Cornell University engineers and plant scientists have teamed up to develop a low-cost system that allows grape growers to predict their yields much earlier in the season and more accurately than costly traditional methods.

The new method allows a grower to use a smartphone to record video of grape vines while driving a tractor or walking through the vineyard at night. Growers may then upload their video to a server to process the data. The system relies on computer-vision to improve the reliability of yield estimates.

When workers manually count clusters on a vine, accuracy greatly depends on the person counting. In an experiment, the researchers found that for a panel of four vines containing 320 clusters, manual counts ranged from 237 to 309. Workers will count the number of grape clusters in a small portion of the vineyard to get an average number of clusters per row. Farmers will then multiply the average by the total number of rows to predict yields for a vineyard. When cluster numbers are miscounted, multiplying only further amplifies inaccurate yield predictions.

The researchers intend for this app to be open sourced, and the machine learning components setup such that users simply upload their video to a server that will process the data for them.

> Source: PHYS.ORG