Artificial intelligence analysis of contributive factors in determining blackleg disease severity in canola farmlands

Abstract

Canola (Brassica napus L.) production is threatened by blackleg disease caused by Leptosphaeria maculans. Disease outcome is determined by interactions among pathogens, plants, farming practices, and environmental factors. Although the gene-for-gene interactions between the pathogen and its plant host are relatively clear, how precisely the pathogen interacts with the environment and farming practices is still poorly understood, making disease forecasting challenging for commercial farmlands. In recent years, artificial intelligence (AI) has been successful in forecasting disease risks based on environmental factors. In this study, we evaluated two AI methods and a data augmentation method to forecast disease risk using a dataset collected from 116 farmlands in Alberta in 2021 and 2022. We first assessed a machine learning model (support vector machine or SVM) and a deep-learning model (convolutional neural network or CNN) to predict blackleg severity based on five weather variables, flea beetle damage, root maggot damage, and crop-rotation variables. Both SVM and CNN predicted the disease risk with an accuracy of over 66%. The data augmentation method did not improve model performance. Flea beetle feeding and maggot damage contribute little to the model’s performance, and omitting these data did not appear to affect the results. In contrast, crop rotation contributes substantially to model performance. The five weather variables contribute roughly equally to the model’s performance, and removing any of the individual weather variables did not impact prediction ability for both models.