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AgroBench: Vision-Language Model Benchmark in Agriculture

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A new expert-annotated benchmark of 4,342 agricultural questions shows most open-source vision-language models score near chance on weed identification.

desk verdict A genuinely useful expert-annotated agricultural VLM benchmark, but the human validation and label-validity evidence are weaker than the headline claims and need tightening before the 'VLMs lack agricultural knowledge' conclusion fully lands. read the letter →

arxiv 2507.20519 v1 pith:MEG7LJEH submitted 2025-07-28 cs.CV

Risa Shinoda , Nakamasa Inoue , Hirokatsu Kataoka , Masaki Onishi , Yoshitaka Ushiku This is my paper · ORCID
classification cs.CV
keywords AgroBenchvision-languagemodelsagriculturalcomputervisionweedidentificationcropdiseaseexpertannotationmultiple-choicequestionansweringVLMevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

AgroBench is a new benchmark for evaluating vision-language models in agriculture, built from 4,342 multiple-choice questions across seven tasks: disease identification, pest identification, weed identification, crop management, disease management, machine usage, and traditional farming methods. Unlike earlier agricultural VLM datasets that relied on synthetic GPT-generated annotations, every question in AgroBench was created and reviewed by agronomists, and the image set covers 203 crop categories and 682 disease categories. The paper reports that closed-source models such as GPT-4o and Gemini outperform open-source models overall, but that all models lag on fine-grained identification, with most open-source models at or near random guessing on weed identification. Error analysis attributes the majority of failures (about 52 percent) to missing domain knowledge rather than perception or reasoning. If the benchmark results hold, they show that current VLMs are not yet reliable for practical agricultural identification and that AgroBench provides a yardstick for measuring future progress.

What carries the argument

The central machinery is AgroBench itself, a benchmark organized as seven vision-language tasks, each using five-option multiple-choice questions with a single correct answer. Images were curated from licensed and public sources, with one author holding a Ph.D. in agriculture selecting clear images and another set of agricultural degree-holders reviewing the QA pairs. For the identification tasks, the benchmark deliberately includes misleading distractor options—diseases with similar symptoms, pests associated with the target crop, and weeds of similar appearance. AgroBench also computes task accuracy as an average across tasks rather than QA count, so that large categories do not dominate the overall score. This design is what lets the paper attribute failures to category-specific knowledge gaps.

What would settle it

If an independent panel of agronomists—who have not seen the benchmark—took the weed and disease identification questions under the same five-option, forced-choice format and also scored near the 20 percent random baseline, that would indicate the benchmark is measuring label ambiguity or unanswerable questions rather than VLM capability. Since the paper's own human validation shows agricultural degree-holders scoring 20-25 percent on these tasks, this check is directly relevant; a larger panel reproducing near-chance human accuracy would force a reinterpretation of the claim that VLMs specifically lack fine-grained agricultural knowledge.

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Extended reading notes

Core claim

The central discovery of this paper is the construction and evaluation of AgroBench, an expert-annotated, multiple-choice benchmark of 4,342 QA pairs across seven agricultural tasks. On this benchmark, the authors establish that current vision-language models, and open-source models in particular, are far from reliable on fine-grained agricultural identification: weed identification proves the hardest task, with most open-source models performing near the random baseline, and disease identification also scores well below the management tasks. The paper's evaluation further shows that closed-source models outperform open-source models on average, and that the most common error type is lack of knowledge (51.92 percent), followed by perceptual error (32.69 percent). The authors argue that these results demonstrate the need for more fine-grained agricultural visual knowledge in VLMs and that AgroBench can serve as a comprehensive evaluation tool for future agricultural VLM development.

Load-bearing premise

The correct answers supplied by the expert annotators are accurate and unambiguous, so that a low model score is evidence of a model limitation rather than a bad or debatable question.

