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Peach blossom mapping

Researchers at the University of California, Davis (UCD) have recently developed two innovative machine vision techniques to improve agricultural efficiency in areas plagued by labour shortages.

As David Slaughter, Professor of Biological and Agricultural Engineering at UCD explains, the team has been using advanced machine vision technology to develop automated systems that perform very labour intensive tasks like weeding or fruit-thinning. 

"These activities are typically drudging tasks and California farmers have been facing an increasingly difficult prospect in finding sufficient amounts of legal season labourers to perform these tasks since the stronger border controls have been put in place since 9/11," says Slaughter.

As part of a project funded by the California Tomato Research Institute, the team has been working on an automated system to detect and kill weeds growing among tomato plants on a farm; a particularly challenging task for machine vision because the weed foliage is "often intermingled with the tomato foliage on a real farm" making it difficult to sort using typical machine vision methods.

Hyperspectral imaging

To solve this problem, the group has developed a hyperspectral imaging system that automatically identifies plant species.  The system is similar in concept to a colour machine vision system, except that instead of three colour bands (red, green and blue), it distinguishes between several hundred narrow spectral bands across the visible and near-infrared spectrum.

The group has developed a hyperspectral imaging system that automatically identifies plant species. 

"By using more detailed spectral information, we can extract the spectral fingerprint of each pixel in the image and use advanced multivariate machine learning techniques to identify the most probable plant species associated with that spectrum," explains Slaughter.

Hyperspectral weed control system
A 3-D stereo machine vision system uses high-resolution colour cameras to detect blossoms and determine their 3-D coordinates.

With the help of colleague Prof. Ken Giles, the team has developed a thermal micro-dosing system that can take the weed pixel-map generated by the hyperspectral imaging system and spot-treat, leaf-by-leaf, each weed in a field using heated food-grade oil, thus creating a robotic weed-control system that can be used by organic farmers to automatically control weeds growing between crop plants in a field.

The next step for this technology may be commercialisation, as there has been some interest by a group of former Stanford University students in forming the start-up company, Blue River Technology.  

Robotic blossom thinning

In a second study, funded by the USDA Specialty Crops Initiative, the team has developed a peach blossom mapping system for a robotic blossom thinning machine. 

A 3-D stereo machine vision system uses high-resolution colour cameras to detect blossoms and determine their 3-D coordinates.

"The challenge is to accurately map the location of each blossom on the peach tree so that the 3-D coordinates of blossoms that need to be removed can be transmitted to the robotic arm performing the mechanical thinning task," says Slaughter.

For this application, they have developed a 3-D stereo machine vision system that uses high-resolution colour cameras to detect blossoms and determine their 3-D coordinates - employing graphical processing unit hardware to perform the stereo matching task in real-time.  The system is mounted on a mobile platform with GPS and Lidar sensors to simultaneously map blossoms, trunks and branches in 3-D with the GPS coordinates of the tree. 

3-D stereo machine vision system peach blossom mapping reconstruction
3-D stereo machine vision system peach blossom mapping reconstruction

"In orchard tests in California, we have validated the concept and can accurately locate the 3-D coordinates of each blossom to within a blossom-width," says Slaughter.

Written by Andrew Williams, Contributing Editor, Novus Light Technologies Today

Labels: hyperspectral imaging,imaging,machine vision,3D mapping

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