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Huang T, Huang Z, Peng X, Pang L, Sun J, Wu J, He J, Fu K, Wu J, Sun X. Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods. Front Cardiovasc Med 2024; 11:1308017. [PMID: 38984357 PMCID: PMC11232034 DOI: 10.3389/fcvm.2024.1308017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 06/11/2024] [Indexed: 07/11/2024] Open
Abstract
Objective This study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model. Methods We conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA). Results Logistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit. Conclusion This study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients.
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Affiliation(s)
- Tao Huang
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Zhihai Huang
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Xiaodong Peng
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Lingpin Pang
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jie Sun
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jinbo Wu
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jinman He
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Kaili Fu
- Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jun Wu
- Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Xishi Sun
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
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Li X, Chen M, He S, Xu X, He L, Wang L, Gao Y, Tang F, Gong T, Wang W, Xu M, Liu C, Yu L, Liu W, Yang W. Estimation of soybean yield based on high-throughput phenotyping and machine learning. FRONTIERS IN PLANT SCIENCE 2024; 15:1395760. [PMID: 38903425 PMCID: PMC11187272 DOI: 10.3389/fpls.2024.1395760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/20/2024] [Indexed: 06/22/2024]
Abstract
Introduction Soybeans are an important crop used for food, oil, and feed. However, China's soybean self-sufficiency is highly inadequate, with an annual import volume exceeding 80%. RGB cameras serve as powerful tools for estimating crop yield, and machine learning is a practical method based on various features, providing improved yield predictions. However, selecting different input parameters and models, specifically optimal features and model effects, significantly influences soybean yield prediction. Methods This study used an RGB camera to capture soybean canopy images from both the side and top perspectives during the R6 stage (pod filling stage) for 240 soybean varieties (a natural population formed by four provinces in China: Sichuan, Yunnan, Chongqing, and Guizhou). From these images, the morphological, color, and textural features of the soybeans were extracted. Subsequently, feature selection was performed on the image parameters using a Pearson correlation coefficient threshold ≥0.5. Five machine learning methods, namely, CatBoost, LightGBM, RF, GBDT, and MLP, were employed to establish soybean yield estimation models based on the individual and combined image parameters from the two perspectives extracted from RGB images. Results (1) GBDT is the optimal model for predicting soybean yield, with a test set R2 value of 0.82, an RMSE of 1.99 g/plant, and an MAE of 3.12%. (2) The fusion of multiangle and multitype indicators is conducive to improving soybean yield prediction accuracy. Conclusion Therefore, this combination of parameters extracted from RGB images via machine learning has great potential for estimating soybean yield, providing a theoretical basis and technical support for accelerating the soybean breeding process.
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Affiliation(s)
- Xiuni Li
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Menggen Chen
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Shuyuan He
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Xiangyao Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Lingxiao He
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Li Wang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Yang Gao
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Fenda Tang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Tao Gong
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyan Wang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Mei Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Chunyan Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Liang Yu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Weiguo Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyu Yang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
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3
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Dai Y, Yu S, Ma T, Ding J, Chen K, Zeng G, Xie A, He P, Peng S, Zhang M. Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages. FRONTIERS IN PLANT SCIENCE 2024; 15:1328834. [PMID: 38774220 PMCID: PMC11106403 DOI: 10.3389/fpls.2024.1328834] [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: 10/27/2023] [Accepted: 04/22/2024] [Indexed: 05/24/2024]
Abstract
Introduction Unmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice. Methods To investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT). Results The results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R 2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R 2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%-13.5%), demonstrating excellent practical applicability. Discussion Model evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.
