Abstract Optical technologies that are able to analyze physical properties of biological samples are increasingly drawing interest in modern agriculture. The use of X-rays for analysis of internal properties of agricultural products, such as seeds, has proven its worth in providing information regarding their quality in a non-destructive manner. However, visual evaluations of radiographic images are time-consuming, subjective, and highly prone to error. Therefore, it is necessary to develop methods that allow these analyses to be performed in an efficient and assertive manner. To that end, a free-access, open-source, and easy-to-use tool called IJCropSeed has been developed for high-throughput analysis of radiographic images of seeds from several agricultural crops. In addition, an experiment was conducted in which machine learning models were developed from the information obtained from the tool to predict the seed germination capacity and seedling vigor of Crambe abyssinica. The results showed that IJCropSeed had a high performance for the analysis of digital radiographic images of the 24 agricultural crops evaluated, with high speed and high precision of segmentation of the images. The use of parameters obtained with the tool, in combination with the machine learning models, proved to be highly efficient in classifying the quality of C. abyssinica seeds. It is a non-destructive and highly effective method.

中文翻译：

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IJCropSeed：一种用于作物种子射线照片高通量分析的开放获取工具

摘要 能够分析生物样品物理特性的光学技术越来越引起现代农业的兴趣。使用 X 射线分析农产品（例如种子）的内部特性，已证明其在以非破坏性方式提供有关其质量的信息方面是有价值的。然而，放射影像的视觉评估耗时、主观且极易出错。因此，有必要开发允许以有效和自信的方式进行这些分析的方法。为此，开发了一种名为 IJCropSeed 的免费访问、开源且易于使用的工具，用于对几种农作物种子的射线照相图像进行高通量分析。此外，进行了一项实验，其中根据从工具获得的信息开发机器学习模型，以预测 Crambe abyssinica 的种子发芽能力和幼苗活力。结果表明，IJCropSeed 对评价的 24 种农作物的数字射线照相图像进行分析具有较高的性能，图像分割速度快，精度高。使用该工具获得的参数与机器学习模型相结合，被证明在分类 C. abyssinica 种子的质量方面非常有效。这是一种非破坏性且高效的方法。结果表明，IJCropSeed 对评价的 24 种农作物的数字射线照相图像进行分析具有较高的性能，图像分割速度快，精度高。使用该工具获得的参数与机器学习模型相结合，被证明在分类 C. abyssinica 种子的质量方面非常有效。这是一种非破坏性且高效的方法。结果表明，IJCropSeed 对评价的 24 种农作物的数字射线照相图像进行分析具有较高的性能，图像分割速度快，精度高。使用该工具获得的参数与机器学习模型相结合，被证明在分类 C. abyssinica 种子的质量方面非常有效。这是一种非破坏性且高效的方法。