| Rapid detection of cucumber leaf water content based on hyperspectral technology and machine learning |
|
View Fulltext View/Add Comment Download reader |
| |
| DOI：10.7606/j.issn.1000-7601.2026.03.25 |
| Key Words: cucumber leaf water content hyperspectral machine learning |
| Author Name | Affiliation | | YANG Haiyang | College of Enology and Horticulture, Ningxia University,Yinchuan, Ningxia 750000, China | | YANG Jiahao | College of Enology and Horticulture, Ningxia University,Yinchuan, Ningxia 750000, China | | LV Pengyuan | College of Information Engineering, Ningxia University,Yinchuan, Ningxia 750000, China | | GESANG Quzhen | Vegetable Research Institute, Tibet Academy of Agriculture and Animal Husbandry Sciences, Lhasa, Xizang 850000, China | | WANG Wenhai | Vegetable Research Institute, Tibet Academy of Agriculture and Animal Husbandry Sciences, Lhasa, Xizang 850000, China | | WANG Jitao | Ningxia Agricultural Technology Extension Center,Yinchuan, Ningxia 750000, China | | MA Hua | College of Enology and Horticulture, Ningxia University,Yinchuan, Ningxia 750000, China | | CAO Yune | College of Enology and Horticulture, Ningxia University,Yinchuan, Ningxia 750000, China |
|
| Hits: 261 |
| Download times: 13 |
| Abstract: |
| Traditional methods for determining plant water content are often time\|consuming, labor\|intensive, destructive, and difficult to apply for real\|time continuous monitoring under field conditions. To address these limitations, this study developed a rapid and nondestructive monitoring model for cucumber leaf water content based on hyperspectral imaging technology combined with machine learning algorithms. Cucumber leaves at the flowering and fruiting stages were used as the research objects, and leaf spectral reflectance was extracted. Several spectral preprocessing methods were applied, including convolution smoothing, multiplicative scatter correction (MSC), moving average, baseline correction, and standard normal variate (SNV) transformation. Subsequently, feature wavelengths were selected using competitive adaptive reweighted sampling, successive projections algorithm, uninformative variable elimination (UVE), and genetic algorithm–partial least squares. Based on the selected feature wavelengths, random forest, extreme learning machine, and convolutional neural network models were established. The results showed that the convolutional neural network (CNN) model constructed using MSC preprocessing combined with UVE\|based feature wavelength selection achieved the best performance. The prediction coefficients of determination at the flowering and fruiting stages were 0.844 and 0.781, respectively, while the corresponding RMSE values were 0.021 g·g-1 and 0.005 g·g-1. These results indicate that hyperspectral imaging combined with the MSC-UVE-CNN method can achieve rapid and nondestructive detection of cucumber leaf water content, providing a technical reference for cucumber water status monitoring and precision irrigation management. |
|
|
|