High-throughput phenotyping refers to the non-destructive and efficient evaluation of plant phenotypes. In recent years, it has been coupled with machine learning in order to improve the process of phenotyping plants by increasing efficiency in handling large datasets and developing methods for the extraction of specific traits. Previous studies have developed methods to advance these challenges through the application of deep neural networks in tandem with automated cameras; however, the datasets being studied often excluded physical labels. In this study, we used a dataset provided by Oak Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white labels displaying treatment (control or drought), block, row, position, and genotype. Optical character recognition (OCR) was used to read these labels on the plants, image segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications, machine learning models were used to predict treatment based on those classifications, and analyzed encoded EXIF tags were used for the purpose of finding leaf size and correlations between phenotypes. We found that our OCR model had an accuracy of 94.31% for non-null text extractions, allowing for the information to be accurately placed in a spreadsheet. Our classification models identified leaf shape, color, and level of brown splotches with an average accuracy of 62.82%, and plant treatment with an accuracy of 60.08%. Finally, we identified a few crucial pieces of information absent from the EXIF tags that prevented the assessment of the leaf size. There was also missing information that prevented the assessment of correlations between phenotypes and conditions. However, future studies could improve upon this to allow for the assessment of these features.
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council（国家研究委员会先进制造智能工业技术与系统研究所）；University of Salento（萨伦托大学）
Plant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective crop management. Currently, plant phenotyping is a manually intensive and time consuming process, which involves human operators making measurements in the field, based on visual estimates or using hand-held devices. In this work, methods for automated grapevine phenotyping are developed, aiming to canopy volume estimation and bunch detection and counting. It is demonstrated that both measurements can be effectively pe…
Institute of Imaging & Computer Vision, RWTH Aachen University（亚琛工业大学成像与计算机视觉研究所）；Julius Kühn Institute: Federal Research Centre for Cultivated Plants（尤利乌斯·库恩研究所（联邦作物研究中心））；LemnaTec GmbH（LemnaTec有限公司）
The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsible for crop loss on a global scale. Here, we introduce a high-throughput system based on computer vision that allows quantifying BCN infestation and characterizing nematode cysts through phenotyping. After recording microscopic images of soil extracts in a standardized setting, an instance segmentation algorithm serves to detect nematode cysts in these samples. Going beyond fast and precise cyst counting, the image-based approach enables quantification of cyst density and phenotyping of morphological features of cysts u…
Indian Institute of Technology（印度理工学院）；The University of Tokyo（东京大学）
In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant pheno…
RGB-D cameras have been successfully used for indoor High-ThroughpuT Phenotyping (HTTP). However, their capability and feasibility for in-field HTTP still need to be evaluated, due to the noise and disturbances generated by unstable illumination, specular reflection, and diffuse reflection, etc. To solve these problems, we evaluated the depth-ranging performances of two consumer-level RGB-D cameras (RealSense D435i and Kinect V2) under in-field HTTP scenarios, and proposed a strategy to compensate the depth measurement error. For performance evaluation, we focused on determining their optimal …
Athena Research and Innovation Center（雅典娜研究与创新中心）；Institute of Mediterranean & Forest Ecosystems（地中海与森林生态系统研究所）
Plant phenotyping refers to a quantitative description of the plants properties, however in image-based phenotyping analysis, our focus is primarily on the plants anatomical, ontogenetical and physiological properties.This technique reinforced by the success of Deep Learning in the field of image based analysis is applicable to a wide range of research areas making high-throughput screens of plants possible, reducing the time and effort needed for phenotypic characterization.In this study, we use Deep Learning methods (supervised and unsupervised learning based approaches) to semantically segm…
University of Minnesota（明尼苏达大学）；University of California-Davis（加州大学戴维斯分校）
