Mapping fruit characteristics faster with vision and AI

Mapping fruit characteristics faster with vision and AI

Breeders who have developed a new tomato variety must register it before it can be introduced onto the market. For such a registration, the national examination office has to assess the new variety on a large number of characteristics. That is largely manual work. Within the European collaborative project INVITE, the Business Unit Greenhouse Horticulture and Flower Bulbs of Wageningen University & Research is investigating whether part of this can be automated by means of imaging and artificial intelligence (AI).

New varieties of flowers, plants, vegetables, and fruits are assessed according to the DUS method: is the new variety distinguishable from existing varieties (Distinctness), is the new variety and its possible fruits homogeneous (Uniformity), and are the character traits stable over consecutive seasons (stability). In order to establish whether a variety adheres to the DUS standard, bodies such as the Dutch Naktuinbouw score new varieties on dozens of characteristics. As scoring occurs manually and hundreds of varieties need to be checked for each crop each year, this procedure is highly labor-intensive. Within the European collaborative project INVITE several research institutes are now looking for solutions to automate these procedures.

Measuring and weighing tomatoes

In the case of tomatoes, a registration involves 61 DUS traits, of which at least a third relate to the fruit. This means that, in order to fulfill a year's worth of variety registration requests, experts have to manually measure, weigh, and assess thousands of tomatoes.

Continue reading.

Courtesy of Wageningen University and Research

Source: Wageningen University and Research

Share