arXiv 1710.002412018-01-26cs.CV

DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

Univ. Saskatchewan（萨斯喀彻温大学）

Shubhra Aich, Anique Josuttes, Ilya Ovsyannikov, Keegan Strueby, Imran Ahmed, Hema Sudhakar Duddu, Curtis Pozniak, Steve Shirtliffe, Ian Stavness

备注：: WACV 2018 (Code repository: WACV-2018" target="_blank" rel="noopener">https://github.com/p2irc/deepwheat_WACV-2018)

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels

University of Cambridge（剑桥大学）；AstraZeneca（阿斯利康）

We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells. Our model is based on a knowledge distillation approach with a vision transformer backbone (DINO), and we use this as a benchmark model for our study. Using WS-DINO, we fine-tuned with weak label information available in high-content microscopy screens (treatment and compound) and achieve state-of-the-art performance in not-same-compound mechanism of action prediction on the BBBC021 dataset (98%), and not-same-compound-and-batch performance…

2022-11-17cs.CV

Estimating Phenotypic Traits From UAV Based RGB Imagery

Purdue University（普渡大学）

In many agricultural applications one wants to characterize physical properties of plants and use the measurements to predict, for example biomass and environmental influence. This process is known as phenotyping. Traditional collection of phenotypic information is labor-intensive and time-consuming. Use of imagery is becoming popular for phenotyping. In this paper, we present methods to estimate traits of sorghum plants from RBG cameras on board of an unmanned aerial vehicle (UAV). The position and orientation of the imagery together with the coordinates of sparse points along the area of int…

2018-07-02cs.CV

3D植物根系骨架检测与提取

3D Plant Root Skeleton Detection and Extraction

Intelligent Vision and Sensing (IVS) Lab at SUNY Binghamton（SUNY布林茅伊大学智能视觉与感知实验室）；Yancheng Institute of Technology（盐城职业技术学院）；University of Georgia（佐治亚大学）

提出一种从少量图像中提取植物根系3D骨架的方法，包含侧根检测匹配、三角测量及主侧根整合，实验验证其有效性，可应用于自动化育种机器人以提升育种效率。

2025-08-11cs.CV

Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud

College of Information and Electrical Engineering, Shenyang Agricultural University（沈阳农业大学信息与电气工程学院）；School of Information and Intelligence Engineering, University of Sanya（三亚学院信息与智能工程学院）

Nowadays, there are many approaches to acquire three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from three-dimensional (3D) point clouds remains challenging, especially for new emerging leaves that are very close and wrapped together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three main steps: skeleton extraction, coarse segmentation based on the skeleton, fine segmentation based on stem-leaf classification. The segmentation method was tested on 30 maize seedlings and …

2020-09-07cs.CV

FrontVeg V2：一种用于高分辨率棚架作物图像中前景感知零样本植物性状分割的无训练软件框架

FrontVeg V2: A Training-Free Software Framework for Foreground-Aware Zero-Shot Plant Trait Segmentation in High-Resolution Images of Trellised Crops

IRHS, INRAE, Univ. Angers（法国国家农业、食品与环境研究院（INRAE）IRHS研究所，昂热大学）；LARIS, Univ. Angers（昂热大学LARIS实验室）；GAFL, INRAE（法国国家农业、食品与环境研究院（INRAE）GAFL研究所）；SVQV, UMR1131, INRAE, Univ. Strasbourg（法国国家农业、食品与环境研究院（INRAE）UMR1131 SVQV研究所，斯特拉斯堡大学）；UEAV, INRAE（法国国家农业、食品与环境研究院（INRAE）UEAV实验站）；UEA, UMR0393, INRAE（法国国家农业、食品与环境研究院（INRAE）UMR0393 UEA研究所）

FrontVeg V2提出一种无训练软件框架，结合深度估计与零样本分割，实现棚架作物高分辨率图像中前景感知的植物性状分割，减少邻行干扰，支持多作物多性状表型分析。

2026-10-06cs.CV

面向三维作物结构恢复与表型性状提取的去噪感知时序点云补全方法

Denoising-Aware Temporal Point Cloud Completion for 3D Crop Architecture Recovery and Phenotypic Trait Extraction

Sardar Vallabhbhai National Institute of Technology（萨达尔·瓦拉巴伊国家理工学院）；Indian Institute of Technology Bombay（印度理工学院孟买分校）

该研究针对时序植物重建数据集缺失的问题，提出SynthCrop4D数据集与结合去噪和Adaptive Temporal PoinTr的两阶段流程，提升了作物点云补全质量，可用于表型性状提取。

2026-08-31cs.CV

更多相关论文收起更多论文

跨模态学习用于作物冠层特性估计

Crossmodal learning for Crop Canopy Trait Estimation

Department of Computer Engineering, Iowa State University, Ames, IA, USA（计算机工程系，爱荷华州立大学）；Department of Mechanical Engineering, Iowa State University, Ames, IA, USA（机械工程系，爱荷华州立大学）

本文提出跨模态学习方法，通过融合高分辨率卫星影像与无人机影像细节，提升作物冠层特性估计的准确性。

2025-11-20cs.CV

基于叶片表型与光合性状的数据驱动方法预测特定物种植物对光谱转换膜的响应

Data-driven Prediction of Species-Specific Plant Responses to Spectral-Shifting Films from Leaf Phenotypic and Photosynthetic Traits

