Applying graph neural networks to predict fungal disease occurrences in precision agriculture

Abstract Purpose Fungal diseases remain among the leading causes of global crop losses, with management still heavily reliant on fungicide applications. While traditional decision support systems and machine learning models offer valuable predictive insights, they often overlook the spatial and relational dynamics underlying pathogen spread. This study evaluates the feasibility and advantages of Graph Neural Networks (GNNs) for predicting fungal disease occurrence in three key crops—onion ( Botrytis squamosa ), lettuce ( Botrytis lactucae ), and carrot ( Cercospora carotae )—to enhance precision agriculture decision-making. Methods Field observations from farms in southern Quebec were used to build plant-level graphs, with nodes representing plants enriched by biological and weather features, and edges defined by spatial proximity. Graph convolutional networks were trained for binary fungal disease occurrence classification and benchmarked against machine learning and deep learning baselines. Graph augmentation techniques and robustness tests under missing and noisy features were applied to assess GNN’s stability. Results Across the three pathosystems, GNNs achieved the strongest overall predictive performance. For onions ( B. squamosa ), Random Forest slightly outperformed the GNN on the complete feature set (accuracy = 76.4% and F1-score = 0.77); here, the GNN provided lower but comparable metric scores (accuracy = 74.8% and F1-score = 0.73). For lettuce ( B. lactucae ), the GNN achieved the highest metric scores with the accuracy of 90.4% and F1-score of 0.90, surpassing all other baselines. For carrot ( C. carotae ), GNNs reached the accuracy of 75.8% and F1-score of 0.77, clearly outperforming Decision Tree, Random Forest, k-NN, and Feed-Forward Neural Networks (FFNs). Graph augmentation further improved the GNN results: random walk sampling increased the model’s accuracy on onion data to 79.3% and F1-score to 0.79, and on lettuce data to 93.9% and to 0.94, respectively, while node/edge perturbation improved the model’s accuracy on carrot data to 78.6% and F1-score to 0.80. Furthermore, the results of the robustness experiments suggest that GNNs can still track overall field-level infection trends with up to 75% of features masked or 50% replaced by noise. Conclusion GNNs offer clear advantages for fungal disease occurrence prediction by incorporating spatial and relational plant patterns, thus improving both the accuracy and robustness of predicted outcomes.