Literature survey image processing

Literature survey image processing

Literature survey image processing Computer vision researchers have long been trying to propose methods for visual sorting and grading of fruits. Sorting of fruits can be done mostly based on their characteristics such as the colour of the fruit, size, surface irregularities. Some advanced techniques use laser imaging, fluorescent imaging, and spectroscopy for defect detection.

This section reviews various methods for and papers for sorting and grading of fruits and especially citrus fruits such as lemon and orange.

(Kondo & Ting, 1998) showed a fundamental setup to get the data such as colour, size and mass. The author provided a simple prototype for industry to classify the product and forward to proper channel. Modern sorters can sort fruits very fast at a speed more than ten fruits per second base on colour, shape, defects and stem detection.

(Jahnsa, 2001) sorted tomatoes based on computer vision techniques and observed that the tomatoes can be sorted based on mass using only image and computer vision. The absolute error was about 2.06%.

Mangoes can be sorted based on their colour and shape. The geometric features such as shape can be compared with reference shape. Shape analysis is a good feature for variety of mangoes. For grading purpose, the pixel value is another good feature. Pixel value greater than 100 means the skin is good and pure. This method has 83.3% accuracy (Pauly & Sankar, 2015).

Fruits such as mango can be sorted based on their maturity. A camera is used to acquire a digital image of mango. In the second step, the noise is removed using pseudo median filter. Image is then converted to binary for edge detection. The method is 90% accurate overall (Bipan Tudu, 2012).

To evaluate the quality of fruit, a new method was proposed using HIS colour model. A digital image of fruit, taken using CCD camera captured in RGB colour space was transferred into HIS colour space. Colour intensity histogram of only hue H channel was calculated. The histogram was provided to back propagation neural network as input. The output of the network was the description of quality of fruit (Cui, Wang, Chen, & Ping, 2013).

A date fruit sorting and grading system was proposed. The system consisted of software and hardware. The hardware section included a conveyor belt system with a camera integrated into it. A computer loaded with software was used to analyse the digital image of dates and classify. The over al accuracy was found to be 80%. The problem associated with detecting the flabbiness of fruit was observed (Ohali, 2011).

A robot was designed to identify and pick fruits automatically using computer vision. A physical system was designed that could be mounted to tractor. A camera was used to capture the images. The image was further processed to detect defective apples. A vacuum grabber was used to pick the apples. (Clowting, 2007)

Food colour measurement in computer vision applications was reviewed. The paper analysed the pros and cons of colour measurement for food was described and future scope and trend in the field was proposed (Wu & Sun, 2013).

A very intuitive method for apple defect detection was proposed. The method incorporated the automatic light correction. The method counted and distinguished between the true defect and stem end. The method used support vector machine for classification (Huang, Zhang, Gong, & Li, 2015).

(Jhawar, December 2015) proposed a lemon sorting system based on pattern recognition techniques such as nearest prototype, edited multi-seed nearest neighbour and linear regression. Features extracted were Mean Values of Red, Green, and Blue, size, standard deviation and min max values of the grey level image. They collected their samples from different locations in India consisting five different breeds. The scope of their research was limited to only ripeness measurement. Our model closely resembles this model in ripeness measurements but goes beyond this research in terms of defective fruit detection. Their system was able to perform at 100% accuracy using linear regression.

(Seema, Kumar, & Gill, 2015) prepared a fruit recognition system to sort mixed fruits based on type of fruit. The features used for fruit recognition were shape, size, and colour. They got an accuracy of 100% based on 120 samples.

(Khojasteh, 2010) proposed a lemon grading embedded system based on colour and volume only. No defect-based classification was done. Greenish lemons with smaller size were considered as grade B, while larger yellowish lemons were considered as grade B. They used two cameras to cover the maximum lemon area.

(Momin, 2013) proposed a very advanced technique for lemon defect detection. Fluorescent imaging was the base of the research since it has been used to extract the florescent component from the peels of citrus fruits. A technique of spectroscopy was used to identify the fluorescent components. Fluorescent components and spectroscopy helps identify the chemical composition of lemon peel, which in return can be used for defect detection. The technique had a success rate of around 85%.

(Khoje, 2013 ) used Curvelet transforms to for pattern recognition. Fruit quality was assessed using pattern recognition techniques. Curvelet transform is a multi-resolution technique that works on lower and higher resolutions to extract both local and global features related to fruit’s surface. The technique was evaluated on lemons and guava. Textural features extracted from Curvelet transform were standard deviation, energy, entropy, and mean. Probabilistic Neural Network and Support Vector Machine were trained using these features and performance was evaluated for two classes, healthy and defective. SVM performed better and provided an accuracy of 96%.

(Swapnil S. Pawar & Dale, 2016) designed a system to recognize a fruit based on features such as roundness value and colour. If the object is recognized as fruit using K-Nearest Neighbours, then the fruit as subjected to defect detection. A simple thresholding was used to isolate defective area. If the pixel value exceeds a threshold value, then it belongs to the pure skin otherwise the pixel belongs to the defective area. All such pixels are counted to get the total defective area.

(Iqbal, 2016) devised an approach to sort citrus fruits especially lemon, oranges and sweet-limes. I single view image was proven enough for classification based on colour features. Only hue from HSV colour space was used for classification. Different approaches such as colour distance, linear discriminant analysis and probabilistic distribution were used to evaluate the classification accuracy. An accuracy of 90% was obtained based on colour classification. Moreover, colour variability was used for fruit maturity analysis. Colour variability was measured using hue mean and hue median.

Defect detection on the spherical fruits is a tough task due to uneven lighting around the spherical shape. The study covered different defects like scarring and copper burn, which are common in oranges. Non-uniform spherical orange images were transformed using Butterworth filter resulting in even lighting distribution. It was observed that the stem end was detected as defect in the algorithm. Red and Green ratio in colour image along with big area and elongated region removal algorithms were used to detect stem end. The method detected defects extremely well with an accuracy of 98.9%. However, the method could not discriminate the types of defects (Li, 2013).

(Blasco, 2014) designed an automated system for citrus fruit harvesting. The authors realized that the field conditions vary massively. To make the system consistent, a good and efficient lighting system was necessary. Moreover, a low power processing unit and image acquisition system was required.

Our method used various techniques presented in different papers. The methods have been combined and modified according to requirements.

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