Editorial extensions

If this is right

  • Open-source vision-language models are not yet ready for weed identification in real farm settings; most perform near random guessing on the 108 weed species covered.
  • Improving fine-grained visual knowledge—especially for weeds, pests, and subtle disease symptoms—is a concrete next target for agricultural VLM development, since over half of errors are traced to missing knowledge rather than perception or reasoning.
  • Because closed-source models clearly outperform open-source ones on average, model scale and web-scale training appear to matter for agricultural expertise; progress in open-source agricultural AI can be measured against the closed-source scores in AgroBench.
  • The five-option multiple-choice format and per-task score averaging make AgroBench a stable, reproducible yardstick for comparing future models, including both identification and management-related tasks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The near-chance human scores on weed and disease identification in the paper's own validation raise the possibility that many items are extremely difficult even for trained agronomists, so a directly usable AgriBench variant might need to distinguish 'model lacks knowledge' from 'question is borderline unanswerable' before being used to certify field-ready AI.
  • A natural next experiment is to measure how much accuracy improves when a model is given access to a weed or plant encyclopedia as retrieval context; since most errors are classified as lack of knowledge, retrieval-augmented answering is a testable extension of the paper's diagnosis.
  • If the benchmark were expanded with per-question expert confidence ratings or multiple independent labelers, it could be used to quantify label noise and produce a difficulty-calibrated subset for tracking VLM progress on truly diagnostic items.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. AgroBench introduces a seven-task, five-option multiple-choice benchmark for evaluating vision-language models (VLMs) in agriculture, comprising 4,342 QA pairs over crop/disease/pest/weed identification and crop/disease management, machine usage, and traditional methods. The authors evaluate four closed-source and eight open-source VLMs in a zero-shot setting, report a human validation on a subset, and conclude that closed-source models outperform open-source ones and that fine-grained identification — especially weed identification (WID) — remains difficult, with open-source VLMs performing close to random. The dataset and evaluation code are released, and an error analysis attributes most failures to lack of knowledge and perceptual errors.

Significance. If the benchmark's labels and images are valid, AgroBench is a useful evaluation resource: it is broader in task coverage and category counts than prior synthetic agricultural VLM benchmarks, and the human-validation attempt and error taxonomy are valuable. The claim that current VLMs lack fine-grained agricultural knowledge, particularly for weed identification, is plausible and practically important. However, the validity of the ground-truth labels for the fine-grained identification tasks is not established, and the human validation itself is at odds with the central claim: agriculture-degree participants scored at or near chance on WID (20.0%) and DID (25.0%). The reported random baseline is also miscalibrated, with several task baselines below the 20% chance level. These issues mean the headline conclusion currently rests on an unverified yardstick. The paper's strengths — broad task design, expert-authored management questions, and model/error analysis — could survive a revision that establishes label quality and recalibrates the baselines.

