  Early, precise detection of nutrient deficiency stress (NDS) has key economic
as well as environmental impact; precision application of chemicals in place of
blanket application reduces operational costs for the growers while reducing
the amount of chemicals which may enter the environment unnecessarily.
Furthermore, earlier treatment reduces the amount of loss and therefore boosts
crop production during a given season. With this in mind, we collect sequences
of high-resolution aerial imagery and construct semantic segmentation models to
detect and predict NDS across the field. Our work sits at the intersection of
agriculture, remote sensing, and modern computer vision and deep learning.
First, we establish a baseline for full-field detection of NDS and quantify the
impact of pretraining, backbone architecture, input representation, and
sampling strategy. We then quantify the amount of information available at
different points in the season by building a single-timestamp model based on a
UNet. Next, we construct our proposed spatiotemporal architecture, which
combines a UNet with a convolutional LSTM layer, to accurately detect regions
of the field showing NDS; this approach has an impressive IOU score of 0.53.
Finally, we show that this architecture can be trained to predict regions of
the field which are expected to show NDS in a later flight -- potentially more
than three weeks in the future -- maintaining an IOU score of 0.47-0.51
depending on how far in advance the prediction is made. We will also release a
dataset which we believe will benefit the computer vision, remote sensing, as
well as agriculture fields. This work contributes to the recent developments in
deep learning for remote sensing and agriculture, while addressing a key social
challenge with implications for economics and sustainability.
