A review of computer vision technologies for plant phenotyping

Li, Zhenbo; Guo, Ruohao; Li, Meng; Chen, Yaru; Li, Guangyao

doi:10.1016/j.compag.2020.105672

Cited by 213 publications

(118 citation statements)

References 136 publications

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“…As a result, these small objects cannot be detected accurately. Apart from UAVs, there are many ways to collect seedling images, such as moving-cart-based systems [ 15 ], tractor-based systems [ 17 , 18 ], and automatic field robots [ 19 ], which can collect images and information accurately and efficiently [ 20 ]. Besides RGB-camera-based methods, there are also some other sensors that can be used for object counting in the field, such as Lidar sensors [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Wang

Xiang

Tang

et al. 2021

Sensors

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Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting.

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Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Wang

Xiang

Tang

et al. 2021

Sensors

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“…Since the submission of this paper, we have made significant progress in using more end-to-end approaches (using the data from these models). Newer architectures [ 49 , 50 ], automated hyperparameter exploration [ 51 ], semisupervised/self-supervised approaches [ 52 , 53 ], and domain adaptation approaches [ 54 ] can be leveraged to provide improved results.…”

Section: Discussionmentioning

confidence: 99%

Detection of the Progression of Anthesis in Field-Grown Maize Tassels: A Case Study

et al. 2021

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The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk (stigma) and fertilization of the ovules. Both the amount and timing of pollen shed are physiological traits that impact the production of a hybrid seed. This study describes an automated end-to-end pipeline that combines deep learning and image processing approaches to extract tassel flowering patterns from time-lapse camera images of plants grown under field conditions. Inbred lines from the SAM and NAM diversity panels were grown at the Curtiss farm at Iowa State University, Ames, IA, during the summer of 2016. Using a set of around 500 pole-mounted cameras installed in the field, images of plants were captured every 10 minutes of daylight hours over a three-week period. Extracting data from imaging performed under field conditions is challenging due to variabilities in weather, illumination, and the morphological diversity of tassels. To address these issues, deep learning algorithms were used for tassel detection, classification, and segmentation. Image processing approaches were then used to crop the main spike of the tassel to track reproductive development. The results demonstrated that deep learning with well-labeled data is a powerful tool for detecting, classifying, and segmenting tassels. Our sequential workflow exhibited the following metrics: mAP for tassel detection was 0.91, F1 score obtained for tassel classification was 0.93, and accuracy of semantic segmentation in creating a binary image from the RGB tassel images was 0.95. This workflow was used to determine spatiotemporal variations in the thickness of the main spike—which serves as a proxy for anthesis progression.

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“…Quantitative variables of ASs could also be derived, such as the number of internodes, the number of leaves, or the leaf area, through the application of allometric relations [39], making their application possible on large reconstructed tree structures. This method could also be used in plant phenotyping, which often consists of retrieving and quantifying the abundance of different structural elements [62] such as specialized axes [63,64]. However, quantifying the abundance of different axes types trough manual digitization is time consuming [65] and TLS has been mostly used to derive coarse variables of the tree canopy (e.g., crown volume and leaf area [66]).…”

Section: Real-life Examplementioning

confidence: 99%

Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

Lecigne

Delagrange

Taugourdeau³

2021

Forests

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The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture.

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A review of computer vision technologies for plant phenotyping

Cited by 213 publications

References 136 publications

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Detection of the Progression of Anthesis in Field-Grown Maize Tassels: A Case Study

Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

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