major comments (4)
  1. [Table 2; §3.2, §4.1] The human validation does not establish that the fine-grained identification labels are usable. Table 2 reports human accuracy of 20.0% on WID and 25.0% on DID for participants with at least a bachelor's degree in agriculture, while the five-option chance level is 20%. With human experts at or near chance, the possibility that many WID/DID items are ambiguous or that the distractor sets are too confusable cannot be dismissed. The central claim in the abstract and §4.2 that VLMs perform close to random in weed identification and that this reflects a lack of fine-grained VLM knowledge therefore requires an expert label-ability study (for example, independent expert annotation with per-item agreement, or a larger human evaluation) to rule out the alternative that the benchmark is measuring label uncertainty rather than model competence. The current text does not report inter-annotator agreement, and each item was answered by only two raters.
  2. [Table 2; §4.2] The Random Choice baseline is miscalibrated. For five-option questions the expected accuracy is exactly 20%, yet the reported Random Choice values range from 15.64% to 22.11%, including DMN 15.64%, WID 17.90%, CMN 16.06%, and TM 19.31%. These sub-20% values are used to support the 'close to random' conclusion for open-source VLMs on WID. A proper baseline should be 20% with sampling variability reported (e.g., binomial confidence intervals or a multi-seed Monte Carlo average). As reported, the comparison is not statistically grounded; for instance, EMU2Chat's 23.81% WID accuracy is numerically close to 20%, but the paper provides no error bars to establish what 'close' means relative to the 55.17% of Gemini 1.5-Pro.
  3. [§4.1, Table 2 caption, §4.2] The human validation protocol is described inconsistently, and the human–model comparison is not matched on the same items. Section 4.1 states that 28 students answered 20 questions each, yielding 280 questions with two responses per question (560 responses), while the Table 2 caption says validation was on 80 samples per task, which would be 560 samples for seven tasks. More importantly, human scores are computed on this small subset, whereas all model scores are computed on the full test sets (e.g., DID has 1,502 QA pairs); the §4.2 statement that 'closed-source VLMs achieve better results than humans' is therefore not a like-for-like comparison. The authors should either evaluate models on the identical human subset or collect human responses on a matched sample of the full set.
  4. [Abstract; §3.1; §3.2; Figure 1; Figures 8–13] The dataset is described as covering '682 disease categories' in the abstract and Figure 1, but §3.1 states that DID provides 682 crop-disease combinations covering 370 disease categories. Appendix Figures 8–13 plot 'Plant-Disease Pair' counts, confirming that 682 is the number of crop-disease pairs, not distinct diseases. This overstates the category coverage and should be corrected throughout. In addition, §3.2 states that WID images and labels are inherited from existing datasets [11,23,24,42] with code that crops images and assigns bounding boxes; no expert re-annotation of weed species is described. Since WID is the headline task on which open VLMs are 'close to random', the provenance of its labels is directly relevant to the main conclusion and must be clarified, and ideally validated by expert re-annotation.
minor comments (4)
  1. [Table 3 caption] The caption says text-only performance is 'close to random', but the reported text-only overall accuracies are 29.71% (GPT-4o) and 37.49% (LLaVA-Next-8B) versus the random baseline of 19.03%; this phrasing is misleading and should be revised.
  2. [References [6] and [7]] References [6] and [7] list the same paper ('Are we on the right way for evaluating large vision-language models?') and should be merged or one removed.
  3. [Figure 5 caption] There is a typo in the Figure 5 caption: 'T wo-shot' should be 'Two-shot'.
  4. [Figure 6] The x-axis label 'T otal' contains an erroneous space and should be 'Total'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: AgroBench is an empirical evaluation with no fitted inputs, and its central claims do not reduce to any self-citation or definitional identity.

full rationale

The paper's central claim — that open-source VLMs score near random on weed identification and that error analysis attributes most failures to lack of knowledge — rests on measuring fixed models on expert-labeled QA pairs. There is no derivation chain in the sense the circularity audit targets: no parameters are fitted, no quantity is defined in terms of another and then 'predicted', no uniqueness theorem is invoked, and no prior work by the authors carries a load-bearing argument. The only caveat that could undermine the headline is external validity of labels: Section 3.2 reports a single annotator (a Ph.D. in Agriculture) with review by other agricultural degree holders, and Table 2 shows agriculture-degree participants scoring only 20.0% on WID and 25.0% on DID. That is a validity concern about whether the images carry enough evidence or whether the five-option format is fair, not circularity: the model scores are not generated from, or constrained by, those human scores. The text-only ablation (Table 3) shows that removing images drops performance substantially, which is an independent check on task design rather than a tautology. Weed identification labels are inherited from external datasets [11,23,24,42], which raises provenance and reproducibility questions but creates no circular loop: external labels are inputs, not outputs of the paper's evaluation. No prediction here reduces to its own input by construction, so a non-finding (score 0) is the honest result.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters, invented entities, or fitted values. The benchmark rests on domain assumptions about label quality, image representativeness, and the validity of the multiple-choice format. These are typical for dataset papers but should be documented with inter-annotator agreement and full data release.

