  Recent research on the application of remote sensing and deep learning-based
analysis in precision agriculture demonstrated a potential for improved crop
management and reduced environmental impacts of agricultural production.
Despite the promising results, the practical relevance of these technologies
for field deployment requires novel algorithms that are customized for analysis
of agricultural images and robust to implementation on natural field imagery.
The paper presents an approach for analyzing aerial images of a potato (Solanum
tuberosum L.) crop using deep neural networks. The main objective is to
demonstrate automated spatial recognition of healthy vs. stressed crop at a
plant level. Specifically, we examine premature plant senescence resulting in
drought stress on Russet Burbank potato plants. We propose a novel deep
learning (DL) model for detecting crop stress, named Retina-UNet-Ag. The
proposed architecture is a variant of Retina-UNet and includes connections from
low-level semantic representation maps to the feature pyramid network. The
paper also introduces a dataset of aerial field images acquired with a Parrot
Sequoia camera. The dataset includes manually annotated bounding boxes of
healthy and stressed plant regions. Experimental validation demonstrated the
ability for distinguishing healthy and stressed plants in field images,
achieving an average dice score coefficient (DSC) of 0.74. A comparison to
related state-of-the-art DL models for object detection revealed that the
presented approach is effective for this task. The proposed method is conducive
toward the assessment and recognition of potato crop stress in aerial field
images collected under natural conditions.
