2021 IEEE WA Systems, Man, and Cybernetics Society Chapter Webinar

Webinar Title: How will the next-generation of sensor-based decision systems look in the context of intelligent agriculture?

Abstract for webinar: The development of cost-effective, digitally based decision support systems is a key challenge in optimising farm management. Yet, the majority of sensor-based decision tools which support fertiliser management have relied on simplistic mechanistic frameworks normally informed by a single sensor. We explored a 20-year long experiment to test standard practices and modern machine learning. Results showed that the proposed approach was able to predict the optimal N rate with an RMSE of 16.5 kg N ha–1 (R2 = 0.79). This method was also the only one that was statistically superior (p &lt; 0.05) to the control scenario (the application of the historical average optimal N rate. In summary, by forcing the input data through the mechanistic framework, the decision error increased. This study challenges the idea that farm decisions should follow pre-established agronomic mechanistic frameworks and that digital technologies must necessarily be used to estimate specific crop and soil attributes so as to enable deployment of current decision systems at scale and site-specifically.Speaker: Dr Jonathan Richetti ----CSIRO Early Research Career (CERC) Postdoctoral Fellow Agriculture and FoodSpeaker's bio: Dr Jonathan Richetti background is in agricultural engineering, focusing on remote sensing, modelling, and artificial intelligence applied to agriculture. Jonathan used machine learning methods on remote sensing data for regional yield prediction during his doctoral studies in Brazil. This study rendered him the best oral presentation - graduate competition by the Airborne and Satellite Remote Sensing community of the Agronomy Society of America in 2017. Also, while a visiting research scholar at the University of Florida, he also researched how remote sensing data can be infused into crop modelling. Currently, his research aims to improve fertilisation strategy in precision agriculture. To do that, he is developing methods that combine machine learning, crop modelling, and remote sensing.

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