assumptions (3)
  • domain assumption Ground-truth labels in AgroBench are correct and unambiguous as annotated by the author agronomist and reviewed by others.
    The benchmark's validity depends on the accuracy of expert annotations. Section 3.2 describes the annotator as one of the authors with a PhD in Agriculture, with QA reviewed by other degree-holders. No inter-annotator agreement is reported, and human participants score near random on DID and WID (Table 2), raising ambiguity concerns.
  • domain assumption Images are representative of real-world farm conditions and unambiguous for the target labels.
    Section 3.2 states images were curated from licensed sources, prioritizing real farm settings, and unclear images removed. For WID, images come from existing datasets [11,23,24,42], which may have different quality. If many images are ambiguous, the difficulty scores reflect image ambiguity rather than model capability.
  • domain assumption The multiple-choice QA format with five options measures VLM agricultural knowledge.
    The paper uses exact matching of five options for evaluation. The format may allow models to exploit priors (e.g., common management strategies), as acknowledged in Section 4.3.

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Cite this review

Pith. "Pith review of AgroBench: Vision-Language Model Benchmark in Agriculture." pith.science (2026). https://pith.science/paper/MEG7LJEH

@misc{pith2026250720519,
  author       = {Pith},
  title        = {Pith review of: AgroBench: Vision-Language Model Benchmark in Agriculture},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MEG7LJEH}},
  note         = {Machine review of arXiv:2507.20519}
}
read the original abstract

Precise automated understanding of agricultural tasks such as disease identification is essential for sustainable crop production. Recent advances in vision-language models (VLMs) are expected to further expand the range of agricultural tasks by facilitating human-model interaction through easy, text-based communication. Here, we introduce AgroBench (Agronomist AI Benchmark), a benchmark for evaluating VLM models across seven agricultural topics, covering key areas in agricultural engineering and relevant to real-world farming. Unlike recent agricultural VLM benchmarks, AgroBench is annotated by expert agronomists. Our AgroBench covers a state-of-the-art range of categories, including 203 crop categories and 682 disease categories, to thoroughly evaluate VLM capabilities. In our evaluation on AgroBench, we reveal that VLMs have room for improvement in fine-grained identification tasks. Notably, in weed identification, most open-source VLMs perform close to random. With our wide range of topics and expert-annotated categories, we analyze the types of errors made by VLMs and suggest potential pathways for future VLM development. Our dataset and code are available at https://dahlian00.github.io/AgroBenchPage/ .

Figures

Figures reproduced from arXiv: 2507.20519 by the authors.

