A²I : Scrutinising different types of green-groceries through machine vision

  • August 10, 2018

A holistic approach to improve food supply chain efficiency

Food, shelter & clothing are the most basic & essential needs of mankind. The challenge of supplying nutritiously fresh, affordable and processed foods to 9 billion people in 2050 will require a 70 percent increase in food production which can be met by strategizing to maximize the efficiency of available resources. Hence, Food processing is one of the major manufacturing sectors in the world. Food processing mainly involves transforming raw food ingredients by using a set of methods & techniques to produce marketable food products that can be easily prepared and served to the consumer at affordable prices.

For food manufacturers, Artificial Intelligence(AI) is being used to improve both their production processes and their products. Apparently, it might not seem to be the case but, food processing is actually dominated by big data problems. For problems in consumer desire, ingredient sorting, and recipe development — AI is providing the most workable alternative to human expertise.

Food processing often comprises sorting a large quantity of feedstock and careful inspection of the final product. Among manufacturing operations, one challenge that is relatively unique to food processing plants is that the feedstock is often not uniform. To know if, say, a potato or an apple is usable requires an assessment of its size, shape, colour, and marks. At the turn of the 20th century, according to TOMRA, a leader in food sorting technology, 90 percent of all food sorting was done by professional inspectors who were capable of making these highly complex assessments at a reasonable speed.

A comprehensive approach for improving food supply chain efficiency and developing sustainable agrifood systems to the ever-changing world is needed. In order to achieve this, automatic systems are now being developed to collect hundreds of pieces of data on a single produce and rapidly make an assessment about it within a few seconds.

Machine learning creates the capability to sort foods by sending the exact same determination every single time. The sorting machines uses cameras, Near Infra Red (NIR) spectroscopy, X-rays, and lasers to gather and process all that data from hundreds of individual ingredients as they rapidly move across a conveyor belt. These systems can bring down labour costs significantly and produces higher yield & optimised quality.

Another classic example of advancement of AI in agrifood sorting industry is where a Brazilian research group worked vigorously on developing an application that enables papaya growers to send the ripest papayas to local markets and saving less ripe papayas for export. Hence, reducing food waste and appreciably adding value to the product.

Detailed experimentation of classification of different produce according to the ripeness level using machine learning and computer vision techniques is explained in this paper.

Hence, AI is not a futuristic concept — it can be leveraged today to make operations more productive, efficient and profitable. With the speed of the marketplace and the rate of exponential change in the resource-constrained world, exciting insights in the food industry will be possible in the near future.