# An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions

arXiv: https://arxiv.org/abs/2108.00952 · HTML: https://mlsanity.com/paper/2108.00952/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt](https://mlsanity.com/paper/2008.04060/) (2020)
- [Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning](https://mlsanity.com/paper/2012.03129/) (2020)
- [Forecasting Corn Yield with Machine Learning Ensembles](https://mlsanity.com/paper/2001.09055/) (2020)
- [DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation](https://mlsanity.com/paper/2007.10521/) (2020)
- [Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms](https://mlsanity.com/paper/1908.06746/) (2019)
- [Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting](https://mlsanity.com/paper/2003.12025/) (2020)
- [Image-Based Sorghum Head Counting When You Only Look Once](https://mlsanity.com/paper/2009.11929/) (2020)
- [Improved Weighted Random Forest for Classification Problems](https://mlsanity.com/paper/2009.00534/) (2020)
- [High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks](https://mlsanity.com/paper/2010.12552/) (2020)
- [Model identification for ARMA time series through convolutional neural networks](https://mlsanity.com/paper/1804.04299/) (2018)

## Abstract (arXiv)

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.