Tech Tuesday
A Farm Robot in Your Future

A Farm Robot in Your Future

The dream of automating a range of ag tasks may be closer to reality; and USDA scientists fight the battle of managing biotech data.

Any fan of science fiction has dreamed a fair share of ideas of how automation could change the nature of agriculture, but these days those dreams are closer to reality. During the European Robotics Forum recently, attendees learned about ways that robotics have changed, and how these automatons could be working in a farm field near you soon.

ROBOT SCOUT: This Harper Adams University College image shows a computer rendition of a crop scout robot that can work in tandem with others to diagnose and protect a crop. The effect is less crop protection product use and more targeted application. (corrected version)

Originally, researchers took on the challenge of trying to recreate a human in robotic form. That approach meant complicated systems with human hand-eye coordination and advanced object recognition. Of course, that over-reaching approach yielded little practical result for the farm. But a new approach could make a big difference and the opportunity for robotics could be growing.

To get past those early overly complex approaches, a new take on farm robotics was needed. "We've started with a clean sheet of paper," says Simon Blackmore, head of engineering, Harper Adams University College, in the United Kingdom. "We're reevaluating the whole approach to agriculture. At the moment, crops are drilled in straight rows to suit machines, but what if they were drilled to fllow the contours of the land, or to take account of the micro level environmental conditions within a portion of a field?" He says the potential to boost production could be "staggering."

Delegates to the Forum got a look at multi-task robots from the Universities of Copenhagen, South Denmark, Wageningen and Kaiserslautern and the research institute at WUR in the Netherlands. Perhaps the most interesting is the robotic Crop Scout (shown on this page), which is a monitoring platform capable of measuring crops and checking for disease. In trials using the scout, researchers cut the crop spray used by 98% as the Robotic Sprayer sent by the Crop Scout treated only the small area affected by diseases or pests.

Essentially these are much smaller machines that can work cooperatively to carry out tasks. This "networked" approach to assigning the robots to specific tasks offers some interesting possibilities. However, while the dream may be closer to reality for robotics fans, don't park your tractors and sprayers yet - it'll be a few years before these "Roomba's" of the farm field have a chance. And they still must prove themselves commercially before you'll be writing a check for a few.

Source: Euro Robotics Forum 2012

Cattle Breeding Tool Helps with Plants

Plant breeders are dealing with more information than ever before thanks to the proliferation of DNA data that results from tests and trials. But researchers at USDA's Agricultural Research Service Plant, Soil and Nutrition Research Unit at the agency's Robert W. Holley Center for Agriculture and Health in Ithaca, N.Y. have found that by using a statistical approach called Genomic Selection, they can capture and exploit more of the data produced by the growing number of studies focused on DNA sequences found in plant genomes. GS is actually a technique currently in used for cattle breeding.

As scientists and plant breeders move toward the use of molecular tools to develop improved varieties, identifying genes associated with desirable traits can be a challenge, but can speed breeding since researchers don't have to wait to grow plants from seeds.

But to analyze those tools, researchers have to plow through a ton of data, and some of those traits - like drought tolerance - are multi-gene traits, each gene having a small impact. These genes are called quantitative trait loci, and the conventional marker assisted selection approach to handling data is limited in detecting those small effect QTLs.

The new recommended approach exploits more data - including those QTLs and estimating the impact of all the known genetic markers in a plant population.

The researchers recently constructed statistical models, using both GS and MAS approaches, and compared how well they could predict values associated with 13 agronomic traits in crosses made from a "training population" assembled for the study. They gauged the model's accuracy by comparing their predictions with field observations of 374 lines of wheat.

The results showed the GS approach was more accurate at predicting trait values. Jannink had similar success in a study using oats. Both studies were published in The Plant Genome. The work is expected to speed up molecular breeding efforts and should prove extremely useful, given the pace of advances in DNA technology.

Source: Agricultural Research magazine.

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