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Affiliation(s)
- Yan Dai
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
- Jiangsu Province Engineering Research Center for Agricultural Soil-Water Efficient Utilization, Carbon Sequestration and Emission Reduction, Nanjing, China
| | - Shuang’en Yu
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
- Jiangsu Province Engineering Research Center for Agricultural Soil-Water Efficient Utilization, Carbon Sequestration and Emission Reduction, Nanjing, China
| | - Tao Ma
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
- Jiangsu Province Engineering Research Center for Agricultural Soil-Water Efficient Utilization, Carbon Sequestration and Emission Reduction, Nanjing, China
| | - Jihui Ding
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
- Jiangsu Province Engineering Research Center for Agricultural Soil-Water Efficient Utilization, Carbon Sequestration and Emission Reduction, Nanjing, China
| | - Kaiwen Chen
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
- Jiangsu Province Engineering Research Center for Agricultural Soil-Water Efficient Utilization, Carbon Sequestration and Emission Reduction, Nanjing, China
| | - Guangquan Zeng
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Airong Xie
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Pingru He
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Suhan Peng
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Mengxi Zhang
- College of Innovation and Entrepreneurship, Hunan Polytechnic of Water Resources and Electric Power, Changsha, China
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Umani K, Zhang C, McGee RJ, Vandemark GJ, Sankaran S. A pulse crop dataset of agronomic traits and multispectral images from multiple environments. Data Brief 2024; 53:110013. [PMID: 38435735 PMCID: PMC10907176 DOI: 10.1016/j.dib.2023.110013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/11/2023] [Accepted: 12/21/2023] [Indexed: 03/05/2024] Open
Abstract
Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the "Palouse" region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20-78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.
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Affiliation(s)
- Kingsley Umani
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
| | - Chongyuan Zhang
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, Indiana, United States
| | - Rebecca J. McGee
- USDA-ARS, Grain Legume Genetics and Physiology Research, Washington State University, Pullman, WA, United States
| | - George J. Vandemark
- USDA-ARS, Grain Legume Genetics and Physiology Research, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
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5
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Ji Y, Liu Z, Cui Y, Liu R, Chen Z, Zong X, Yang T. Faba bean and pea harvest index estimations using aerial-based multimodal data and machine learning algorithms. PLANT PHYSIOLOGY 2024; 194:1512-1526. [PMID: 37935623 PMCID: PMC10904323 DOI: 10.1093/plphys/kiad577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/13/2023] [Indexed: 11/09/2023]
Abstract
Early and high-throughput estimations of the crop harvest index (HI) are essential for crop breeding and field management in precision agriculture; however, traditional methods for measuring HI are time-consuming and labor-intensive. The development of unmanned aerial vehicles (UAVs) with onboard sensors offers an alternative strategy for crop HI research. In this study, we explored the potential of using low-cost, UAV-based multimodal data for HI estimation using red-green-blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors at 4 growth stages to estimate faba bean (Vicia faba L.) and pea (Pisum sativum L.) HI values within the framework of ensemble learning. The average estimates of RGB (faba bean: coefficient of determination [R2] = 0.49, normalized root-mean-square error [NRMSE] = 15.78%; pea: R2 = 0.46, NRMSE = 20.08%) and MS (faba bean: R2 = 0.50, NRMSE = 15.16%; pea: R2 = 0.46, NRMSE = 19.43%) were superior to those of TIR (faba bean: R2 = 0.37, NRMSE = 16.47%; pea: R2 = 0.38, NRMSE = 19.71%), and the fusion of multisensor data exhibited a higher estimation accuracy than those obtained using each sensor individually. Ensemble Bayesian model averaging provided the most accurate estimations (faba bean: R2 = 0.64, NRMSE = 13.76%; pea: R2 = 0.74, NRMSE = 15.20%) for whole growth stage, and the estimation accuracy improved with advancing growth stage. These results indicate that the combination of low-cost, UAV-based multimodal data and machine learning algorithms can be used to estimate crop HI reliably, therefore highlighting a promising strategy and providing valuable insights for high spatial precision in agriculture, which can help breeders make early and efficient decisions.
