A new method for estimating surface area of cylindrical fruits (zucchini) using digital image processing (original) (raw)

SIZE DETERMINATION OF APPLE AND ORANGE FRUITS USING THE IMAGE PROCESSING TECHNIQUE

In this study, the image processing technique is presented for determination of volume of apple and orange fruits. Volume of each apple and orange fruits were estimated using the image-processing technique and then compared with the exact volume determination using the water displacement method, and volume estimation computed by mean fruit diameter. The experimental setup was consists of a digital CCD camera, an appropriate lighting system, a diffuser plate and a personal computer. The LSD-test results indicated that the volume determined with image processing was not significantly different from the volume measured with water displacement, whereas the volume determined with geometric mean diameter was significantly different at 95% and 99% confidence from the volume measured with water displacement. The computed volume and the measured mass of apples and oranges are correlated in the form of M=0.693V+45.85, and M=0.929V+13.84, respectively. Results obtained in this study indicates that the image-processing technique can easily be employed in automated sorting and grading of apple and orange fruits. INTRODUCTION Nondestructive quality evaluation of fruits and other agricultural products is important for the food and agricultural industry. Fruits and vegetables are graded based on their external factors like size, shape, color, external damage, etc. Classification of fruits based on size is one of the most important tasks performed in the packinghouses [1].Fruit size estimation is also helpful in packaging, transportation and marketing operations [2]. The size of an agricultural produce is frequently represented by its mass because it is relatively simple to measure. However, volume-based sorting may provide a more efficient method than mass sorting. On the other hand, physical attributes of fresh products such as density, mass, surface area and volume, have often been used to calculate heat transfer, respiration rates, water loss, quantity of pesticide applications, evaluation of fruit growth and quality, ripeness index to forecast optimum harvest time, grading and so on [3-8].

A Review of Volume Estimation Techniques of Fruit

2015

Image processing is a process of understanding, analysis and modification on the image. Based on image processing some of the techniques were developed for volume estimation of fruits like as lemon, orange, mango etc. Volume estimation of fruit is use in packaging industries. This paper review different type of methods like as Monte Carlo method, water displacement method and color image segmentation technique and algorithm like as Image analysis algorithm and Canny Edge Detection algorithm for volume estimation of fruits. This paper includes advantages and disadvantages of the all methods of volume estimation of the fruits and also describes comparison of all methods. Volume estimation of fruits has some of the problems like it is time consuming process and accuracy of result. Keywords— Fruit, Volume measurement, Segmentation.

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.

IJERT-A Review of Volume Estimation Techniques of Fruit

International Journal of Engineering Research and Technology (IJERT), 2015

https://www.ijert.org/a-review-of-volume-estimation-techniques-of-fruit https://www.ijert.org/research/a-review-of-volume-estimation-techniques-of-fruit-IJERTV4IS010259.pdf Image processing is a process of understanding, analysis and modification on the image. Based on image processing some of the techniques were developed for volume estimation of fruits like as lemon, orange, mango etc. Volume estimation of fruit is use in packaging industries. This paper review different type of methods like as Monte Carlo method, water displacement method and color image segmentation technique and algorithm like as Image analysis algorithm and Canny Edge Detection algorithm for volume estimation of fruits. This paper includes advantages and disadvantages of the all methods of volume estimation of the fruits and also describes comparison of all methods. Volume estimation of fruits has some of the problems like it is time consuming process and accuracy of result.

Prediction of raw produce surface area from weight measurement

Journal of Food Engineering, 2006

Determination or estimation of the surface area of various foods can have a wide variety of applications for food producers and processors. The objective of this study was to develop a computer vision technique to rapidly determine the surface area of objects with irregular shape. The imaging system developed acquires and stores images of multiple projections of an object. Surface fitting and approximation of a 3-D wire-frame model were used to calculate object surface area for apples, cantaloupes, strawberries and tomatoes. Weight and surface area measurements of each fruit set were used to develop equations to predict fruit surface area (cm 2 ) from raw fruit weight. One application of these equations can be for food microbiologists to obtain more accurate determination of surface microbial populations. Analysts can gravimetrically sample whole produce to estimate product surface area and, therefore, report microbial concentrations per area.

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

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.

Non-destructive and Destructive Methods to Determine the Leaf Area of Zucchini

Journal of Agricultural Studies

Leaf area estimation is a very important indicator in studies related to plant anatomy, morphology and physiology, and in many cases, it is a fundamental criterion to understand plant response to input conditions. Although there are leaf area prediction models have been produced for some plant species, a leaf area estimation model has not yet been developed for the zucchini. The objective of this paper was to determine the leaf area based on destructive and non-destructive methods for zucchini. The accuracy of measurement methods was evaluated and compared to determinations of the leaf area by the scanning integration method (LICOR equipment LI 3100C), considered as standard procedure. Non-destructive methods consisted of digital photography and measurement of leaf dimensions (width and length) based on ImageJ software. The destructive methods used were a) leaf area integrator LI-3100C, b) determination of leaf mass and c) weighing of leaf discs punched from the leaves. According to...