Crop production needs to increase in a sustainable manner to meet the growing global demand for food. To identify crop varieties with high yield potential, plant scientists and breeders evaluate the performance of hundreds of lines in multiple locations over several years. To facilitate the process of selecting advanced varieties, an automated framework was developed in this study. A hyperspectral camera was mounted on an unmanned aerial vehicle to collect aerial imagery with high spatial and spectral resolution. Aerial images were captured in two consecutive growing seasons from three experim…
University of Western Ontario（西安大略大学）；Canadian Surgical Technologies & Advanced Robotics（加拿大外科技术与先进机器人中心）；Stryker Mako Surgical Corporation（史赛克马科外科公司）；Bulgarian Academy of Sciences（保加利亚科学院）；University of Guelph（圭尔夫大学）
Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies. We present an automated system for analyzing plant growth in indoor conditions. A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple vi…
Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate, automated, high-throughput phenotyping can relieve a huge burden in the crop breeding pipeline. In this paper, we propose a scalable, high-throughput approach to automatically count and segment panicles (heads), a key phenotype, from aerial sorghum crop imagery. Our counting approach uses the image density map obtained from dot or region annotation as the target with a novel deep convolutional neural network archit…
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs（农业农村部光谱传感重点实验室）；College of Agriculture and Biotechnology, Zhejiang University（浙江大学农业与生物技术学院）
Robust Image-Driven Phenotyping of Ovarian Tumor Cells using Optimized Dynamic Features in Hyperbolic Channels
Cancer Hospital of Dalian University of Technology（大连理工大学附属肿瘤医院）；Dalian University of Technology（大连理工大学）；Faculty of Medicine, Dalian University of Technology（大连理工大学医学部）；Hunan Provincial Maternal and Child Health Care Hospital（湖南省妇幼保健院）；CReSTIC, Université de Reims Champagne-Ardenne（兰斯香槟-阿登大学CReSTIC实验室）；The Queen’s University of Belfast Joint College, China Medical University（中国医科大学-贝尔法斯特女王大学联合学院）
Iowa State University（爱荷华州立大学）；Syngenta Seeds（先正达种子公司）
The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society. The explosion of collectable data has started a revolution in agriculture to the point where innovation must occur. To a commercial organization, the accurate and efficient collection of information is necessary to ensure that optimal decisions are made at key points of the breeding cycle. However, due to the shear size of a breeding program and current resource limitations, the ability to collect precise data on individual plants is not possible. In particular, efficient phen…
ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), CONICET-Universidad de Buenos Aires（生理学、生物分子与神经科学研究所（IFIBYNE），CONICET-布宜诺斯艾利斯大学）；APOLO Biotech（APOLO生物科技）；Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional sinc(i)（信号、系统与计算智能研究所）；CONICET-Universidad Nacional del Litoral（CONICET-沿海大学）；Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2)（巴黎萨克雷大学，CNRS，INRAE，埃夫里大学，巴黎萨克雷植物科学研究所）；Université Paris Cité, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2)（巴黎城市大学，CNRS，INRAE，巴黎萨克雷植物科学研究所）
A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond
Department of Electrical and Computer Engineering, Michigan State University（电气与计算机工程系，密歇根州立大学）；Department of Systems and Information Engineering & Link Lab, University of Virginia（系统与信息工程系及Link实验室，弗吉尼亚大学）；Department of Statistics and Probability, Michigan State University（统计与概率系，密歇根州立大学）；School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University（化学、化工与生物技术学院，南洋理工大学）；Department of Agricultural and Biological Engineering, Mississippi State University（农业与生物工程系，密西西比州立大学）；Department of Mechanical Engineering, Michigan State University（机械工程系，密歇根州立大学）
University of Bonn（波恩大学）；Julius Kühn-Institute – Federal Research Centre for Cultivated Plants（尤利乌斯·库恩研究所 – 联邦栽培植物研究中心）
The berry size is one of the most important fruit traits in grapevine breeding. Non-invasive, image-based phenotyping promises a fast and precise method for the monitoring of the grapevine berry size. In the present study an automated image analyzing framework was developed in order to estimate the size of grapevine berries from images in a high-throughput manner. The framework includes (i) the detection of circular structures which are potentially berries and (ii) the classification of these into the class 'berry' or 'non-berry' by utilizing a conditional random field. The approach used the c…
Vision sensors lie in the heart of computer vision. In many computer vision applications, such as AR/VR, non-contacting near-field communication (NFC) with high throughput is required to transfer information to algorithms. In this work, we proposed a novel NFC system which utilizes multiple frequency bands to achieve high throughput.
