Precision agriculture: AI- and Expert- based approach to forcast fruit production in high intra-field variation settings

A significant challenge in forecasting soft fruit yield is high intra-field crop variation, i.e. specific parts of the field performing worse than others. Most forecast models operate by predicting the mean growth rate as a crop as a whole, however, extrapolating yield over any crops showing variation is likely to cause forecast errors. This is because crop growth rate to environmental variables is highly complex, has multiple interactions (e.g. temperature x radiation x water) and non-linear (photosynthesis response to light). However, with the outburst of big-data analytics and AI this information can be used to develop algorithms and models that can be useful and inexpensive tools to the farmers. Given the highly competitive nature of the sector, growers need tools not only for monitoring the field performance but also for providing them with as accurate yield predictions as possible. Currently, growers delve into experience-based/empirical predictions, which sometimes do not represent the reality due to factors/patterns they fail to consider. That means they might provide wrong estimates to the retailers, leading to either wasting products or missing to meet the demand. The PhD student will work closely with the fruit industry to use large amounts of numerical data to develop bespoke AI and deep learning techniques on the cloud (minimising the cost of expensive equipment) that will leverage important a priori knowledge, such as seasonal variations, and will inform future predictions based on posterior information, such as weather forecast or other relevant information. The models will incorporate expert-based input - not just data-based one - to add the expert knowledge and/or constraints to the system. It will also include a hierarchical and/or rule-based component to consider the intra-field variations, hence providing predictions for each section of the field separately. Initial maps of the field will be created with the help of the farmers. Additional input from ongoing CTP PhD studies might include video capture using robots that can be used as an extra modality to the developed model, if needed. The PhD student will develop various skills spanning machine learning, agriculture and industrial demands, benefitting by the highly interdisciplinary team.

1: Dr Georgios Leontidis and Professor Stefanos Kollias are with the School of Computer Science at the University of Lincoln. Both are experts on machine learning and AI, participating in various funded projects related to agriculture, energy and industry. Both have a proven record of PhD supervision. Professor Kollias has supervised to completion more than 43 PhD students.
2: Mr David May are with the Lincoln Institute for Agri-food technology at the University of Lincoln. He has vast experience of the agri-food sector with deep knowledge of the demands and needs of industry and academia related to this.