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

• URL: http://arxiv.org/abs/2108.00952v1
• Date: Mon, 2 Aug 2021 14:53:58 GMT
• Title: An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions
• Authors: Saba Moeinizade, Hieu Pham, Ye Han, Austin Dobbels, Guiping Hu
• Abstract summary: 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. Results suggest the effectiveness of our proposed CNN-LSTM model compared to the local regression method.
• Score: 7.4022258821325115
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Optimizing Resource Consumption in Diffusion Models through Hallucination Early Detection [87.22082662250999]
  We introduce HEaD (Hallucination Early Detection), a new paradigm designed to swiftly detect incorrect generations at the beginning of the diffusion process. We demonstrate that using HEaD saves computational resources and accelerates the generation process to get a complete image. Our findings reveal that HEaD can save up to 12% of the generation time on a two objects scenario.
  arXiv  Detail & Related papers  (2024-09-16T18:00:00Z)
• BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions [30.27773980916216]
  Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability. Advancements in field management through non-chemical weeding by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) are helpful to address these challenges. The analysis of plant traits, called phenotyping, is an essential activity in plant breeding, it however involves a great amount of manual labor.
  arXiv  Detail & Related papers  (2023-12-22T14:06:44Z)
• Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks [2.513679466277441]
  We present a two-stage framework consisting first of an image prediction model and second of a growth estimation model. The image prediction model is a conditional Wasserstein generative adversarial network (CWGAN) In the generator of this model, conditional batch normalization (CBN) is used to integrate different conditions along with the input image. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits.
  arXiv  Detail & Related papers  (2023-12-06T11:54:50Z)
• Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
  We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery. We solve real-world deep learning problems in the very specific context of agave crop segmentation. With the resulting accurate models, agave production forecasting can be made available for large regions.
  arXiv  Detail & Related papers  (2023-03-21T03:15:29Z)
• Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images [3.6868861317674524]
  Monitoring seed maturity is an increasing challenge in agriculture due to climate change and more restrictive practices. Traditional methods are based on limited sampling in the field and analysis in laboratory. We propose a method for estimating parsley seed maturity using multispectral UAV imagery, with a new approach for automatic data labeling.
  arXiv  Detail & Related papers  (2022-08-09T09:06:51Z)
• End-to-end deep learning for directly estimating grape yield from ground-based imagery [53.086864957064876]
  This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Three model architectures were tested: object detection, CNN regression, and transformer models. The study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale.
  arXiv  Detail & Related papers  (2022-08-04T01:34:46Z)
• Continual Learning for Blind Image Quality Assessment [80.55119990128419]
  Blind image quality assessment (BIQA) models fail to continually adapt to subpopulation shift. Recent work suggests training BIQA methods on the combination of all available human-rated IQA datasets. We formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets.
  arXiv  Detail & Related papers  (2021-02-19T03:07:01Z)
• Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications [7.450586438835518]
  The objective of this study is to develop a machine learning (ML) approach adept at soybean pod counting. We developed a multi-view image-based yield estimation framework utilizing deep learning architectures. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort.
  arXiv  Detail & Related papers  (2020-11-13T20:37:04Z)
• High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks [23.67862313758282]
  We propose a deep learning based approach, named DeepStand, for image-based corn stand counting at early phenological stages. Our proposed method can successfully count corn stands and out-perform other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-23T17:28:29Z)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.