Journal
JOURNAL OF FOOD PROCESS ENGINEERING
Volume 44, Issue 1, Pages -Publisher
WILEY
DOI: 10.1111/jfpe.13584
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Funding
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- National Natural Science Foundation of China [31971788]
- Project of Agricultural Equipment Department of Jiangsu University [4121680001]
- Synergistic Innovation Center of Jiangsu Modern Agricultural Equipment and Technology [4091600002]
- Science and Technology Support Project of Changzhou (Social Development) [CE20185029]
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Hyperspectral images were used to identify tea white star disease and anthrax. High correlation in average spectra was found between the two diseases, but classification results were poor. By using mask technology and extreme learning machine based on neural network structure, classification accuracy was significantly improved.
Hyperspectral images were used to identify the two similar diseases of tea white star disease and anthrax in this research. The average spectra of healthy leaves, white star disease, and anthrax leaves were collected, respectively. It was found that the average spectrum of white star disease and anthrax had strong morphological correlation and poor classification results. Then, the mask technology was used to segment the diseased region of leaves in order to get the best region of interest. After that, the average spectral separability of diseased region was significantly improved. Finally, through the comparison of the classification results between support vector machine and extreme learning machine (ELM), it was found that the ELM model based on neural network structure got the best identification results, and its classification accuracy reached 95.77%. This study provides a new method to identify similar diseases of leaf plants. Practical Applications There is a certain similarity in disease characteristics between white star disease and anthracnose disease of tea. The similarity leads to a low accuracy in the classification and identification of diseases using hyperspectral technology. In order to solve this problem, this research proposed a spectrum extraction method based on the region of interest of the spots region. The experimental results showed that the average spectrum obtained from the leaf spots region could significantly improve the characterization of tea white star disease and anthrax, and the classification accuracy of the prediction model was significantly improved. This study provides a theoretical reference for the identification of tea similar diseases.
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