NEEDL-Bench: Dataset for Swiss Needle Cast and Stomata Detection in Microscopy Images

• URL: http://arxiv.org/abs/2607.12076v1
• Date: Mon, 13 Jul 2026 18:54:19 GMT
• Title: NEEDL-Bench: Dataset for Swiss Needle Cast and Stomata Detection in Microscopy Images
• Abstract summary: NEEDL-Bench is a microscopy detection benchmark for Swiss Needle Cast (SNC), a fungal disease of Douglas-fir trees.<n>Needl-Bench is a dataset of 3250 annotated images from 1082 Douglas-fir needles, annotated for both keypoints and bounding-box detectors.<n>We evaluate multiple popular keypoint and bounding box methods for detection on this dataset as a baseline and observe a maximum F1 score of 0.8479.
• Score: 39.66563302789307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present NEEDL-Bench, a microscopy detection benchmark for Swiss Needle Cast (SNC), a fungal disease of Douglas-fir trees. Douglas-fir is a keystone species of major ecological and economic importance as a softwood timber resource, and SNC affects productivity by forming sexual reproductive structures (pseudothecia) that emerge through the gas exchange pores (stomata) of the needles, thereby blocking gas exchange and compromising needle function. To date, there is no dataset for automatic computer vision detection of these structures, despite computer vision being well poised to standardize and viably scale severity measurements. To address this, we present NEEDL-Bench, a dataset of 3250 annotated images from 1082 Douglas-fir needles, annotated for both keypoints and bounding-box detectors. This dataset exhibits a challenging collection of features, including blur, poor object contrast, small objects of interest, and occlusions. To better capture both the nominal distribution of the data and the full breadth of rare structures, we present two distinct evaluation splits: either random sampling from the collected images or sequential sampling to maximize structural diversity. We evaluate multiple popular keypoint and bounding box methods for detection on this dataset as a baseline and observe a maximum F1 score of 0.8479, suggesting significant potential for gains from future development on this problem. Further, we find that larger models generally do not show commensurate gains in performance on this dataset, indicating that improvements on this problem will not come from scaling laws but rather from domain-specific inductive biases.

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