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Affiliation(s)
- Yishan Ji
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zehao Liu
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yuxing Cui
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Rong Liu
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhen Chen
- Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
| | - Xuxiao Zong
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Tao Yang
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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Aguilar-Ariza A, Ishii M, Miyazaki T, Saito A, Khaing HP, Phoo HW, Kondo T, Fujiwara T, Guo W, Kamiya T. UAV-based individual Chinese cabbage weight prediction using multi-temporal data. Sci Rep 2023; 13:20122. [PMID: 37978327 PMCID: PMC10656565 DOI: 10.1038/s41598-023-47431-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R2) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest.
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Affiliation(s)
- Andrés Aguilar-Ariza
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Masanori Ishii
- Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Midoricho, Nishitokyo-shi, Tokyo, 188-0002, Japan
| | - Toshio Miyazaki
- Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan
| | - Aika Saito
- Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan
| | | | - Hnin Wint Phoo
- Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan
| | - Tomohiro Kondo
- Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan
| | - Toru Fujiwara
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Wei Guo
- Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Midoricho, Nishitokyo-shi, Tokyo, 188-0002, Japan
| | - Takehiro Kamiya
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.
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7
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Tanaka Y, Watanabe T, Katsura K, Tsujimoto Y, Takai T, Tanaka TST, Kawamura K, Saito H, Homma K, Mairoua SG, Ahouanton K, Ibrahim A, Senthilkumar K, Semwal VK, Matute EJG, Corredor E, El-Namaky R, Manigbas N, Quilang EJP, Iwahashi Y, Nakajima K, Takeuchi E, Saito K. Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0073. [PMID: 38239736 PMCID: PMC10795498 DOI: 10.34133/plantphenomics.0073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 06/28/2023] [Indexed: 01/22/2024]
Abstract
Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world's food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t·ha-1 across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel-1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting.
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Affiliation(s)
- Yu Tanaka
- Graduate School of Agriculture,
Kyoto University, Kitashirakawa Oiwake-chou, Sakyo-ku, Kyoto 606-8502, Japan
- Graduate School of Environmental, Life, Natural Science and Technology,
Okayama University, 1-1-1, Tsushima Naka, Okayama 700-8530, Japan
| | - Tomoya Watanabe
- Graduate School of Mathematics,
Kyushu University, 744, Motooka, Fukuoka Shi Nishi Ku, Fukuoka 819-0395, Japan
| | - Keisuke Katsura
- Graduate School of Agriculture,
Tokyo University of Agriculture and Technology, 3-5-8 Saiwaicho, Fuchu, Tokyo 183-8509, Japan
| | - Yasuhiro Tsujimoto
- Japan International Research Center for Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan
| | - Toshiyuki Takai
- Japan International Research Center for Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan
| | - Takashi Sonam Tashi Tanaka
- Faculty of Applied Biological Sciences,
Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
- Artificial Intelligence Advanced Research Center,
Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
| | - Kensuke Kawamura
- Japan International Research Center for Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan
| | - Hiroki Saito
- Tropical Agriculture Research Front,
Japan International Research Center for Agricultural Sciences, 1091-1 Maezato, Ishigaki, Okinawa 907-0002, Japan
| | - Koki Homma
- Graduate School of Agricultural Science,
Tohoku University, Aramaki Aza-Aoba, Aoba, Sendai, Miyagi 980-8572, Japan
| | | | - Kokou Ahouanton
- Africa Rice Center (AfricaRice), 01 BP 2551 Bouaké, Côte d'Ivoire
| | - Ali Ibrahim
- Africa Rice Center (AfricaRice), Regional Station for the Sahel, B.P. 96, Saint-Louis, Senegal
| | - Kalimuthu Senthilkumar
