Quality check of food quality using computer vision
Tags: Computer Vision, Machine Learning, TensorFlow, Quality Assurance, Computer Science

Traditionally food inspection has been done through human inspection a process which was tedious, expensive, destructive, and most a time wasn’t reliable given the uncountable human errors. Currently, the use of technology for food inspection has been introduced globally and has proven effective. The increased consumer awareness and quality demand in the market have created the need for quality sorting and delivery of top quality products in the market for it is a necessity not just for consumer taste but also a need for human health. Computer vision process provides an alternative for a computerized, non-destructive, cost-effective technique to accomplish the task. The process currently is applied to assessing the quality of fruits, vegetables, and eggs, and other food products. The process includes capturing, processing, and analyzing images, facilitating the objective and non-destructive assessment of the visual quality characteristic of food products.

Fresh Apples

The basics of computer vision quality check on food products.

Image acquisition.

A digital image is produced by several image sensors which include many light-sensitive digital cameras, range sensors, radar, tomographic sensors, ultrasonic cameras amongst others. Depending on the quality of the sensors used the image produced can be 2D or 3Dimage or image sequences.

Preprocessing.

This is a process used to determine whether the images contain specific data required for the process. The process includes Resampling, to assume that the image coordinates system is correct. Noise reduction to ensure that the sensor noise does not cause false information, contrast enhancement to ensure all details are detected, and scale-space representation to ensure that image structures are at a locally appropriate scale.

Feature extraction.

Specific features are extracted from the images which will help determine the quality of a product. This feature may include line edge and ridges or localized interest points such as bolds and points while more complex features may be related to motion, shape, or texture.

Detection or segmentation.

This is the step of the process where an image point or image region may require more or further processing. In this process, there is what is known as image recognition, where classifying detected objects into different categories, and image registration, where comparing two or more part of an image happens to come up with different views of the same object.

Rotten Apples

Decision making.

The final part of the process which is required for the application. It determines whether the product passes or fails on the food inspection and also does the match or no match on the application process.

The process of quality food check using computer vision.

Sorting process.

This a process where cameras are used and images are sent to the computer server for analysis and a decision is made from there. A single-camera with mirrors on the side to enable capture of dimensional images can be used or multiple cameras can be used for the image capture process. Isolated single boxes with sufficient lighting can be used to overcome the light variation problem and get better images. After the images have been processed a decision is made and the products that pass continue to the quality store while those that fail are pushed back to the store through electronic valves controlled by circuits that open and close based on the decision made. The size of the image determines the accuracy of the decision made. A bigger resolution may take a lot of time to process though it gives the best result while low resolutions are processed faster the accuracy may be questionable. One should rely on a resolution that will give accurate results and safe on time at the same time.

Image segmentation.

This is the process of dividing images into regions with characteristics. At this stage, an image is segmented into two classes the object and background. An object is detected and the area of the object is analyzed to detect damages in it. The process depends on the nature of the product and requires classification. The damage detected can be a colour change, hole, or cracks. Image segmentation is used to extract the damaged areas from the product and determine also if the noted change like a hole presents damage to the product or not. Image segmentation has two approaches the boundary estimation using edge detection and regional baed segmentation. In region-based segmentation, an image of the product is taken in low resolution which does not affect the accuracy of the result with noises in the background, and colour on the image is used to detect damage in the product. The defected part is compared with the colour of the rest of the product body.

Classification.

The colour is the main factor for acceptance of a product through this method though it is not the only factor. The colour in the spot of a product compared to the colour of the whole product helps to give the final decision. The cracks in products determined by the use of the edge dimension detection determine the decision on those products. All these factors detected in the segmentation stage more stages are required for the decision to be arrived at. Classification stage determines whether the spots or cracks found in the segmentation and edge detection stage are bad or good.

Stimulation and result.

The pc is set for sorting results from the images based on their resolutions with similarities and many differences. A database is used to test the results from the sorting system and the size of the image is used as a trade-off between small unclear images and high-resolution images which are time-consuming during the process. The results are based on the speed and accuracy of the system.

Computer vision for food quality is the best for the industrial process for it is an accurate, safe, time-saving, and cost-effective process compared to manual quality check that could come with inaccuracy due to destruction and time wastage. This process provides hygiene, rapid, economic, consistent, and objective quality assessment for products.to increase food quality assessments in industries and firms employing the computer vision assessment is the way out for quality food manufacture and supply globally.