Figure 1
Figure 1. We present AgroBench (Agronomist AI Benchmark) designed to comprehensively evaluate 682 disease categories across 203 agricultural crop types for 7 vision-language question-answer tasks. In the era of larger-scale vision-language models (VLMs), our AgroBench is obviously non-trivial in terms of many more crop and disease categories with all expert annotations for establishing QA benchmarks in the agricultural domain… view at source ↗
Figure 2
Figure 2. Examples of labeled images for DID, PID, and WID tasks. Our dataset includes 682 crop-disease pairs, 134 pest categories, and 108 weed categories. We prioritized collecting images from real farm settings. DID DMN PID WID CMN MQA TM Fungs Bacteria Others Fungs Bacteria Others Lepidoptera Hemiptera Others Coleoptera Asterales Others Poales Caryophyllales Vegetable Fruit Others Harvester Tillage Others Others Tool Proc… view at source ↗
Figure 3
Figure 3. Seven benchmark tasks in AgroBench. AgroBench includes multiple topics with a diverse range of categories. The total accuracy is calculated by the average of each task to mitigate the difference in QAs. minimizes harm to the environment. Our benchmark pro￾vides 544 labeled images that cover 134 pest categories, including insects, mites, and other organisms harmful to plants. For categories where we obtain multiple i… view at source ↗
Figures from the paper (37 more)
Figure 4
Figure 4. Figure 4: Examples of QA pairs for CMN and DMN tasks. (a) and (b) Crop management QA types for white asparagus and asparagus, respectively. Their difference in the harvest timing affects the answer’s difference correctly. (c) and (d) Disease management QA types for the alfalfa b…
Figure 5
Figure 5. Figure 5: Results of seven benchmark tasks with Chain of Thought (CoT). Baseline indicates results without CoT. In the one-shot, two-shot, and three-shot settings, we provide one, two, and three CoT examples per task, respectively, to guide the model. DID DMN PID WID CMN MQA TM …
Figure 6
Figure 6. Figure 6: Error analysis on seven benchmark tasks with GPT-4o. We extract a maximum of 15 errors per task from the zero-shot CoT result. We manually analyze how they conclude the incorrect answer. tiple answers, we consider it incorrect. 4.2. Main Results Here, we discuss the ma…
Figure 7
Figure 7. Figure 7: Error examples of GPT-4o. Examples illustrate the two main error types: Lack of Knowledge and Perceptual Error. provide a single CoT reasoning example along with an im￾age and the corresponding prompt for each task to guide the model in answering the question. We selec…
Figure 8
Figure 8. Figure 8: Disease Categories Counts (A). 14 [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Disease Categories Counts (B). 15 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Disease Categories Counts (C). 16 [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: Disease Categories Counts (D). 17 [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Disease Categories Counts (E). 18 [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: Disease Categories Counts (F). 19 [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]
Figure 14
Figure 14. Figure 14: Pest Counts. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_14.png]
Figure 15
Figure 15. Figure 15: Weed Counts. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_15.png]
Figure 16
Figure 16. Figure 16: Disease Identification Task (DID) Example (A). Question What disease is affecting the plant? Options A. Cedar apple rust B. Apple proliferation C. Blister spot D. Apple scab E. Flyspeck Plant Apple Answer D. Apple scab Disease Apple scab Cause Fungus [PITH_FULL_IMAGE…
Figure 17
Figure 17. Figure 17: Disease Identification Task (DID) Example (B). 22 [PITH_FULL_IMAGE:figures/full_fig_p022_17.png]
Figure 18
Figure 18. Figure 18: Disease Management Task (DMN) Example (A). Question What planting technique helps avoid the introduction of the disease shown in the image? Options A. Harvest cuttings from plants showing early wilting symptoms. B. Use cuttings taken only from the upper part of the pl…
Figure 19
Figure 19. Figure 19: Disease Management Task (DMN) Example (B). 23 [PITH_FULL_IMAGE:figures/full_fig_p023_19.png]
Figure 20
Figure 20. Figure 20: Pest Identification Task (PID) Example (A). Question What pest is affecting the plant? Options A. Cocoa Weevil B. Squash Vine Borer C. Cassava Green Mite D. Cocoa Mealybugs E. Cocoa Mirid Species Cocoa Mirid Answer E. Cocoa Mirid Order Hemiptera [PITH_FULL_IMAGE:figu…
Figure 21
Figure 21. Figure 21: Pest Identification Task (PID) Example (B). 24 [PITH_FULL_IMAGE:figures/full_fig_p024_21.png]
Figure 22
