AI Case Study

Researchers from the University of Lincoln have built a learning computer system to early detect potentially harmful flaws in production and packaging of potatoes

Researchers at the University of Lincoln's Robotics Lab have deployed computer vision and machine learning algorithms to train algorithms to be able to detect, identify and quantify many of the common blemishes affecting potatoes. By differentiating between normal and abnormal states of products the learning computer system will be able to sort poratoes and early detect potentially harmful flaws in production and packaging.

Industry

Basic Materials

Agriculture

Project Overview

"TADD - or the Trainable Anomaly Detection and Diagnosis system - is able to "detect, identify and quantify many of the common blemishes affecting potatoes", Dr Tom Duckett of the University of Lincoln told the BBC."

"This research is achieving impact in several areas within the food industry, including quality analysis of fresh produce, food processing and food packaging. The technology was trialled in 2012 at the leading post-harvest applied research facility for agricultural storage in the UK, and was also licensed to a world-leading supplier of food packaging machines and equipment for inclusion in a new product range under development. The longer-term impacts include safer food, reduced food waste, more efficient food production, and better use of natural resources (e.g. reduced use of water, pesticides and other inputs), through early detection of potentially harmful flaws in production and packaging.

A second version of the TADD prototype system was built (1Q 2013), incorporating a larger chamber and technical improvements to meet commercial specifications, and was evaluated in trials at Sutton Bridge Crop Storage Research (SBCSR), which is the leading post- harvest applied research facility for agricultural storage in the UK." (impact.ref.ac.uk)

Reported Results

"The longer-term impacts include safer food, reduced food waste, more efficient food production, and better use of natural resources (e.g. reduced use of water, pesticides and other inputs), through early detection of potentially harmful flaws in production and packaging.

TADD was tested in 2012 with a local potato-buying firm, Branston, where it has performed at least as well as its human teachers."

Technology

"Computer vision and machine learning algorithms that automatically learn salient image features (e.g. colour and texture) to differentiate between expected and anomalous states of a given product".

Function

Operations

General Operations

Background

"The UK potato industry is worth about £3.5bn, but much of the sorting of produce is still done by hand."

Benefits

Data