Development of a New Method for Volume Measurement Based on Moiré Techniques / Desenvolvimento De Um Novo Método Para Medida De Volume Baseado Em (original) (raw)
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Development of a new method for volume measurement based on moiré techniques
INMATEH-Agricultural Engineering, 2014
This research work reports a shadow moiré method applied in generating spatial dimensions of solid figures. The focus of this work is to study as well as to turn feasible a new method of measuring object shape as plant organs. Selected experimental setup included four Ronchi optical grids out of phase by ¼ of period, a conventional white light source and a digital camera. The samples were hold by a support, following by data acquisition process generating four elevation digital models (EDM). EDM were employed to create the tridimensional Model (TM) which, in turn, was also applied to generate the object volume. The method of water volume displacement has been used for data comparison in which the change of liquid volume is equal to the object volume. The results allowed to state that moiré techniques can be used for volumetric determination of irregular objects as fruits and others vegetable organs. The work includes error determination.
Estimation of surface area and volume of agricultural products are considered as important factors in optimization of storage conditions, packaging, transportation, water adsorption/desorption, heat, pesticides and also their breathing. The objective of this study was to establish a method to obtain non-destructive measurement of surface area of cylindrical fruit like zucchini (Cucurbita pepo) using image processing technique. In this approach, measuring surface area was performed by a scanner set. Before peeling the fruit off, the external surface area of zucchini was imaged by rolling it on the scanner’s screen. Then, the external surface area was calculated using image processing technique. In order to evaluate the accuracy of method, the results were compared with the usual measured skin surface area after peeling (actual value) in the same condition. The results showed that the precision of this new method is significantly high (R2= 94.43). The mean and standard deviation of the surface area differences between the two methods were 5.81 cm2 and 0.13 cm2, respectively. The Bland-Altman approach was also considered to be satisfactory. The results showed that image processing method was suitable for surface area estimation of almost all zucchini sizes. Furthermore, all the process (photography and image processing) was performed in less than 6 seconds. The area of cylindrical fruits can be measured nondestructively, quickly, and precisely using the applied image processing technique, providing a hardware to scan external surface area of crop.
Plant architecture research subjects are of significant importance to genetics, photosynthesis, transpiration, crop-machine mechanical relationship, etc. In this sense, this research work had been carried in developing a new technique to generate the three dimensional view of plant shape. In this work the plant architecture determination will be carried out by means of a phase shift moiré method. The importance of the proposed method is based on the application of a sequence of four grids out of phase by an angle of π/2 radians one from each other. A digital camera was employed to capture the moiré patterns, which will generate the image to be processed. The procedure of image analysis involved softwares as Microsoft Powerpoint, Coreldraw and Idrisi. The use of a highly continuous sinusoidal grid, of a collimated light beam, of a higher resolution Charge Coupled Device Camera, as well as the superposition of image discontinuities during the unwrapping procedure included in the phase shifting method, avoided noise occurrence satisfactorily, improving image quality.The conception of tests leading to height determination to generate a topographic description of a plant model was conceived based on optical moiré techniques. Obtained results reveal a great potential of the proposed method in determining plant architecture with high precision, at low cost and being not time demanding.
Volume estimation of strawberries, mushrooms, and tomatoes with a machine vision system
International Journal of Food Properties
A computer vision technique was used to determine the volume of raw agricultural products with an irregular shape. Thirty images of each rotated raw roma tomato, salad tomato, white button mushroom, and strawberry were collected. Volume determinations of these products using the optical imaging system were compared to volume measurements collected using a water displacement/buoyant force method. A high correlation was found between measurements from both methods. Furthermore, regression analyses of weight and volume measurements of each product set were used to develop equations to predict object volume from object weight. Weight measurement of raw produce can be used as a non-destructive method to estimate unit volume for sorting and grading purposes.
