Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation
Deep learning is making strides in plant phenotyping and agriculture. But pretrained models require significant adaptation to work on new target datasets originating from a different experiment even on the same species. The current solution is to retrain the model on the new target data implying the...
Adversarial Large-scale Root Gap Inpainting
Root imaging of a growing plant in a non-invasive, affordable, and effective way remains challenging. One approach is to image roots by growing them in a rhizobox, a soil-filled transparent container, imaging them with digital cameras, and segmenting root from soil background. However, due to soil occlusion...
Understanding deep neural networks for regression in leaf counting
Deep learning methods are constantly increasing in popularity and success across a wide range of computer vision applications. However, they are perceived as `black boxes', due to the lack of an intuitive interpretation of their decision processes. We present a study aimed at understanding how Deep Neural...