Journal
SENSORS
Volume 22, Issue 16, Pages -Publisher
MDPI
DOI: 10.3390/s22166064
Keywords
hyperspectral imaging; sparrow search algorithm (SSA); random forest (RF); maize mildew; nondestructive detection
Funding
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA28110502]
- Science and Technology Development Plan Project of Jilin province [20210201044GX, 20210404020NC]
- Science and Technology Project of Education Department of Jilin Province [JJKH20220330KJ, JJKH20200328KJ]
Ask authors/readers for more resources
This study successfully established a method for classifying maize seeds using hyperspectral imaging and machine learning algorithms, which can effectively detect the degree of mildew and provide new ideas for quality assessment and selection of seeds.
Mildew of maize seeds may affect their germination rates and reduce crop quality. It is crucial to classify maize seeds efficiently and without destroying their original structure. This study aimed to establish hyperspectral datasets using hyperspectral imaging (HSI) of maize seeds with different degrees of mildew and then classify them using spectral characteristics and machine learning algorithms. Initially, the images were processed with Otus and morphological operations. Each seed's spectral features were extracted based on its coding, its edge, region of interest (ROI), and original pixel coding. Random forest (RF) models were optimized using the sparrow search algorithm (SSA), which is incapable of escaping the local optimum; hence, it was optimized using a modified reverse sparrow search algorithm (JYSSA) strategy. This reverse strategy selects the top 10% as the elite group, allowing us to escape from local optima while simultaneously expanding the range of the sparrow search algorithm's optimal solution. Finally, the JYSSA-RF algorithm was applied to the validation set, with 96% classification accuracy, 100% precision, and a 93% recall rate. This study provides novel ideas for future nondestructive detection of seeds and moldy seed selection by combining hyperspectral imaging and JYSSA algorithms based on optimized RF.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available