Can we detect plant diseases without prior knowledge of their existence?

There is a need to help farmers make decisions to maximize crop yields. Many studies have emerged in recent years using deep learning on remotely sensed images to detect plant diseases, which can be caused by multiple factors such as environmental conditions, genetics or pathogens. This problem can be considered as an anomaly detection task. However, these approaches are often limited by the availability of annotated data or prior knowledge of the existence of an anomaly. In many cases, it is not possible to obtain this information. In this work, we propose an approach that can detect plant anomalies without prior knowledge of their existence, thus overcoming these limitations. To this end, we train a model on an auxiliary prediction task using a dataset composed of samples of normal and abnormal plants. Our proposed method studies the distribution of heatmaps retrieved from an explainability model. Based on the assumptions that the model trained on the auxiliary task is able to extract important plant characteristics, we propose to study how closely the heatmap of a new observation follows the heatmap distribution of a normal dataset. Through the proposed a contrario approach, we derive a score indicating potential anomalies.

Experiments show that our approach outperforms reference approaches such as f-AnoGAN and OCSVM on the GrowliFlower and PlantDoc datasets and has competitive performances on the PlantVillage dataset, while not requiring the prior knowledge on the existence of anomalies.