Chen Chao, Jiang Yue, Zhu Xiaoqing
Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China.
Plants (Basel). 2024 Dec 4;13(23):3403. doi: 10.3390/plants13233403.
For accurate segmentation of lettuce canopy images, dealing with uneven illumination and background interference, hyperspectral imaging technology was applied to capture images of lettuce from the rosette to nodule stages. The spectral ratio method was used to select the characteristic wavelengths, and the characteristic wavelength images were denoised and image fused before being processed by filtering and threshold segmentation. To verify the accuracy of this segmentation method, the manual segmentation method and the segmentation method used in this study were compared, and the area overlap degree (AOM) and misclassification rate (ME) were used as criteria to evaluate the segmentation results. The results showed that the segmentation effect was the best when 553.8 nm, 702.5 nm and 731.3 nm were selected as the characteristic wavelengths of lettuce for the spectral ratio method, with an AOM of 0.9526 and an ME of 0.0477. Both have a variance of less than 0.01 and have the best stability. Hyperspectral imaging technology combined with multi-wavelength image and multi-threshold segmentation can achieve accurate segmentation of lettuce canopy images.
为了准确分割生菜冠层图像，处理光照不均和背景干扰问题，采用高光谱成像技术采集生菜从莲座期到结球期的图像。利用光谱比值法选择特征波长，对特征波长图像进行去噪和图像融合，然后通过滤波和阈值分割进行处理。为验证该分割方法的准确性，将人工分割方法与本研究使用的分割方法进行比较，采用面积重叠度（AOM）和误分类率（ME）作为评价分割结果的标准。结果表明，光谱比值法选择553.8nm、702.5nm和731.3nm作为生菜的特征波长时，分割效果最佳，AOM为0.9526，ME为0.0477。两者方差均小于0.01，稳定性最佳。高光谱成像技术结合多波长图像和多阈值分割能够实现生菜冠层图像的准确分割。
