Machine’s Eye View of Poultry

Take another look at the hardhat and safety glasses on the food safety inspector. Mounted on the hardhat is a small camera and a flashlight that gives off specially filtered light.

Take another look at the hardhat and safety glasses on the food safety inspector. Mounted on the hardhat is a small camera and a flashlight that gives off specially filtered light.

The safety glasses are actually a wearable miniature computer monitor that displays data from a miniature computer on the inspector’s belt. The data tells the inspector whether there is any fecal matter on the processing equipment.

Another inspector might be looking through what at first looks like a pair of ordinary binoculars. But these binocular lenses filter special bands of light to check for disease, defects, or fecal matter on the meat, produce, or equipment.

There’s also a hand-held device that shines filtered light to do a sanitation check of the processing plant. The device has a camera that sends images to another eyewear-mounted computer display. White specks on the image reveal fecal matter.

Although these gadgets sound like something dreamed up by James Bond’s gadget man, cutting-edge prototypes like this actually exist in an Agricultural Research Service lab in Beltsville, Maryland.

At the Instrumentation and Sensing Laboratory (ISL), a team of scientists — led by Yud-Ren Chen and including biophysicist Moon Kim, agricultural engineer Kuanglin Chao, and visiting scientists from around the globe — design the portable inspection devices.

Chen, Chao, and visiting scientist Chun-Chieh Yang have finished work on a high-speed online imaging system for chicken inspection. They are turning over a prototype to industry as part of a co-operative research and development agreement with Stork-Gamco of Gainesville, Georgia, a major manufacturer of chicken-processing equipment. Chen and Kim and biomedical engineer Alan Lefcourt are working on a similar system for inspecting fruits and vegetables.

Because all these systems use optically filtered light and opto-electronics to “see,” they are called “machine vision” or “optical sensing” systems. At the heart of these machine vision systems is a digital multispectral camera that can take photos at different wavelengths simultaneously and can even detect light invisible to the naked eye. The systems include the latest, fastest cameras of this type. All the systems rely on two or three wavelengths chosen to do the best job of seeing special features.

Fully funded by ARS — with additional funds from industry —Chen’s team works with both industry and universities, such as the University of Kentucky and the nearby University of Maryland at College Park.

Sensing Remotely, Close Up
Machine vision using multiple images at selected wavelengths is also being developed for use in remote sensing of Earth by satellite imagery. But its potential for use in monitoring food safety and quality should be even greater, since the sensors are only inches away from the target object, and there is a wider range of applications.

The basic idea of machine vision is to supplement human inspectors with instruments that shine light on every single fruit, vegetable, meat, or poultry product as it speeds by on the processing line faster than ever. Typical lines today can process about 360 fruits per minute or up to 180 poultry carcasses per minute, for example.

The system developed by Chen’s team spots almost all biological conditions that cause inspectors to take a second look at chicken carcasses, such as signs of diseases that pose food safety risks or otherwise mar a chicken’s consumer appeal.

Chen’s team is now focusing its attention on apples, developing a system that could be used for other fresh produce as well. It can detect contaminants on the apple surface, such as fecal matter.

Stephen Delwiche, an agricultural engineer at the ISL, works with colleagues at the ARS Grain Marketing and Production Research Center in Manhattan, Kansas, on high-speed optical inspection of wheat and other grains. He uses near-infrared reflected light to detect proteins in wheat as well as scab and other molds.

Yud-Ren Chen is with the USDA-ARS Instrumentation and Sensing Laboratory, Bldg. 303, 10300 Baltimore Ave., Beltsville, MD 20705-2350; phone (301) 504-8450, fax (301) 504-9466.