Indian Institute of Technology Bombay（孟买印度理工学院）；ICAR-Indian Agricultural Research Institute（ICAR-印度农业研究所）
Development of either drought-resistant or drought-tolerant varieties in rice (Oryza sativa L.), especially for high yield in the context of climate change, is a crucial task across the world. The need for high yielding rice varieties is a prime concern for developing nations like India, China, and other Asian-African countries where rice is a primary staple food. The present investigation is carried out for discriminating drought tolerant, and susceptible genotypes. A total of 150 genotypes were grown under controlled conditions to evaluate at High Throughput Plant Phenomics facility, Nanaji …
We study performance characteristics of convolutional neural networks (CNN) for mobile computer vision systems. CNNs have proven to be a powerful and efficient approach to implement such systems. However, the system performance depends largely on the utilization of hardware accelerators, which are able to speed up the execution of the underlying mathematical operations tremendously through massive parallelism. Our contribution is performance characterization of multiple CNN-based models for object recognition and detection with several different hardware platforms and software frameworks, usin…
From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping
University of Memphis（孟菲斯大学）；University of Missouri（密苏里大学）；University of Arkansas Division of Agriculture（阿肯色大学农业分部）；Mississippi State University（密西西比州立大学）
Huazhong University of Science and Technology（华中科技大学）；The University of Adelaide（阿德莱德大学）
Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most…
Some of the most severe bottlenecks preventing widespread development of machine learning models for human behavior include a dearth of labeled training data and difficulty of acquiring high quality labels. Active learning is a paradigm for using algorithms to computationally select a useful subset of data points to label using metrics for model uncertainty and data similarity. We explore active learning for naturalistic computer vision emotion data, a particularly heterogeneous and complex data space due to inherently subjective labels. Using frames collected from gameplay acquired from a the…
Automated 3D Kinematic Monitoring for Circadian Activity and Anomaly Detection in Juvenile Fish
AI Research Center, National Taiwan Ocean Univ.（台湾海洋大学人工智能研究中心）；Dept. of Aquaculture, National Taiwan Ocean Univ.（台湾海洋大学水产养殖系）；Center of Excellence for the Oceans, National Taiwan Ocean University（台湾海洋大学海洋卓越研究中心）
Generating high-quality 3DMPCs by adaptive data acquisition and NeREF-based radiometric calibration with UGV plant phenotyping system
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs（农业农村部光谱传感重点实验室）
Saint Louis University（圣路易斯大学）；University of Arizona（亚利桑那大学）；Donald Danforth Plant Science Center（唐纳德·丹福斯植物科学中心）
Automated high throughput plant phenotyping involves leveraging sensors, such as RGB, thermal and hyperspectral cameras (among others), to make large scale and rapid measurements of the physical properties of plants for the purpose of better understanding the difference between crops and facilitating rapid plant breeding programs. One of the most basic phenotyping tasks is to determine the cultivar, or species, in a particular sensor product. This simple phenotype can be used to detect errors in planting and to learn the most differentiating features between cultivars. It is also a challenging…
We present an approach to leaf level segmentation of images of Arabidopsis thaliana plants based upon detected edges. We introduce a novel approach to edge classification, which forms an important part of a method to both count the leaves and establish the leaf area of a growing plant from images obtained in a high-throughput phenotyping system. Our technique uses a relatively shallow convolutional neural network to classify image edges as background, plant edge, leaf-on-leaf edge or internal leaf noise. The edges themselves were found using the Canny edge detector and the classified edges can…
StomaD2: An All-in-One System for Intelligent Stomatal Phenotype Analysis via Diffusion-Based Restoration Detection Network
Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms（河南人工智能理论与算法工程研究中心）；School of Mathematics and Statistics（数学与统计学学院）；International Joint Research Laboratory for Global Change Ecology（全球变化生态学联合研究实验室）；School of Life Sciences（生命科学学院）；State Key Laboratory of Cotton Biology（棉花生物学国家重点实验室）
Deakin University（迪肯大学）；Lancaster University（兰卡斯特大学）；The University of Warwick（华威大学）；South China University of Technology（华南理工大学）；Medigrowth Australia Pty Ltd（澳大利亚Medigrowth私人有限公司）
As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the se…
Robotics and Autonomous Systems The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Brisbane, Australia（澳大利亚联邦科学与工业研究组织（CSIRO）数据61部布里斯班机器人与自主系统部（Robotics and Autonomous Systems））
Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data. Currently, the benchmark datasets for leaf segmentation contain only a few hundred labeled training images. In this paper, we propose a framework for leaf instance segmentation by augmenting real plant datasets with generated synthetic images of plants inspired by domain randomisation. We…
China Agricultural University（中国农业大学）；University of California, Berkeley（加州大学伯克利分校）；LuDong University（鲁东大学）
Leaf segmentation is the most direct and effective way for high-throughput plant phenotype data analysis and quantitative researches of complex traits. Currently, the primary goal of plant phenotyping is to raise the accuracy of the autonomous phenotypic measurement. In this work, we present the LeafMask neural network, a new end-to-end model to delineate each leaf region and count the number of leaves, with two main components: 1) the mask assembly module merging position-sensitive bases of each predicted box after non-maximum suppression (NMS) and corresponding coefficients to generate origi…
PointRAFT: 3D deep learning for high-throughput prediction of potato tuber weight from partial point clouds
