Data from: Efficient imaging and computer vision detection of two cell shapes in young cotton fibers

Methods
Cotton plants were grown in a well-controlled greenhouse in the NC State Phytotron as described previously (Pierce et al, 2019). Flowers were tagged on the day of anthesis and harvested three days post anthesis (3 DPA). The distinct fiber shapes had already formed by 2 DPA (Stiff and Haigler, 2016; Graham and Haigler, 2021), and fibers were still relatively short at 3 DPA, which facilitated the visualization of multiple fiber tips in one image.
Cotton fiber sample preparation, digital image collection, and image analysis:
Ovules with attached fiber were fixed in the greenhouse. The fixative previously used (Histochoice) (Stiff and Haigler, 2016; Pierce et al., 2019; Graham and Haigler, 2021) is obsolete, which led to testing and validation of another low-toxicity, formalin-free fixative (#A5472; Sigma-Aldrich, St. Louis, MO; Fig. S1). The boll wall was removed without damaging the ovules. (Using a razor blade, cut away the top 3 mm of the boll. Make about 1 mm deep longitudinal incisions between the locule walls, and finally cut around the base of the boll.) All of the ovules with attached fiber were lifted out of the locules and fixed (1 h, RT, 1:10 tissue:fixative ratio) prior to optional storage at 4°C. Immediately before imaging, ovules were examined under a stereo microscope (incident light, black background, 31X) to select three vigorous ovules from each boll while avoiding drying. Ovules were rinsed (3 x 5 min) in buffer [0.05 M PIPES, 12 mM EGTA. 5 mM EDTA and 0.1% (w/v) Tween 80, pH 6.8], which had lower osmolarity than a microtubule-stabilizing buffer used previously for aldehyde-fixed fibers (Seagull, 1990; Graham and Haigler, 2021). While steadying an ovule with forceps, one to three small pieces of its chalazal end with attached fibers were dissected away using a small knife (#10055-12; Fine Science Tools, Foster City, CA). Each ovule piece was placed in a single well of a 24-well slide (#63430-04; Electron Microscopy Sciences, Hatfield, PA) containing a single drop of buffer prior to applying and sealing a 24 x 60 mm coverslip with vaseline.
Samples were imaged with brightfield optics and default settings for the 2.83 mega-pixel, color, CCD camera of the Keyence BZ-X810 imaging system (www.keyence.com; housed in the Cellular and Molecular Imaging Facility of NC State). The location of each sample in the 24-well slides was identified visually using a 2X objective and mapped using the navigation function of the integrated Keyence software. Using the 10X objective lens (plan-apochromatic; NA 0.45) and 60% closed condenser aperture setting, a region with many fiber apices was selected for imaging using the multi-point and z-stack capture functions. The precise location was recorded by the software prior to visual setting of the limits of the z-plane range (1.2 µm step size). Typically, three 24-sample slides (representing three accessions) were set up in parallel prior to automatic image capture. The captured z-stacks for each sample were processed into one two-dimensional image using the full-focus function of the software. (Occasional samples contained too much debris for computer vision to be effective, and these were reimaged.)
Resources in this dataset:Resource Title: Deltapine 90 - Manually Annotated Training Set. File Name: GH3 DP90 Keyence 1_45 JPEG.zipResource Description: These images were manually annotated in Labelbox.Resource Title: Deltapine 90 - AI-Assisted Annotated Training Set. File Name: GH3 DP90 Keyence 46_101 JPEG.zipResource Description: These images were AI-labeled in RoboFlow and then manually reviewed in RoboFlow.
Resource Title: Deltapine 90 - Manually Annotated Training-Validation Set. File Name: GH3 DP90 Keyence 102_125 JPEG.zipResource Description: These images were manually labeled in LabelBox, and then used for training-validation for the machine learning model.Resource Title: Phytogen 800 - Evaluation Test Images. File Name: Gb cv Phytogen 800.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Pima 3-79 - Evaluation Test Images. File Name: Gb cv Pima 379.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Pima S-7 - Evaluation Test Images. File Name: Gb cv Pima S7.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Coker 312 - Evaluation Test Images. File Name: Gh cv Coker 312.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Deltapine 90 - Evaluation Test Images. File Name: Gh cv Deltapine 90.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Half and Half - Evaluation Test Images. File Name: Gh cv Half and Half.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Fiber Tip Annotations - Manual. File Name: manual_annotations.coco_.jsonResource Description: Annotations in COCO.json format for fibers. Manually annotated in Labelbox.Resource Title: Fiber Tip Annotations - AI-Assisted. File Name: ai_assisted_annotations.coco_.jsonResource Description: Annotations in COCO.json format for fibers. AI annotated with human review in Roboflow.

