Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling

Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only "normal" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating "normal" images for training. Evaluation on the Agriculture-Vision data challenge shows a 6.3% mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-Vision Challenge Dataset [5].

1. Introduction

In precision agriculture, Unmanned Aerial Vehicles (UAVs) have emerged as a pivotal tool for monitoring agricultural landscapes efficiently. UAVs provide much higher resolution images compared to satellite images, thus capturing fine grained details on the agricultural fields. Accurate anomaly detection in UAV images is crucial for the early identification of potential issues such as pest infestations, diseases, and nutrient deficiencies. The dynamic and diverse nature of agricultural fields further compounds the challenge, as anomalies can vary greatly in appearance due to factors such as crop type, growth stage, and environmental conditions as compared to other anomaly detection settings, compared in Figure 1. Thus, there is a need for a completely label free approach to training anomaly detection models so that it can be applied across different crops for different kinds of anomalies. Traditionally, super-vised learning methods have been used for anomaly detection systems [2], [4], [11], [21]. These methods are inherently limited by their dependence on large sets of annotated data, which are labor-intensive to create and may not capture the full spectrum of possible anomalies. Even in case of un-supervised and self supervised methods [6], [12], [15], [29], [30] where explicit anomaly labels are not used, there is a dependence on using only "normal" data for training thus making it necessary for a user to curate normal data that does not contain any types of anomalies.