Graduate School of Agricultural and Life Sciences, The University of Tokyo（东京大学农业与生命科学研究生院）；Department of Integrative Biological Sciences and Industry, Sejong University（世宗大学整合生物科学与工业系）
Iowa State University（爱荷华州立大学）；Syngenta Seeds（先正达种子公司）
The success of modern farming and plant breeding relies on accurate and efficient collection of data. For a commercial organization that manages large amounts of crops, collecting accurate and consistent data is a bottleneck. Due to limited time and labor, accurately phenotyping crops to record color, head count, height, weight, etc. is severely limited. However, this information, combined with other genetic and environmental factors, is vital for developing new superior crop species that help feed the world's growing population. Recent advances in machine learning, in particular deep learning…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The ubiquity of smartphone cameras and IoT cameras, together with the recent boom of deep learning and deep neural networks, proliferate various computer vision driven mobile and IoT applications deployed on the edge. This paper focuses on applications which make soft real time requests to perform inference on their data - they desire prompt responses within designated deadlines, but occasional deadline misses are acceptable. Supporting soft real time applications on a multi-tenant edge server is not easy, since the requests sharing the limited GPU computing resources of an edge server interfe…
Purdue University（普渡大学）；Loyola University Chicago（芝加哥洛约拉大学）
Computer vision on low-power edge devices enables applications including search-and-rescue and security. State-of-the-art computer vision algorithms, such as Deep Neural Networks (DNNs), are too large for inference on low-power edge devices. To improve efficiency, some existing approaches parallelize DNN inference across multiple edge devices. However, these techniques introduce significant communication and synchronization overheads or are unable to balance workloads across devices. This paper demonstrates that the hierarchical DNN architecture is well suited for parallel processing on multip…
University of California, San Diego（加利福尼亚大学圣迭戈分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛与麻省理工学院布罗德研究所）
High-throughput screening (HTS) using model organisms is a promising method to identify a small number of genes or drugs potentially relevant to human biology or disease. In HTS experiments, robots and computers do a significant portion of the experimental work. However, one remaining major bottleneck is the manual analysis of experimental results, which is commonly in the form of microscopy images. This manual inspection is labor intensive, slow and subjective. Here we report our progress towards applying computer vision and machine learning methods to analyze HTS experiments that use Caenorh…
Institute for Infocomm Research (IR), A*STAR（新加坡科技研究局资讯通信研究院（IR））；National University of Singapore (NUS)（新加坡国立大学（NUS））
Regular inspection of rail valves and engines is an important task to ensure the safety and efficiency of railway networks around the globe. Over the past decade, computer vision and pattern recognition based techniques have gained traction for such inspection and defect detection tasks. An automated end-to-end trained system can potentially provide a low-cost, high throughput, and cheap alternative to manual visual inspection of these components. However, such systems require a huge amount of defective images for networks to understand complex defects. In this paper, a multi-phase deep learni…
Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap
Department of Biological and Environmental Engineering（生物与环境工程系）；Horticulture Section, School of Integrative Plant Science（植物科学整合学院园艺系）；School of Electrical and Computer Engineering（电气与计算机工程学院）
In recent years, convolutional neural network has gained popularity in many engineering applications especially for computer vision. In order to achieve better performance, often more complex structures and advanced operations are incorporated into the neural networks, which results very long inference time. For time-critical tasks such as autonomous driving and virtual reality, real-time processing is fundamental. In order to reach real-time process speed, a light-weight, high-throughput CNN architecture namely RoadNet-RT is proposed for road segmentation in this paper. It achieves 90.33% Max…
High-performance semiconductor optoelectronics such as perovskites have high-dimensional and vast composition spaces that govern the performance properties of the material. To cost-effectively search these composition spaces, we utilize a high-throughput experimentation method of rapidly printing discrete droplets via inkjet deposition, in which each droplet is comprised of a unique permutation of semiconductor materials. However, inkjet printer systems are not optimized to run high-throughput experimentation on semiconductor materials. Thus, in this work, we develop a computer vision-driven B…
Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to novel sensing techniques and high-throughput technologies. In reference to biomedical image analysis, the advances in image acquisition modalities and high-throughput imaging experiments are creating new challenges. This huge information ensemble could overwhelm the analytic capabilities needed by physicians in their daily decision-making tasks as well as by biologists investigating complex biochemical systems. In particular, quantitative imaging methods convey scientifically and clinically relevant inf…
Computer Vision (CV) classifiers which distinguish and detect nonverbal social human behavior and mental state can aid digital diagnostics and therapeutics for psychiatry and the behavioral sciences. While CV classifiers for traditional and structured classification tasks can be developed with standard machine learning pipelines for supervised learning consisting of data labeling, preprocessing, and training a convolutional neural network, there are several pain points which arise when attempting this process for behavioral phenotyping. Here, we discuss the challenges and corresponding opportu…
