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Lassonde prof partners with fintech agricultural company to improve crop yield predictions using graph signal processing and deep learning


While many are aware of the use of mathematics and machine learning techniques in the fields of computer science, engineering, physics and medicine, have you ever wondered how well these techniques fare in literal fields or pastures?

Pretty well actually, as Professor Gene Cheung, from the Lassonde School of Engineering at York University, discovered while working with Growers Edge to improve crop yield prediction using graph signal processing and deep learning.

Growers Edge is a fintech company operating in the agricultural space, providing decision-making tools, risk management and financial solutions to agricultural retailers and producers. One way they do this is by providing warranty-backed and data analytics-supported crop growing prescriptions to farmers: a combination of growing practices and the use of certain products or seeds to help maximize crop yields. “Crop yield prediction helps farmers determine benchmarks,” says Tim Eadie, Data Engineer at Growers Edge. “Sometimes they have production history but many times they don’t.” Crop models help fill in the gap for those who do not have a ten-year production history on their farms.

Yield prediction error in all the countries using denoised features

Although the team at Growers Edge was already implementing deep learning in their crop yield modelling, they needed to find a way to overcome challenges to improve prediction performance. This is where Professor Cheung’s expertise in graph signal processing (GSP) comes in. With his help, Growers Edge can apply GSP to help denoise relevant features before feeding the data into the deep learning model. A graph is a mathematical abstraction modeling correlated data, with nodes (representing fields / counties) and edges (encoding pairwise similarities). Graph examples in the real world include sensor networks (connecting wireless temperature in a forest), social networks (connecting friends on Facebook), etc. In this case, each node represents a particular county described by a set of environmental variables (a feature vector) such as precipitation or sunshine and vegetation indices (EVI, NDVI) of that area.

Feature of different counties in Iowa as a discrete signal on a combinational graph

“The key insight is that one county ought to be similar to its geographical neighbours,” explains Professor Cheung. “Therefore, both data and ultimately crop yields ought to be correlated as well.” The similarity of neighbouring nodes is reflected in the graph’s edge weights which are calculated by incorporating not only the geographic distance between the two counties but also the similarity of reliable environmental variables such as clay composition. In turn, this graph-based approach can be used to denoise unreliable features such as satellite-based EVI and NDVI by averaging out the values from neighbouring counties that are geographically near and environmentally similar. Cheung and Eadie applied this graph-based denoising to USDA corn data from 10 states in the corn belt encompassing nearly a thousand counties. They denoised the unreliable EVI features before feeding the data to their DL model and improved its accuracy by 0.434% compared to the previous approach. While this percentage improvement may not seem large at face value, Eadie reminds us that these predictions are for bushels per acre and when farmers are dealing with hundreds of thousands of acres these numbers add up quickly.

By combining graphs with both spatial and temporal modelling using earth observation data in the context of crop yields, the research team is paving new ground. “Graph signal processing is an excellent approach to capture correlations,” says Professor Cheung. “However, at this time nobody uses it for crop prediction, so we need to convince people in this field that it is a meaningful approach.” Growers Edge is certainly onboard.

“Some of the best research and solutions for many of the world’s most pressing issues have been born out of academic and private partnerships,” said Growers Edge CEO Dan Cosgrove. “In collaboration with our team, Professor Cheung and his student assistants have helped build a solution that can positively impact the entire U.S. ag industry for years to come. We value the contributions of Professor Cheung made on this project and look forward to finding creative solutions to problems in the future!”

As an applied mathematician, Professor Cheung is always looking for impactful applications and once he realized that graph signal processing was applicable to crop yield prediction, he and Eadie started to collaborate. Cheung is now an advisor on the Grower Edges Data Advisory Board, and his PhD student, Saghar Bagheri, has started a Mitacs internship at Growers Edge. Their next step is to continue refining their model and design the best possible graph for the deep learning architecture.

“It has definitely been a challenging but exciting experience,” says Eadie. “We’re discovering newer and more innovative ways to tackle a problem that ultimately helps and empowers farmers.”