Redirigiendo al acceso original de articulo en 24 segundos...
Inicio  /  Applied Sciences  /  Vol: 9 Par: 5 (2019)  /  Artículo
ARTÍCULO
TITULO

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

Insuck Baek    
Moon S. Kim    
Byoung-Kwan Cho    
Changyeun Mo    
Jinyoung Y. Barnaby    
Anna M. McClung and Mirae Oh    

Resumen

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed.

Palabras claves

 Artículos similares

       
 
Minghui Shao, Biao Wu, Yan Li and Xiaoli Jiang    
This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the juris... ver más

 
Zijia Zheng, Yizhu Jiang, Qiutong Zhang, Yanling Zhong and Lizheng Wang    
The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the wate... ver más
Revista: Water

 
Somayeh Shahrabadi, Telmo Adão, Emanuel Peres, Raul Morais, Luís G. Magalhães and Victor Alves    
The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computatio... ver más
Revista: Algorithms

 
Anibal Pedraza, Lucia Gonzalez, Oscar Deniz and Gloria Bueno    
HER2 overexpression is a prognostic and predictive factor observed in about 15% to 20% of breast cancer cases. The assessment of its expression directly affects the selection of treatment and prognosis. The measurement of HER2 status is performed by an e... ver más
Revista: Algorithms

 
Liming Li and Zeang Zhao    
To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal ... ver más
Revista: Applied Sciences