Three-Dimensional Shape Measurement of Strawberries by Volume Intersection Method
Transactions of the ASABE, 2006
Strawberries are one of the most popular fruits in Japan, and are profitable for farmers because of their high price. However, much time and labor are required for harvesting, grading, and packing of strawberries, which has hindered the expansion of production scale, so the processes need to be automated. In this study, we developed a method of obtaining the 3D shape of strawberries in order to automate the grading and packing processes. The 3D shape of an object can be measured by using a laser scanner; however, the method is expensive and time-consuming. In this study, nine mirrors were set up around the object at an angle to reflect side views of the object, and the side views were taken simultaneously with a digital camera. The 3D shape was reconstructed from the nine side views by the volume intersection method, and was compared with data measured by a laser scanner. The results showed that the 3D shape of strawberries could be measured by the proposed method. The positional root mean square errors in the reconstructed contours were between 0.5 and 2 mm for most of the tested samples.
Determination of orange volume and surface area using image processing technique
2009
A b s t r a c t. In this paper, an accurate image processing algorithm for determination of volume and surface area of orange is developed. The proposed machine vision system consists of two CCD cameras, an appropriate lighting system and a personal computer. The cameras are placed at right angle to each other in order to give two perpendicular views of the image of the orange. Initially, the algorithm segments the background and divides the image into a number of frustums of right elliptical cone. The volume and surface area of each frustum are then computed by the segmentation method. The total volume and surface area of the orange is approximated as the sum of all elementary frustums. The difference between the computed volumes and surface areas obtained by the image processing method and measured by water displacement and tape method, respectively, are not statistically significant at the 5% level. The Bland-Altman results show that the orange size has no effect on the accuracy of estimated volume and surface area found by the image processing technique. The regression formula, M=0.68V IP +44.6, between the computed volume and the measured mass of oranges is found to be highly correlated with R 2 =0.93. K e y w o r d s: orange, volume, surface area, mass, image processing, segmentation method
IMAGE PROCESSING METHOD TO DETERMINE SURFACE AREA AND VOLUME OF AXI-SYMMETRIC AGRICULTURAL PRODUCTS
International Journal of Food Properties, 2002
An image processing based method was developed to measure volume and surface area of ellipsoidal agricultural products such as eggs, lemons, limes, and peaches. The method assumes that each product has an axisymmetric geometry and is a sum of superimposed elementary frustums of right circular cones. The product volume and surface area are calculated as the sum of the volumes and surface areas of individual frustums using Matlab 1 . The dimensions of individual frustums are determined from a digitized picture of the product acquired by a Charged Coupled Device (CCD) camera and processed in Adobe Photoshop 1 . The volumes and surface areas computed showed good agreement with analytical and experimental results. The developed method proved to be accurate, precise, and easy to use.
Development of a Machine Vision System for Modeling Surface Area and Volume of Orange
In this paper, a machine vision system for determination of surface area and volume of orange is presented. The proposed machine vision system consists of two CCD cameras, an appropriate lighting system and a personal computer. The cameras are placed at right angles to each other in order to capture two perpendicular views of the image of the orange. Initially, the algorithm removes the background by subtraction technique. The image of the main produce is then divided into a number of frustums of right elliptical cones. The surface area and volume of each frustum are computed by the segmentation method. The total surface area and volume of the orange is then approximated as the sum of all elementary frustums. The difference between the computed surface areas and volumes obtained by the proposed machine vision system and measured by classical methods (tape method and water displacement, respectively), were not statistically significant at the 5% level. The Bland-Altman results indicated the orange size has no effect on the accuracy of estimated surface area and volume of the oranges found by the image processing technique. The characterization results of oranges showed that the computed volume and measured mass parameters are highly correlated with a coefficient of determination of 0.93. Therefore the same algorithm may be used to grade the oranges based on the mass using the estimated volume information that has already been computed by segmentation method. The developed algorithm is quite general and may be readily extended for surface area and volume computation of other axi-symmetric fruits such as melon, kiwifruit, pomegranate, and pear. The proposed method is rotationally invariant and does not require fruit alignment on the conveyor and may easily be integrated in a multi-product sorting system for grading citrus.