Deep Learning for Reference-Free Geolocation for Poplar Trees

• URL: http://arxiv.org/abs/2301.13387v1
• Date: Tue, 31 Jan 2023 03:37:47 GMT
• Title: Deep Learning for Reference-Free Geolocation for Poplar Trees
• Authors: Cai W. John, Owen Queen, Wellington Muchero, and Scott J. Emrich
• Abstract summary: Geolocation is concerned with locating the native region of a given sample based on its genetic makeup. Here, we investigate genomic geolocation of Populus trichocarpa, or poplar, which has been identified by the US Department of Energy as a fast-rotation biofuel crop. Our model, MashNet, predicts latitude and longitude for poplar trees from randomly-sampled, unaligned sequence fragments.
• Score: 0.17999333451993943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A core task in precision agriculture is the identification of climatic and ecological conditions that are advantageous for a given crop. The most succinct approach is geolocation, which is concerned with locating the native region of a given sample based on its genetic makeup. Here, we investigate genomic geolocation of Populus trichocarpa, or poplar, which has been identified by the US Department of Energy as a fast-rotation biofuel crop to be harvested nationwide. In particular, we approach geolocation from a reference-free perspective, circumventing the need for compute-intensive processes such as variant calling and alignment. Our model, MashNet, predicts latitude and longitude for poplar trees from randomly-sampled, unaligned sequence fragments. We show that our model performs comparably to Locator, a state-of-the-art method based on aligned whole-genome sequence data. MashNet achieves an error of 34.0 km^2 compared to Locator's 22.1 km^2. MashNet allows growers to quickly and efficiently identify natural varieties that will be most productive in their growth environment based on genotype. This paper explores geolocation for precision agriculture while providing a framework and data source for further development by the machine learning community.

Related papers

• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components. GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric. We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• Detection and Geographic Localization of Natural Objects in the Wild: A Case Study on Palms [13.350975037304194]
  We develop PRISM (Processing, Inference, and Mapping), a flexible pipeline for detecting and localizing palms in dense tropical forests using large orthomosaic images. Our contributions are threefold. First, we construct a large UAV-derived orthomosaic dataset collected across 21 ecologically diverse sites in western Ecuador, annotated with 8,830 bounding boxes and 5,026 palm center points. Second, we evaluate multiple state-of-the-art object detectors based on efficiency and performance, integrating zero-shot SAM 2 as the segmentation backbone. Third, we apply calibration methods to align confidence scores with IoU and explore s
  arXiv  Detail & Related papers  (2025-02-18T16:43:11Z)
• Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction [3.017562867737194]
  We propose Spatioformer, where a geolocation encoder is coupled with the transformer model to encode geolocation context into remote sensing imagery. Results demonstrate that geolocational information is advantageous in predicting species richness from satellite observations over large spatial scales. richness maps produced in this study reveal thetemporal dynamics of plant species richness in Australia, providing supporting evidence to inform effective planning and policy development for plant diversity conservation.
  arXiv  Detail & Related papers  (2024-10-25T02:21:01Z)
• Enhancing Worldwide Image Geolocation by Ensembling Satellite-Based Ground-Level Attribute Predictors [4.415977307120618]
  We examine the challenge of estimating the location of a single ground-level image in the absence of GPS or other location metadata. We introduce a novel metric, Recall vs Area, which measures the accuracy of estimated distributions of locations. We then examine an ensembling approach to global-scale image geolocation, which incorporates information from multiple sources.
  arXiv  Detail & Related papers  (2024-07-18T19:15:52Z)
• Predicting the Geothermal Gradient in Colombia: a Machine Learning Approach [0.0]
  geothermal gradient determination is critical for assessing the geothermal energy potential of a given region. We present an approach that leverages recent advances in supervised machine learning to predict the geothermal gradient. We show that predictions of our model are within 12% accuracy and that independent measurements performed by other authors agree well with our model.
  arXiv  Detail & Related papers  (2024-04-08T04:18:54Z)
• GeoLLM: Extracting Geospatial Knowledge from Large Language Models [49.20315582673223]
  We present GeoLLM, a novel method that can effectively extract geospatial knowledge from large language models. We demonstrate the utility of our approach across multiple tasks of central interest to the international community, including the measurement of population density and economic livelihoods. Our experiments reveal that LLMs are remarkably sample-efficient, rich in geospatial information, and robust across the globe.
  arXiv  Detail & Related papers  (2023-10-10T00:03:23Z)
• Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking [61.60169764507917]
  Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. We propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines.
  arXiv  Detail & Related papers  (2023-09-04T13:44:50Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
  We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks. We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
  arXiv  Detail & Related papers  (2023-05-11T03:21:56Z)
• GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
  We study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation. Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
  arXiv  Detail & Related papers  (2023-03-27T17:59:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.