the world. Unfortunately, the sorting and grading of the kiwifruits are manual, which is a time consuming and labor intensive task. Due to the lack of appropriate devices for sorting and grading of kiwifruit based on the quality parameters, only 10% of total production is exported (Mohammadian & Esehaghi Teymouri, 1999).
One of the main quality attribute for evaluating the kiwifruits is weight. Based on the standards, the minimum weight for an excellent kiwifruit is 90 g, while these values for the first and second classes should be 70 and 65 g, respectively (Abedini, 2003). Therefore, developing a device for fast weighing of fruits in the sorting lines can be useful in packaging, storage, exporting and distributing kiwifruit to the consumer markets.
In the past, the mechanical-based systems were commonly used for online weighing of the agricultural materials, but they did not lead to the promising accuracy and speed in sorting lines. Today, electrical instruments equipped with the precise load cells are substituted for fast weighing in the sorting lines. The dropping impact method, in which a free falling fruit drops on a load cell, is one of the suitable techniques for this purpose.
Different studies have addressed the application of dropping impact for fast weighing of agricultural materials (Rohrbach et al., 1982; Calpe et al., 2002; Gilman & Bailey, 2005; Stropek & Gołacki, 2007; Elbeltagi, 2011). The aim of this study reported here was to develop an on-line system for fast weighing of kiwifruit and compare the accuracy of different methods for extracting the weight predictive models.
Materials and Methods:
Sample selection: A total of 232 samples with the weight range of 40 to 120 g were selected. Before conducting the main experiments, the weight and dimensions of the sample were measured using a digital balance and caliper, with the precisions of 0.001 g and 0.01 mm, respectively.
Impact measuring system: The impact signals of kiwifruits in an online situation were acquired using a system, including conveying and ejecting unit, a load cell and data acquisition unit (Fig.1). The load cell was a single point load cell with 5 kg capacity. The load cell was connected to the data acquisition unit (Fig.2) in order to record the impact signal of the device in time domain of 0-5 s.
Before performing the main experiments, the load cell was calibrated using 100, 200, 500 and 1000 g standard masses. All the tests were carried out on three different forward speeds of conveyor, including 1, 1.5 and 2 m s-1 in order to obtain the optimum forward speed.
Data Analysis: In this study, two different methods were applied to build the weight predictive models. In the first method, the main components of the impact signal, including the force value at the first peak Fp, time required to peak force Dp, and the impulse or area under the first peak Ip were calculated and used as independent variables to develop the weight predictive models. In the second method, the impact components were calculated for the 40 successive peaks. Multiple linear regression (MLR) analyses were used to correlate the independent (impact components) and dependent (weight) variables.
Results and Discussion: The weight statistical characteristics of the samples, including the maximum, minimum, average, standard deviation and coefficient of variability in total data, calibration and test sets are shown in Table 1. As depicted, almost the same range and variability were observed for calibration and test data sets, indicating the proper distribution of the samples.
Table 2 summarizes the results of simple and multiple linear regressions for predicting the weight from the signal components (Fp, Dp, Ip) of the first peak at different speeds of 1, 1.5 and 2 m s-1. As shown, at the forward speeds of 1 and 2 m s-1, the multiple regression models based on all three signal components, and at forward speed of 1.5 m s-1, the model based on the combination of Fp and Ip, resulted to the best prediction powers. Among different forward speeds, the forward speed of 1 m s-1 gave the best model with SDR value of 2.180. Fig.4 depicted the predicted versus true values of weight obtained from the best linear regression models using components of Fp, Dp, Ip, Fp-Ip, and multiple of the first peak of impact signal.
The results of simple and multiple linear regression for predicting the weight from the signal components (Fp, Dp, Ip) of the first forty peaks at different speeds of 1, 1.5 and 2 m s-1 are summarized in Table 3. The best models were obtained by multiple combination of all three impact signals at the forward speed of 1 and 2 m s-1, and combination of Fpi-Ipi (i=1,...,40) at 1.5 m s-1 speed. Compared with the first peak results, the accuracy of prediction reached to 84%, 60% and 52% at forward speeds of 1, 1.5 and 2 m s-1, respectively. The best results were obtained at a forward speed of 2 m s-1, in which the SDR reached to a satisfactory value of 2.857 by applying the Ipi (i=1,...,40) values. The predicted versus true values of weight obtained from the best linear regression models using components of Fp, Dp, Ip, Fp-Ip, and multiple of the first forty peaks of impact signal are illustrated in Fig.5.
Conclusions: The results of this study revealed that among different impact component, Ip was the best predictor of the kiwifruits weight. Moreover, the developed models based on impact components of the first forty successive peaks gave the best accuracy with respect to the first peak components.

Keywords

Grading; Impact signal; Impulse; Modeling; Multiple regression

References

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