1
|
Nguyen VD, Sarić R, Burge T, Berkowitz O, Trtilek M, Whelan J, Lewsey MG, Čustović E. Noninvasive imaging technologies in plant phenotyping. TRENDS IN PLANT SCIENCE 2022; 27:316-317. [PMID: 34257004 DOI: 10.1016/j.tplants.2021.06.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/09/2021] [Accepted: 06/17/2021] [Indexed: 06/13/2023]
Affiliation(s)
- Viet D Nguyen
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Rijad Sarić
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Timothy Burge
- Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Oliver Berkowitz
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Martin Trtilek
- Photon Systems Instruments plant phenotyping research centre, Photon System Instruments, 664 24 Drasov, Brno, Czech Republic
| | - James Whelan
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia.
| | - Mathew G Lewsey
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Edhem Čustović
- Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| |
Collapse
|
2
|
Zhao D, Feng S, Cao Y, Yu F, Guan Q, Li J, Zhang G, Xu T. Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm. FRONTIERS IN PLANT SCIENCE 2022; 13:879668. [PMID: 35599890 PMCID: PMC9120945 DOI: 10.3389/fpls.2022.879668] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/19/2022] [Indexed: 05/03/2023]
Abstract
Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.
Collapse
Affiliation(s)
- Dongxue Zhao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Shuai Feng
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Yingli Cao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
| | - Fenghua Yu
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
| | - Qiang Guan
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Jinpeng Li
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Guosheng Zhang
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Tongyu Xu
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
- *Correspondence: Tongyu Xu,
| |
Collapse
|
3
|
Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2021. [DOI: 10.3390/jmse9050488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120° E, 6°–8° N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.
Collapse
|