  For a global breeding organization, identifying the next generation of
superior crops is vital for its success. Recognizing new genetic varieties
requires years of in-field testing to gather data about the crop's yield, pest
resistance, heat resistance, etc. At the conclusion of the growing season,
organizations need to determine which varieties will be advanced to the next
growing season (or sold to farmers) and which ones will be discarded from the
candidate pool. Specifically for soybeans, identifying their relative maturity
is a vital piece of information used for advancement decisions. However, this
trait needs to be physically observed, and there are resource limitations
(time, money, etc.) that bottleneck the data collection process. To combat
this, breeding organizations are moving toward advanced image capturing
devices. In this paper, we develop a robust and automatic approach for
estimating the relative maturity of soybeans using a time series of UAV images.
An end-to-end hybrid model combining Convolutional Neural Networks (CNN) and
Long Short-Term Memory (LSTM) is proposed to extract features and capture the
sequential behavior of time series data. The proposed deep learning model was
tested on six different environments across the United States. Results suggest
the effectiveness of our proposed CNN-LSTM model compared to the local
regression method. Furthermore, we demonstrate how this newfound information
can be used to aid in plant breeding advancement decisions.