Smart Farm Research Center, Korea Institute of Science and Technology（韩国科学技术院智能农场研究中心）；Research Institute of Agriculture and Life Sciences, Seoul National University（首尔国立大学农业与生命科学研究院）；Department of Agriculture, Forestry and Bioresources, Seoul National University（首尔国立大学农业、林业与生物资源系）

本研究针对光谱转换膜作物增产效应的物种差异问题，采集多类作物的表型、光合及环境数据，结合变分自编码器增强数据训练FFNN等模型，实现了SF对作物产量影响的高精度预测。

2025-11-19cs.CVcs.LGeess.IV

3D植物表型分析的转折点：3D基础模型实现分钟到秒级的跨作物重建及更多

The Turning Point of 3D Plant Phenotyping: 3D Foundation Models Enable Minute-to-Second Cross-Crop Reconstruction and Beyond

Northwest A&F University（西北农林科技大学）；Huazhong University of Science and Technology（华中科技大学）；Wuhan Institute of Technology（武汉工程大学）

提出基于3D基础模型的跨作物表型分析框架，用前馈几何恢复替代COLMAP，结合3D高斯泼溅实现少视图重建，将重建时间从6.52分钟降至1.58秒，保持高质量与高精度。

2026-07-03cs.CVq-bio.QM

DT/MARS-CycleGAN：面向 MARS 表型机器人的改进目标检测

DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot

Athens Academy（雅典学院）；University of Florida（佛罗里达大学）；University of Georgia（佐治亚大学）

针对作物表型机器人在复杂背景下的目标检测难题，提出DT/MARS-CycleGAN框架，通过设计新的DT-MARS损失约束图像合成并微调YOLOv8，显著提升了MARS系统的作物检测性能。

2023-10-20cs.CV

基于多条件生成对抗网络的时变生成图像数据驱动作物生长模拟

Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks

University of Bonn（波恩大学）；Forschungszentrum Jülich GmbH（于利希研究中心）

本研究提出两阶段多条件生成对抗网络框架，可结合多种生长条件生成时变作物图像并用于表型分析，能衔接图像与过程类作物生长模型，助力精准农业。

2023-12-06cs.CVstat.ML

智能体农业机器人表型分析的最新进展：从碎片化多模态感知到统一PhenoAgent智能的视角综述

Recent Advances in Agentic Agri-Robotic Phenotyping: A Perspective Review from Fragmented Multimodal Sensing to Unified PhenoAgent Intelligence

Khalifa University of Science and Technology（哈利法科学技术大学）；Chung-Ang University（中央大学）

本综述提出将植物表型分析视为SSPEM智能问题，并引入PhenoAgent框架，整合多模态感知与智能体AI，以实现可解释、可扩展的作物智能决策支持。

2026-09-29cs.CVcs.ET

Wheat3DGS：基于高斯泼溅的田间小麦穗三维重建、实例分割与表型分析

Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting

ETH Zürich（苏黎世联邦理工学院）；Swiss Data Science Center（瑞士数据科学中心）

本文提出Wheat3DGS，首次将3D高斯泼溅与SAM结合用于高通量田间表型分析，实现数百个小麦穗的自动三维实例分割与形态测量，在长度、宽度和体积上分别达到15.1%、18.3%和40.2%的平均绝对百分比误差，优于NeRF和传统MVS方法。

2025-04-09cs.CV

High-Resolution UAV Image Generation for Sorghum Panicle Detection

Purdue University（普渡大学）

The number of panicles (or heads) of Sorghum plants is an important phenotypic trait for plant development and grain yield estimation. The use of Unmanned Aerial Vehicles (UAVs) enables the capability of collecting and analyzing Sorghum images on a large scale. Deep learning can provide methods for estimating phenotypic traits from UAV images but requires a large amount of labeled data. The lack of training data due to the labor-intensive ground truthing of UAV images causes a major bottleneck in developing methods for Sorghum panicle detection and counting. In this paper, we present an approa…

2022-05-08cs.CVeess.IV

NeFF-BioNet：从点云到无人机影像的作物生物量预测

NeFF-BioNet: Crop Biomass Prediction from Point Cloud to Drone Imagery

CSIRO Agriculture and Food（澳大利亚联邦科学与工业研究组织农业与食品部）；Australian National University（澳大利亚国立大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61部）

针对作物生物量传统测量方法的局限，提出可跨点云、无人机影像模态的BioNet预测网络，结合NeFF模块实现2D到3D特征转换，在两类模态数据集上均取得优于现有方法的性能，提供低成本可扩展的大田生物量测算方案。

2024-10-30cs.CV

Eff-3DPSeg: 3D organ-level plant shoot segmentation using annotation-efficient point clouds

McGill University（麦吉尔大学）；Jiangnan University（江南大学）

Reliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Mes…

2022-12-20cs.AIcs.CV

GrowliFlower：用于花椰菜生长分析的图像时间序列数据集

GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER

University of Bonn（波恩大学）；Forschungszentrum Jülich GmbH（于利希研究中心）；JB Hyperspectral Devices GmbH（JB高光谱设备有限公司）

本文介绍了GrowliFlower数据集，包含花椰菜田的无人机图像时间序列及表型特征，支持分类、分割等计算机视觉任务，促进农业自动化发展。

2022-04-01cs.AIcs.CVcs.LG