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 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

使用自监督学习和掩蔽自编码器的方法，通过检测农业无人机图像中的异常像素来实现农田异常检测，从而提高在农业领域的生产效率。