2023
Tan, Chenjiao; Li, Changying; He, Dongjian; Song, Huaibo
Anchor-free deep convolutional neural network for tracking and counting cotton seedlings and flowers Journal Article
In: Computers and Electronics in Agriculture, vol. 215, pp. 108359, 2023, ISSN: 0168-1699.
Abstract | Links | BibTeX | Tags: Anchor free, CNN, Counting, Deep convolutional network, High-throughput phenotyping, object detection, Plant and plant organ, Tracking
@article{Tan2023a,
title = {Anchor-free deep convolutional neural network for tracking and counting cotton seedlings and flowers},
author = {Chenjiao Tan and Changying Li and Dongjian He and Huaibo Song},
url = {https://www.sciencedirect.com/science/article/pii/S0168169923007470},
doi = {https://doi.org/10.1016/j.compag.2023.108359},
issn = {0168-1699},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Computers and Electronics in Agriculture},
volume = {215},
pages = {108359},
abstract = {Accurate counting of plants and their organs in natural environments is essential for breeders and growers. For breeders, counting plants during the seedling stage aids in selecting genotypes with superior emergence rates, while for growers, it informs decisions about potential replanting. Meanwhile, counting specific plant organs, such as flowers, forecasts yields for different genotypes, offering insights into production levels. The overall goal of this study was to investigate a deep convolutional neural network-based tracking method, CenterTrack, for cotton seedling and flower counting from video frames. The network is extended from a customized CenterNet, which is an anchor-free object detector. CenterTrack predicts the detections of the current frame and displacements of detections between the previous frame and the current frame, which are used to associate the same object in consecutive frames. The modified CenterNet detector achieved high accuracy on both seedling and flower datasets with an overall AP50 of 0.962. The video tracking hyperparameters were optimized for each dataset using orthogonal tests. Experimental results showed that seedling and flower counts with optimized hyperparameters highly correlated with those of manual counts (R2 = 0.98 andR2 = 0.95) and the mean relative errors of 75 cotton seedling testing videos and 50 flower testing videos were 5.5 % and 10.8 %, respectively. An average counting speed of 20.4 frames per second was achieved with an input resolution of 1920 × 1080 pixels for both seedling and flower videos. The anchor-free deep convolution neural network-based tracking method provides automatic tracking and counting in video frames, which will significantly benefit plant breeding and crop management.},
keywords = {Anchor free, CNN, Counting, Deep convolutional network, High-throughput phenotyping, object detection, Plant and plant organ, Tracking},
pubstate = {published},
tppubtype = {article}
}
Accurate counting of plants and their organs in natural environments is essential for breeders and growers. For breeders, counting plants during the seedling stage aids in selecting genotypes with superior emergence rates, while for growers, it informs decisions about potential replanting. Meanwhile, counting specific plant organs, such as flowers, forecasts yields for different genotypes, offering insights into production levels. The overall goal of this study was to investigate a deep convolutional neural network-based tracking method, CenterTrack, for cotton seedling and flower counting from video frames. The network is extended from a customized CenterNet, which is an anchor-free object detector. CenterTrack predicts the detections of the current frame and displacements of detections between the previous frame and the current frame, which are used to associate the same object in consecutive frames. The modified CenterNet detector achieved high accuracy on both seedling and flower datasets with an overall AP50 of 0.962. The video tracking hyperparameters were optimized for each dataset using orthogonal tests. Experimental results showed that seedling and flower counts with optimized hyperparameters highly correlated with those of manual counts (R2 = 0.98 andR2 = 0.95) and the mean relative errors of 75 cotton seedling testing videos and 50 flower testing videos were 5.5 % and 10.8 %, respectively. An average counting speed of 20.4 frames per second was achieved with an input resolution of 1920 × 1080 pixels for both seedling and flower videos. The anchor-free deep convolution neural network-based tracking method provides automatic tracking and counting in video frames, which will significantly benefit plant breeding and crop management.
2020
Jiang, Yu; Li, Changying
Convolutional neural networks for image-based high throughput plant phenotyping: A review Journal Article
In: Plant Phenomics, vol. 2020, no. 4152816, 2020.
Links | BibTeX | Tags: CNN, deep learning, machine learning, review
@article{Yu2020,
title = {Convolutional neural networks for image-based high throughput plant phenotyping: A review},
author = {Yu Jiang and Changying Li},
url = {https://spj.sciencemag.org/journals/plantphenomics/2020/4152816/},
doi = {https://doi.org/10.34133/2020/4152816.},
year = {2020},
date = {2020-02-20},
urldate = {2020-02-20},
journal = {Plant Phenomics},
volume = {2020},
number = {4152816},
keywords = {CNN, deep learning, machine learning, review},
pubstate = {published},
tppubtype = {article}
}