- Africa Rice Center (AfricaRice), P.O. Box 1690, Ampandrianomby, Antananarivo, Madagascar
| | - Vimal Kumar Semwal
- Africa Rice Center (AfricaRice), Nigeria Station, c/o IITA, PMB 5320, Ibadan, Nigeria
| | - Eduardo Jose Graterol Matute
- Latin American Fund for Irrigated Rice - The Alliance of Bioversity International and CIAT, Km 17 Recta Cali-Palmira, C.P. 763537, A.A. 6713, Cali, Colombia
| | - Edgar Corredor
- Latin American Fund for Irrigated Rice - The Alliance of Bioversity International and CIAT, Km 17 Recta Cali-Palmira, C.P. 763537, A.A. 6713, Cali, Colombia
| | - Raafat El-Namaky
- Rice Research and Training Center,
Field Crops Research Institute, ARC, Giza, Egypt
| | - Norvie Manigbas
- Philippine Rice Research Institute (PhilRice), Maligaya, Science City of Muñoz, 3119 Nueva Ecija, Philippines
| | - Eduardo Jimmy P. Quilang
- Philippine Rice Research Institute (PhilRice), Maligaya, Science City of Muñoz, 3119 Nueva Ecija, Philippines
| | - Yu Iwahashi
- Graduate School of Agriculture,
Kyoto University, Kitashirakawa Oiwake-chou, Sakyo-ku, Kyoto 606-8502, Japan
| | - Kota Nakajima
- Graduate School of Agriculture,
Kyoto University, Kitashirakawa Oiwake-chou, Sakyo-ku, Kyoto 606-8502, Japan
| | - Eisuke Takeuchi
- Graduate School of Agriculture,
Kyoto University, Kitashirakawa Oiwake-chou, Sakyo-ku, Kyoto 606-8502, Japan
| | - Kazuki Saito
- Japan International Research Center for Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan
- Africa Rice Center (AfricaRice), 01 BP 2551 Bouaké, Côte d'Ivoire
- International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila 1301, Philippines
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Li X, Xu X, Xiang S, Chen M, He S, Wang W, Xu M, Liu C, Yu L, Liu W, Yang W. Soybean leaf estimation based on RGB images and machine learning methods. PLANT METHODS 2023; 19:59. [PMID: 37330499 DOI: 10.1186/s13007-023-01023-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/03/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND RGB photographs are a powerful tool for dynamically estimating crop growth. Leaves are related to crop photosynthesis, transpiration, and nutrient uptake. Traditional blade parameter measurements were labor-intensive and time-consuming. Therefore, based on the phenotypic features extracted from RGB images, it is essential to choose the best model for soybean leaf parameter estimation. This research was carried out to speed up the breeding procedure and provide a novel technique for precisely estimating soybean leaf parameters. RESULTS The findings demonstrate that using an Unet neural network, the IOU, PA, and Recall values for soybean image segmentation can achieve 0.98, 0.99, and 0.98, respectively. Overall, the average testing prediction accuracy (ATPA) of the three regression models is Random forest > Cat Boost > Simple nonlinear regression. The Random forest ATPAs for leaf number (LN), leaf fresh weight (LFW), and leaf area index (LAI) reached 73.45%, 74.96%, and 85.09%, respectively, which were 6.93%, 3.98%, and 8.01%, respectively, higher than those of the optimal Cat Boost model and 18.78%, 19.08%, and 10.88%, respectively, higher than those of the optimal SNR model. CONCLUSION The results show that the Unet neural network can separate soybeans accurately from an RGB image. The Random forest model has a strong ability for generalization and high accuracy for the estimation of leaf parameters. Combining cutting-edge machine learning methods with digital images improves the estimation of soybean leaf characteristics.
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Affiliation(s)
- Xiuni Li
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Xiangyao Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Shuai Xiang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Menggen Chen
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Shuyuan He
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyan Wang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Mei Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Chunyan Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Liang Yu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Weiguo Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China.
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China.
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China.
