Advertisement: RSM mid banner
Advertisement: partnersand mid banner
Advertisement: Kao Data Centre mid banner
Advertisement: CJBS mid banner
Advertisement: Mogrify mid banner
ARM Innovation Hub
Mid banner advertisement: BDO
Advertisement: S-Tech mid banner 3
Barr Ellison Solicitors – commercial property
Advertisement: TTP
Advertisement: Cambridge Network mid banner
Advertisement: Simpsons Creative
Advertisement: HCR mid banner
Advertisement: Wild Knight Vodka
Advertisement: EBCam mid banner
12 June, 2019 - 21:47 By Kate Sweeney

Deep learning tech promises crop-timisation

Earlham Institute

Researchers in the Zhou Group at Earlham Institute (EI) in Norwich have collaborated with Ely-based G’s Growers to develop a machine learning platform which works with computer vision and ultra-scale images taken from the air to help categorise lettuce crops in fields.

The advanced software in the AirSurf-Lettuce system includes measuring quantity, size and pinpointing location to help farmers harvest with precision and getting the crop to market in the most efficient possible way. 

The 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 highly important that farmers and growers understand precisely when crops will become harvest-ready so 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.

Another barrier to efficiency is the fact that inclement weather conditions, which have been increasing in recent years, can throw off harvesting times quite significantly, as crops take different lengths of time to mature.

The AirSurf technology – developed by members of the Zhou Group, including first authors of the paper on the project, Alan Bauer and Aaron Bostrom – uses deep learning combined with sophisticated, ultra-wide-scale imaging analysis to measure iceberg lettuce in a high-throughput mode. 

This is able to identify the precise quantity and location of lettuce plants, with the additional advantage of recognising crop quality, i.e. small, medium or large lettuce heads.

Combining this system with GPS allows farmers to precisely track size distribution of lettuce in fields, which can only help in increasing the precision and effectiveness of farming practice, including harvest time.

First author, Alan Bauer said: “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.”

Group leader Dr Ji Zhou added: “My lab is keen to seek every possible approach to translate our public funded research in algorithm design, machine learning, computer vision, and crop phenomics to techniques and tools that can be used by academic and industrial partners to address challenging problems in crop research and crop production.

“Utilising our research work supported by BBSRC and other public and industry jointly funded projects, we have partnered with G’s, leading vegetable growers in the UK, to equip our Agri-Food sector with smart and precise crop surveillance and analytical methods, for which we are confident that better crop management decisions and enhanced crop marketability could be achieved through our joint efforts.“

Industry partner at G’s Growers’ innovation manager Jacob Kirwan, added: “Farming at a large scale means that precision is essential when ensuring that we are producing crops in an environmentally and economically sustainable way. 

“Using technology like AirSurf means that growers are able to understand the variability in their fields and crops at a much higher level of detail that was previously possible.

“The decisions that can then be taken from this information, such as varying applications of inputs and irrigation; changing harvest strategies and planning the optimum time to sell crop, will all contribute towards increasing on farm yields and improving farm productivity.”

Newsletter Subscription

Stay informed of the latest news and features