plantcv-mcp 是一个 MCP 服务器，允许 AI 代理运行 PlantCV 植物 表型分析。它返回性状编号及其测量时的分段叠加层，以及 当分段退化时，拒绝返回数字。

左侧，channel="a", object_type="dark"：遮罩覆盖画面的3.1%，9个组件， area=32427。**对，channel="s", object_type="dark"：面罩覆盖96.1%;它是 背景，area=1007829。

你为什么要有叠加层

两张图像来自同一个文件和相同的阈值方法;只有通道不同。 没有图片时，两次运行都返回了17个特征，单位正确且可信 大小。右边那个正在测量植物后面的墙。红色标记 测量像素;一条青色线条沿着遮罩的边缘描绘。

所以 segment() 返回覆盖层和诊断，但没有特征，而且 measure() 需要 session_id segment() 铸币：在你看到图像之前，不会被测量。该 例外是measure_images()，它对最多200张图片应用一个配方，且不加叠加。 先在一张图片的叠加层上确定配方（服务器不会检查）;每张图片都静止不动

plantcv-mcp

plantcv-mcp is an MCP server that lets AI agents run PlantCV plant phenotyping. It returns trait numbers and the segmentation overlay they were measured from, and refuses to return numbers when the segmentation is degenerate.

Left, channel="a", object_type="dark": the mask covers 3.1% of the frame, 9 components, area=32427. Right, channel="s", object_type="dark": the mask covers 96.1%; it is the background, area=1007829.

Why you get the overlay

Both images come from the same file and the same threshold method; only the channel differs. Without the picture, both runs return 17 traits with correct units and believable magnitudes. The one on the right is measuring the wall behind the plants. Red marks the measured pixels; a cyan line traces the mask’s edge.

So segment() returns the overlay and diagnostics but no traits, and measure() needs the session_id that segment() mints: no single image is measured before you are shown it. The exception is measure_images(), which applies one recipe to up to 200 images without overlays. Settle the recipe on one image’s overlay first (the server does not check); every image still passes the segment() guards, and one that trips a blocking guard gets no traits.

Measured with PlantCV 4.11.3 on a synthetic render (first row: tests/fixtures/multi_specimen.png) and on photographs, each of these returns believable numbers with correct units: