Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs,understanding plantenvironment interactions,and managing agricultural systems.In the past five years,imaging appr...Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs,understanding plantenvironment interactions,and managing agricultural systems.In the past five years,imaging approaches have shown great potential for high-throughput plant phenotyping,resulting in more attention paid to imaging-based plant phenotyping.With this increased amount of image data,it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly.The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks(CNNs)in plant phenotyping applications.We specifically review the use of various CNN architecture for plant stress evaluation,plant development,and postharvest quality assessment.We systematically organize the studies based on technical developments resulting from imaging classification,object detection,and image segmentation,thereby identifying state-of-the-art solutions for certain phenotyping applications.Finally,we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.展开更多
Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient.The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collec...Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient.The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans.The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems.This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors,manually pushed or motorized carts,gantries,and cable-driven systems.Then,a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components,including mobile platforms,sensors,manipulators,computing units,and software.It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets.At the end of the review,this paper discusses current major challenges and future research directions.展开更多
基金The project was supported by the National Robotics Initiative(NIFA grant no.2017-67021-25928).
文摘Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs,understanding plantenvironment interactions,and managing agricultural systems.In the past five years,imaging approaches have shown great potential for high-throughput plant phenotyping,resulting in more attention paid to imaging-based plant phenotyping.With this increased amount of image data,it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly.The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks(CNNs)in plant phenotyping applications.We specifically review the use of various CNN architecture for plant stress evaluation,plant development,and postharvest quality assessment.We systematically organize the studies based on technical developments resulting from imaging classification,object detection,and image segmentation,thereby identifying state-of-the-art solutions for certain phenotyping applications.Finally,we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.
基金This work was partially supported by the USDA-NIFA under Grant No.2017-67021-25928National Science Foundation under Grant No.1934481.
文摘Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient.The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans.The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems.This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors,manually pushed or motorized carts,gantries,and cable-driven systems.Then,a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components,including mobile platforms,sensors,manipulators,computing units,and software.It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets.At the end of the review,this paper discusses current major challenges and future research directions.
