Amazon India Vice President Machine Learning Rajeev Rastogi said at the “Amazon Smbhav” event that the company is building a computer vision-based grading solution for products such as onions and tomatoes. ”Quality is one of the key drivers of fruit and vegetable purchasing decisions, and a critical factor in achieving customer satisfaction”. Having humans grade the quality of fruits and vegetables by manually examining each individual piece of produce – each tomato or each onion is not scalable to millions of quality assessments per day, Amazon India Vice President Machine Learning Rajeev Rastogi said at the Amazon Smbhav event.
human’s innovation is just beyond expectation. Many Company just want to adopt application of image analysis and computer vision system in an automatic fruit recognition system. Computer vision system, a tool used in image analysis of fruit characterization.
In India, in view of the ever-increasing population, losses in handling and processing and the increased expectation of food products of high quality and safety standards, there is a need for the growth of accurate, fast and objective quality determination of fruits. A number of challenges had to be overcome to enable the system to perform automatic recognition of the kind of fruit or fruit variety using the images from the camera. Several types of fruits are subject to significant variation in color and texture, depending on how ripe they are. Applications for grading the fruit by its quality, size or ripeness are based on its appearance, as well as a decision on whether it is healthy or diseased.
E-commerce major Amazon said it is building a computer vision-based solution that can help in grading the quality of fruits and vegetables that are shipped to customers.
Company is building a computer vision-based grading solution for products such as onions and tomatoes. ”The ML (machine learning) based approach analyses produce images to detect defects such as cuts, cracks, pressure damage, etc. and can carry out millions of assessments per day at a cost that is far below that of any other method. We plan to develop a conveyor belt based automatic grading and packing machine,” he said.
Amazon also plans to use near-infrared sensors to detect attributes such as sweetness and ripeness. These cannot be detected in RGB (red, green, blue) images captured by traditional computer vision algorithms and require destructive methods such as eating the fruit.
Highlighting that there are numerous applications of machine learning across Amazon’s different e-commerce business verticals, Rastogi said the company is using ML to recommend products to customers, forecast future demand for products and improve the quality of product catalog by classifying products and eliminating duplicate products. ”We’re also applying ML techniques to rank products in search results, reduce packaging costs, improve address quality, and mine product design insights from reviews.
As vice president of machine learning at Amazon India, Rastogi is now helping his team drive innovations that have a profound impact not only on shoppers in India but also on the company’s customers around the world.
For instance,Now the associates with high infection risk scores can be prioritized for testing and quarantine actions,” said Rastogi. the Amazon India team has also developed CRISP mobile app to tackle the spread of the Covid-19 pandemic and provide a safe work environment for our fulfilment centre employees. The CRISP app uses Bluetooth signals on mobile phones to track social contacts between Amazon associates. This social contact data is used to alert associates when they breach social distancing norms, for example, when they come too close to other associates. It is also used to identify users with a high risk of getting infected with Covid-19 since they have directly or indirectly come in contact with associates, who have tested positive for Covid-19.
With the ever-increasing expectation of high quality in fruit and vegetables, the need for automation when inspecting the produce is also increasing. So, we are looking for computer vision /machine learning which can increase agricultural field in a good way.
Recent advances in machine vision technology have introduced the ability to be able to see beyond the surface of fruit and vegetables to detect the “hidden” bruising, mould, disease, and pests that can not be detected manually or by colour machine vision systems. These systems can even detect how ripe a fruit is, which is especially useful for fruits that do not change colour when they are ripe (eg avocados).
Machine vision system: A tool for quality inspection of food and agricultural products
Quality inspection of food and agricultural produce are difficult and labor intensive. Simultaneously, with increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. However, these operations generally in India are manual which is costly as well as unreliable because human decision in identifying quality factors such as appearance, flavor, nutrient, texture, etc., is inconsistent, subjective and slow.
Machine vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry.
Computer vision: Tasks include methods for acquiring, processing ,analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions.
Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
Assessment of fruits: Computer vision has been widely used for the inspection and grading of fruits. It offers the potential to automate manual grading practices and thus to standardize techniques and eliminate tedious inspection tasks. Kanali reported that the automated inspection of produce using machine vision not only results in labour savings, but can also improve inspection objectivity.
Computer vision has been used for such tasks as shape classification, defects detection, quality grading and variety classification of the apple. Paulus and Schrevens (1999) developed an image processing algorithm based on Fourier expansion to characterize objectively the apple shape so as to identify different phenotypes. Experimentation by Paulus et al. (1997) also used Fourier analysis of apple peripheries as a quality inspection/classification technique. This methodology gave insight into the way in which external product features affect the human perception of quality. The research found that as the classification involved more product properties and became more complex, the error of human classification increased. Leemans et al. (1998) investigated the defect segmentation of ‘Golden Delicious’ apples using machine vision. The proposed algorithm was found to be effective in detecting various defects such as bruises, russet, scab, fungi or wounds. In similar studies Yang (1996) assessed the feasibility of using computer vision for the identification of apple stems and calyxes which required automatic grading and coring.