Resource Title: Model Weights (iteration 600). File Name: model_weights.zipResource Description: The final model, provided as a zipped Pytorch `.pth` file. It was chosen at training iteration 600.
The model weights can be imported for use of the fiber tip type detection neural network in Python.Resource Software Recommended: Google Colab,url: https://research.google.com/colaboratory/

实验方法
棉株在先前描述的NC State Phytotron温室中经过严格控制生长（Pierce等，2019年）。花朵在花蕾开放当天进行标记，并在开花后第三天（3 DPA）进行收获。至2 DPA时，纤维的特定形状已经形成（Stiff和Haigler，2016年；Graham和Haigler，2021年），而纤维在3 DPA时仍然相对较短，这有助于在一幅图像中可视化多个纤维顶端。
棉花纤维样本制备、数字图像采集和图像分析：
带有纤维的胚珠在温室中固定。之前使用的固定剂（Histochoice）（Stiff和Haigler，2016年；Pierce等，2019年；Graham和Haigler，2021年）已不再适用，这导致了另一种低毒性、无福尔马林的固定剂（#A5472；Sigma-Aldrich，圣路易斯，密苏里州；图S1）的测试和验证。去除了棉铃壁，而不损伤胚珠。（使用剃刀片切去棉铃顶部3毫米，在室壁之间做约1毫米深的纵向切口，最后围绕棉铃底部切割。）所有带有纤维的胚珠都被从室腔中取出并固定（1小时，室温，1:10组织:固定剂比例），然后在4°C下进行可选储存。在成像前，使用立体显微镜（入射光，黑色背景，31倍）检查胚珠，以从每个棉铃中选择三个旺盛的胚珠，同时避免干燥。胚珠用缓冲液（0.05 M PIPES，12 mM EGTA，5 mM EDTA和0.1%（w/v）Tween 80，pH 6.8）冲洗（3 x 5分钟），该缓冲液的渗透压低于先前用于醛固定纤维的微管稳定缓冲液（Seagull，1990年；Graham和Haigler，2021年）。在用镊子稳定胚珠的同时，使用小刀（#10055-12；Fine Science Tools，福斯特城，加利福尼亚州）从胚珠的顶端的基部分离出带有纤维的一到三小段。每个胚珠片段被放置在24孔载玻片（#63430-04；Electron Microscopy Sciences，哈特菲尔德，宾夕法尼亚州）的单个孔中，该孔含有单一的缓冲液滴，然后在表面涂抹凡士林并密封一个24 x 60毫米的盖玻片。
样本使用明场光学和Keyence BZ-X810成像系统（www.keyence.com；位于NC State的细胞和分子成像设施）的2.83百万像素、彩色、CCD摄像机的默认设置进行成像。使用2X物镜通过视觉识别24孔载玻片中每个样本的位置，并使用集成Keyence软件的导航功能进行映射。使用10X物镜（计划阿贝式；NA 0.45）和60%封闭聚光镜孔径设置，通过多点捕获和z堆栈捕获功能选择一个具有许多纤维顶端的区域进行成像。软件在设置z平面范围（1.2 µm步长）的极限之前记录了精确位置。通常，在自动图像捕获之前，平行设置三个包含三个不同来源样本的24样本载玻片。每个样本捕获的z堆栈使用软件的全焦距功能处理成一张二维图像。（偶尔的样本含有过多的碎片，使得计算机视觉无法有效工作，这些样本需要重新成像。）
数据集中包含的资源：
资源标题：Deltapine 90 - 手动标注训练集。文件名：GH3 DP90 Keyence 1_45 JPEG.zip
资源描述：这些图像在Labelbox中进行了手动标注。
资源标题：Deltapine 90 - AI辅助标注训练集。文件名：GH3 DP90 Keyence 46_101 JPEG.zip
资源描述：这些图像在RoboFlow中进行了AI标注，然后手动在RoboFlow中进行了审查。
资源标题：Deltapine 90 - 手动标注训练-验证集。文件名：GH3 DP90 Keyence 102_125 JPEG.zip
资源描述：这些图像在LabelBox中进行了手动标注，然后用于机器学习模型的训练-验证。
资源标题：Phytogen 800 - 评估测试图像。文件名：Gb cv Phytogen 800.zip
资源描述：这些图像用于验证机器学习模型。它们在ImageJ中进行了手动标注。
资源标题：Pima 3-79 - 评估测试图像。文件名：Gb cv Pima 379.zip
资源描述：这些图像用于验证机器学习模型。它们在ImageJ中进行了手动标注。
资源标题：Pima S-7 - 评估测试图像。文件名：Gb cv Pima S7.zip
资源描述：这些图像用于验证机器学习模型。它们在ImageJ中进行了手动标注。
资源标题：Coker 312 - 评估测试图像。文件名：Gh cv Coker 312.zip
资源描述：这些图像用于验证机器学习模型。它们在ImageJ中进行了手动标注。
资源标题：Deltapine 90 - 评估测试图像。文件名：Gh cv Deltapine 90.zip
资源描述：这些图像用于验证机器学习模型。它们在ImageJ中进行了手动标注。
资源标题：Half and Half - 评估测试图像。文件名：Gh cv Half and Half.zip
资源描述：这些图像用于验证机器学习模型。它们在ImageJ中进行了手动标注。
资源标题：纤维顶端标注 - 手动。文件名：manual_annotations.coco_.
资源描述：COCO.格式的纤维标注。在Labelbox中进行手动标注。
资源标题：纤维顶端标注 - AI辅助。文件名：ai_assisted_annotations.coco_.
资源描述：COCO.格式的纤维标注。在Roboflow中进行AI标注，并有人工审查。
资源标题：模型权重（迭代600）。文件名：model_weights.zip
资源描述：作为zipped Pytorch `.pth`文件提供的最终模型。该模型是在训练迭代600时选择的。
模型权重可以导入Python中用于纤维顶端类型检测神经网络的用途。
推荐使用的软件：Google Colab，网址：https://research.google.com/colaboratory/