Earlham Institute’s AirSurf-Lettuce platform

Innovative lettuce imaging UK

Earlham Institute researchers have created a machine learning platform to categorise lettuce crops using computer vision and aerial images. The platform, called AirSurf-Lettuce, is capable of scoring iceberg lettuces with high accuracy of greater than 98 per cent, according to the study.

The researchers conducted field trials at G's Growers, the second largest vegetable grower in the UK, based in Ely. Aerial imagery is used by crop researchers, growers and farmers to monitor crops during the growing season.

To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solutions are required, which not only produce high-quality measures of key crop traits, but also support farmers to make prompt and reliable crop management decisions.

The software includes measuring quantity, size and pinpointing location to help farmers harvest with precision and getting the crop to market in the most efficient way. Importantly, this technology can be applied to other crops, widening the scope for positive impact across the food chain.

Lettuce is big business, especially in East Anglia, with 122,000 tonnes produced in the UK each year. Up to 30 per cent of yield can be lost due to inefficiencies in the growing process as well as harvest strategies, which, if made up, could provide a significant economic boost.

It's very important that farmers and growers understand precisely when crops will become harvest-ready, so that they can set in motion the planning of logistics, trading and marketing their produce further along the chain.

Traditionally, however, measuring crops in fields has been very time-consuming and labour intensive, as well as prone to error; therefore novel AI solutions based on aerial images can provide a much more robust and effective method.

With these systems farmers can to track size distribution of lettuce in fields, which can only help in increasing the precision and effectiveness of farming practice, including harvest time.

Imveurope.com quoted first author, Alan Bauer, as saying: ‘This cross-disciplinary collaboration integrates computer vision and machine learning with the lettuce growing business to demonstrate how we can improve crop yields using machine learning.’

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