Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version

UA Open Drone Processing Pipeline (alpha)

Pipeline for processing drone data for research in agriculture and field plant phenomics

Converging Genomics, Phenomics, and Environments Using Interpretable Machine Learning Models

To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. The state-of-the-art, which has led to many important discoveries, utilizes numerical or statistical models for making predictions or performing in silico experimentation, but these techniques struggle to capture the nonlinear response of natural systems. Machine learning (ML) methods are better able to cope with nonlinearity and have been used successfully in biological applications (e.g., [1–3]), but several barriers still exist, including the opaque nature of the algorithm output and the absence of ML-ready data. Here, we propose to significantly advance technologies in ML and create a new interdisciplinary field, computational ecogenomics. We propose to do this by (a) designing ML techniques for encoding heterogeneous genomic and environmental data, and mapping them to multi-level phenotypic traits, (b) reducing the amount of necessary training data, and (c) developing interactive visualizations to better interpret ML models and their outputs. These advances will responsibly and transparently inform policy to maximize resources during this crucial window for planetary health, while revealing underlying biological mechanisms of response to stress and evolutionary pressure