利用激光清洗技术对不锈钢表面进行清洗试验，研究不同激光功率(300 W、400 W、500 W)对清洗效果的影响。通过SEM和EDS分析不锈钢表面清洗前后的表面形貌及成分分布；利用白光干涉仪检测不锈钢表面粗糙度及清洗厚度。结果表明，随着激光功率的增加，不锈钢表面氧化物逐渐分解剥落，清洗厚度不断加深，在500 W时达到50 μm，并且造成基体部分损伤；粗糙度值先降低后增加，在400 W时达到最低值0.38 μm。激光清洗的清洗阈值近似为3.96×10³W/cm²，基体损伤阈值在5.52×10³W/cm²左右，不锈钢表面氧化层在400 W时达到最佳激光清洗效果。

For many tiller crops, the plant architecture (PA), including the plant fresh weight, plant height, number of tillers, tiller angle and stem diameter, significantly affects the grain yield. In this study, we propose a method based on volumetric reconstruction for high-throughput three-dimensional (3D) wheat PA studies. The proposed methodology involves plant volumetric reconstruction from multiple images, plant model processing and phenotypic parameter estimation and analysis. This study was performed on 80 Triticum aestivum plants, and the results were analyzed. Comparing the automated measurements with manual measurements, the mean absolute percentage error (MAPE) in the plant height and the plant fresh weight was 2.71% (1.08 cm with an average plant height of 40.07 cm) and 10.06% (1.41 g with an average plant fresh weight of 14.06 g), respectively. The root mean square error (RMSE) was 1.37 cm and 1.79 g for the plant height and plant fresh weight, respectively. The correlation coefficients were 0.95 and 0.96 for the plant height and plant fresh weight, respectively. Additionally, the proposed methodology, including plant reconstruction, model processing and trait extraction, required only approximately 20 s on average per plant using parallel computing on a graphics processing unit (GPU), demonstrating that the methodology would be valuable for a high-throughput phenotyping platform.

Total green leaf area (GLA) is an important trait for agronomic studies. However, existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive. A nondestructive method for estimating the total GLA of individual rice plants based on multiangle color images is presented. Using projected areas of the plant in images, linear, quadratic, exponential and power regression models for estimating total GLA were evaluated. Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area. And power models fit better than other models. In addition, the use of multiple side-view images was an efficient method for reducing the estimation error. The inclusion of the top-view projected area as a second predictor provided only a slight improvement of the total leaf area estimation. When the projected areas from multi-angle images were used, the estimated leaf area (ELA) using the power model and the actual leaf area had a high correlation coefficient (R2 > 0:98), and the mean absolute percentage error (MAPE) was about 6%. The method was capable of estimating the total leaf area in a nondestructive, accurate and efficient manner, and it may be used for monitoring rice plant growth.

在单株水稻表型测量研究中,为了实现绿叶面积和茎叶相关表型参数的准确计算提供技术保障,茎叶的分割是非常重要的一步.传统的人工测量方法费时费力,且主观性较强,而基于普通相机拍摄的彩色图像进行分割效果很差.本研究介绍了一种使用可见光-近红外高光谱成像系统自动区分单株盆栽水稻茎叶的方法.首先将各波长下的图像从原始二进制数据中提取出来,接着使用主成分分析所有波长下的图像,并提取出主要的主成分图像,再基于数字图像处理技术将茎叶区分开.实验结果表明,本系统以及文中所用方法对分蘖盛期的水稻茎叶有很好的分割效果,这为后续水稻茎叶表型性状高通量、数字化、无损准确提取提供了重要的技术保障,并进一步促进植物表型组学的发展.