| | - Wenyu Yang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
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Bai D, Li D, Zhao C, Wang Z, Shao M, Guo B, Liu Y, Wang Q, Li J, Guo S, Wang R, Li YH, Qiu LJ, Jin X. Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles. FRONTIERS IN PLANT SCIENCE 2022; 13:1012293. [PMID: 36589058 PMCID: PMC9795850 DOI: 10.3389/fpls.2022.1012293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/28/2022] [Indexed: 06/15/2023]
Abstract
The estimation of yield parameters based on early data is helpful for agricultural policymakers and food security. Developments in unmanned aerial vehicle (UAV) platforms and sensor technology help to estimate yields efficiency. Previous studies have been based on less cultivars (<10) and ideal experimental environments, it is not available in practical production. Therefore, the objective of this study was to estimate the yield parameters of soybean (Glycine max (L.) Merr.) under lodging conditions using RGB information. In this study, 17 time point data throughout the soybean growing season in Nanchang, Jiangxi Province, China, were collected, and the vegetation index, texture information, canopy cover, and crop height were obtained by UAV-image processing. After that, partial least squares regression (PLSR), logistic regression (Logistic), random forest regression (RFR), support vector machine regression (SVM), and deep learning neural network (DNN) were used to estimate the yield parameters. The results can be summarized as follows: (1) The most suitable time point to estimate the yield was flowering stage (48 days), which was when most of the soybean cultivars flowered. (2) The multiple data fusion improved the accuracy of estimating the yield parameters, and the texture information has a high potential to contribute to the estimation of yields, and (3) The DNN model showed the best accuracy of training (R2=0.66 rRMSE=32.62%) and validation (R2=0.50, rRMSE=43.71%) datasets. In conclusion, these results provide insights into both best estimate period selection and early yield estimation under lodging condition when using remote sensing.
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Affiliation(s)
- Dong Bai
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chaosen Zhao
- Nanchang Branch of National Center of Oil Crops Improvement, Jiangxi Province Key Laboratory of Oil Crops Biology, Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Zixu Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mingchao Shao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bingfu Guo
- Nanchang Branch of National Center of Oil Crops Improvement, Jiangxi Province Key Laboratory of Oil Crops Biology, Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Yadong Liu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jindong Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China
| | - Shiyu Guo
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Agriculture, Northeast Agricultural University, Harbin, China
| | - Ruizhen Wang
- Nanchang Branch of National Center of Oil Crops Improvement, Jiangxi Province Key Laboratory of Oil Crops Biology, Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Ying-hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Li-juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiuliang Jin
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China
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Long J, Wu W, Sun S, Shao Y, Duan C, Guo Y, Zhu Z. Berkeleyomyces rouxiae is a causal agent of root rot complex on faba bean ( Vicia faba L.). FRONTIERS IN PLANT SCIENCE 2022; 13:989517. [PMID: 36570924 PMCID: PMC9774499 DOI: 10.3389/fpls.2022.989517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 11/09/2022] [Indexed: 06/17/2023]
Abstract
Faba bean (Vicia faba L.) is an important food and feed legume crop in the world. The root rot complex caused by various pathogens is a main constraint in faba bean production. In April 2021, a severe disease of faba bean with symptoms of black necrosis on roots occurred in experimental fields at the Linxia Institute of Agricultural Sciences, Gansu Province, China. This study aimed to identify the pathogen and evaluate the resistance of faba bean cultivars. The pathogen was isolated from infected soils, and five representative isolates were identified as Berkeleyomyces rouxiae based on morphological characteristics, pathogenicity, and molecular phylogenetic analyses. A host range test showed that chickpea, common bean, cowpea, mung bean, rice bean, lentil, and hyacinth bean were susceptible hosts of the faba bean isolate, whereas adzuki bean, pea, and soybean were non-susceptible hosts, and maize and wheat were non-hosts. Identification of resistance among 36 faba bean cultivars was carried out, and six cultivars were found to be moderately resistant to B. rouxiae. In this study, we first reported black root rot on faba bean caused by B. rouxiae, confirmed and expanded the host range of B. rouxiae, and identified resistant faba bean cultivars.
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Affiliation(s)
- Juechen Long
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- Institute of Specialty Crop, Chongqing Academy of Agricultural Sciences, Chongqing, China
| | - Wenqi Wu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Suli Sun
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yang Shao
- Linxia Institute of Agricultural Sciences, Linxia, Gansu, China
| | - Canxing Duan
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yanping Guo
- Linxia Institute of Agricultural Sciences, Linxia, Gansu, China
| | - Zhendong Zhu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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