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Taniguchi S, Sakamoto T, Imase R, Nonoue Y, Tsunematsu H, Goto A, Matsushita K, Ohmori S, Maeda H, Takeuchi Y, Ishii T, Yonemaru JI, Ogawa D. Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice. FRONTIERS IN PLANT SCIENCE 2022; 13:998803. [PMID: 36582650 PMCID: PMC9792801 DOI: 10.3389/fpls.2022.998803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.
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Affiliation(s)
- Shoji Taniguchi
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Toshihiro Sakamoto
- Institute for Agro-Environmental Sciences, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Ryoji Imase
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Yasunori Nonoue
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Hiroshi Tsunematsu
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Akitoshi Goto
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Kei Matsushita
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Sinnosuke Ohmori
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Hideo Maeda
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Yoshinobu Takeuchi
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Takuro Ishii
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Jun-ichi Yonemaru
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Daisuke Ogawa
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
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Du X, Si L, Jin X, Li P, Yun Z, Gao K. Classification of plug seedling quality by improved convolutional neural network with an attention mechanism. FRONTIERS IN PLANT SCIENCE 2022; 13:967706. [PMID: 35991389 PMCID: PMC9386228 DOI: 10.3389/fpls.2022.967706] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/11/2022] [Indexed: 06/15/2023]
Abstract
The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model's ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88-20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.
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Affiliation(s)
- Xinwu Du
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
- Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang, China
| | - Laiqiang Si
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
| | - Xin Jin
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
- Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang, China
| | - Pengfei Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
| | - Zhihao Yun
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
| | - Kaihang Gao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
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Tan S, Liu J, Lu H, Lan M, Yu J, Liao G, Wang Y, Li Z, Qi L, Ma X. Machine Learning Approaches for Rice Seedling Growth Stages Detection. FRONTIERS IN PLANT SCIENCE 2022; 13:914771. [PMID: 35755682 PMCID: PMC9225317 DOI: 10.3389/fpls.2022.914771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/04/2022] [Indexed: 06/15/2023]
Abstract
Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation, and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Both traditional machine learning algorithms and deep learning algorithms were investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at 3-m height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOGs) were selected as texture features and combined with the support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9, 85.9, 84.9, and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficientnet family and other state-of-art CNN models (VGG16, Resnet50, and Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47, 99.53, 99.39, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tool to detect rice seedling growth stages.
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Affiliation(s)
- Suiyan Tan
- College of Electronic Engineering, South China Agricultural University, Guangzhou, China
| | - Jingbin Liu
- College of Electronic Engineering, South China Agricultural University, Guangzhou, China
| | - Henghui Lu
- College of Electronic Engineering, South China Agricultural University, Guangzhou, China
| | - Maoyang Lan
- College of Electronic Engineering, South China Agricultural University, Guangzhou, China
| | - Jie Yu
- College of Electronic Engineering, South China Agricultural University, Guangzhou, China
| | - Guanzhong Liao
- College of Electronic Engineering, South China Agricultural University, Guangzhou, China
| | - Yuwei Wang
- College of Engineering, South China Agricultural University, Guangzhou, China
| | - Zehua Li
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
| | - Long Qi
- College of Engineering, South China Agricultural University, Guangzhou, China
| | - Xu Ma
- College of Engineering, South China Agricultural University, Guangzhou, China
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Ninomiya S. High-throughput field crop phenotyping: current status and challenges. BREEDING SCIENCE 2022; 72:3-18. [PMID: 36045897 PMCID: PMC8987842 DOI: 10.1270/jsbbs.21069] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/16/2021] [Indexed: 05/03/2023]
Abstract
In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.
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Affiliation(s)
- Seishi Ninomiya
- Graduate School of Agriculture and Life Sciences, The University of Tokyo, Nishitokyo, Tokyo 188-0002, Japan
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
- Corresponding author (e-mail: )
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