Data Fusion and Artificial Neural Networks for Modelling Crop Disease Severity

This paper analyzes the possibility of applying data fusion combined with artificial neural networks (ANN) on a dataset combining hard and soft data for prediction of one of the most devastating crop diseases of winter wheat, i.e., Septoria Tritici (Zymoseptoria tritici). In advanced decision support systems for crop protection choices, disease models form a major component.They reproduce the biophysical processes of disease development and temporal spread as a set of rules or processes to predict disease risk value. However, the adaptation of these rules or processes to incorporate the effects of climate change is complex and requires extensive rework. To remedy this issue, statistical machine learning techniques have been introduced to model disease severity percentage for some diseases.However, the use of artificial neural networks has been limited (mainly to image data) and is unexplored for Septoria Tritici.This paper explores the use of Feed Forward neural networks on fused tabular data for the task of disease severity modelling. First, ten years of trial data ranging from 2008 to 2018 across Europe is used for the creation of the new tabular dataset with a fusion of all important data sources baring impact on disease development: Field-specific data, weather data, crop growth stages, and disease severity observation made by human trial operators (response variable). %Correlation and regression analyses and domain expert knowledge were used for the selection of useful predictor variables from these data sources. Next, two implementation architectures of Feed Forward neural networks on tabular data are employed: a) standard architecture with backpropagation, drop out regularization, and batch normalization and b) advanced architecture with improvements such as cyclic learning rate and cosine annealing.%A comparison of generic two layer feed forward network vs the same with the incorporation of the latest advances to improve the performance of the architecture is presented. The advanced architecture is able to better model the data and make estimations of disease severity with a difference of +-10\% giving a better quantifiable estimate of disease stress. For better outreach to farmers, a technique to incorporate such modelling techniques into the well established Decision Support Systems is also presented.ISBN för värdpublikation: 978-0-578-64709-8, 978-1-7281-6830-2