Figure 22. Figure 22: Weed Identification Task (WID) Example (A). Question What species of weed does the highlighted bounding box represent in the image? Options A. Polygonum aviculare (Prostrate Knotweed) B. Common dandelion (Taraxacum officinale) C. Apera spica-venti (Loose Silky-bent) D…
Figure 23
Figure 23. Figure 23: Weed Identification Task (WID) Example (B). 25 [PITH_FULL_IMAGE:figures/full_fig_p025_23.png]
Figure 24
Figure 24. Figure 24: Crop Management Task (CMN) Example (A). Question What soil conditions are ideal for the plant shown in the image to grow well? Options A. Clay soil with pH 5.1–6.3 B. Neutral soil with pH 6.5–7.1 C. Alkaline soil with pH 7.5–8.1 D. Highly acidic soil with pH 4.5–5.8 E…
Figure 25
Figure 25. Figure 25 [PITH_FULL_IMAGE:figures/full_fig_p026_25.png]
Figure 26
Figure 26. Figure 26: Machine Usage QA Task (MQA) Example (A). Question What is the purpose of the equipment shown in the image? Options A. To add a fertilizer spreader for crop growth B. To attach additional equipment for planting C. To increase the tilling depth in hard soil D. To adjust…
Figure 27
Figure 27. Figure 27: Machine Usage QA Task (MQA) Example (B). 27 [PITH_FULL_IMAGE:figures/full_fig_p027_27.png]
Figure 28
Figure 28. Figure 28: Traditional Methods (TM) Example (A). Question What is the agricultural method shown in the picture? Options A. Irrigation canals B. Terrace farming C. Chinampa D. Slash-and-burn agriculture E. Hydroponics Reasoning The chinampa method involves creating small, rectang…
Figure 29
Figure 29. Figure 29: Traditional Methods QA (TM) Example (B). 28 [PITH_FULL_IMAGE:figures/full_fig_p028_29.png]
Figure 30
Figure 30. Figure 30: Example case of GPT-4o answering without an input image (DMN). Even when the model cannot determine the crop or disease based on the text, it guesses the answer. Question When is the crop shown in the image ready for harvest in the home garden? Options A. As soon as t…
Figure 31
Figure 31. Figure 31: Example case of GPT-4o answering without an input image (CMN). Even when the model cannot determine the crop based on the text, it guesses the answer. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_31.png]
Figure 32
Figure 32. Figure 32: Example case of zero-shot CoT success. The model identifies the pest step by step by observing the image’s appearance, checks all the options, and concludes with the correct answer. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_32.png]
Figure 33
Figure 33. Figure 33: Example of one-shot CoT failure. The model focuses on determining the plant disease species but fails to observe the severity of the disease, leading to an incorrect conclusion. 32 [PITH_FULL_IMAGE:figures/full_fig_p032_33.png]
Figure 34
Figure 34. Figure 34: Example of Lack of knowledge Error (CMN). The model successfully determines that the plant is Rutabagas. However, the model does not know the optimal timing for planting the seeds. 33 [PITH_FULL_IMAGE:figures/full_fig_p033_34.png]
Figure 35
Figure 35. Figure 35: Example of Perceptual Error (DID). The image shows the bacterial leaf streak and bulb rot, and the correct answer is B. The image does not show purple lesions. The affected areas are yellow and brown. 34 [PITH_FULL_IMAGE:figures/full_fig_p034_35.png]
Figure 36
Figure 36. Figure 36: Example of Perceptual Error (DID). The image shows the Common wild oat (Avena fatua), and the correct answer is B. The model successfully guesses that B and D are close to the image; however, it concludes that D is correct without further observation or reasoning. 35 …
Figure 37
Figure 37. Figure 37: Example of Reasoning Error (DID). Successfully reasoning A and C leads to dark areas, but by ignoring the reasoning, the model concludes that C is the correct answer.) 36 [PITH_FULL_IMAGE:figures/full_fig_p036_37.png]
Figure 38
Figure 38. Figure 38: Example of Double Answer Error (MQA). The image shows the border coating machine with the correct answer: C. However, the model stops thinking and provides both C and D as answers, even though it should continue reasoning to determine a single correct answer. 37 [PIT…
Figure 39
Figure 39. Figure 39: Example of Interpretation Misunderstanding (PID). The image shows the Water Fern Weevil, and the correct answer is D. However, even though the image was taken in a lab environment, the model considers this option incorrect because the background is not water. 38 [PIT…
Figure 40
Figure 40. Figure 40: Example of Reject to Answer (DMN). The image shows the blood disease of the banana, and the correct answer is A. However, the model concludes that there is no answer and stops reasoning instead of reconsidering. 39 [PITH_FULL_IMAGE:figures/full_fig_p039_40.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.