Lessons learned while designing a multimodal benchmark for agricultural decision support

I have been reflecting on designing evaluation frameworks for multimodal AI in agriculture: in particular, what it takes to assess whether vision–language models can reliably support agronomic decisions.

Why benchmarks in this domain are hard. General-purpose multimodal benchmarks focus on captioning, visual QA, or perceptual reasoning. Agricultural use cases instead demand domain-specific criteria: expert knowledge (plant pathology, soil science, irrigation, pest management), visual recognition across growth stages and species, and procedural reasoning (e.g. planning and troubleshooting farming workflows). Combining these in a single evaluation is non-trivial: you need tasks that are grounded in real agronomic practice, yet scorable in a consistent way.

Evaluation is the bottleneck. Open-ended questions in specialized domains do not fit classic NLP metrics. You need explainable, task-aware scoring: e.g. judging correctness, specificity, and conciseness against expert references, and for procedural tasks, accuracy and logical flow of steps. Relying on a single automated metric often hides systematic failure modes: for instance, models may perform well on factual agricultural knowledge but misattribute symptoms to the wrong causes when visual and causal reasoning must be combined, or confuse etiology (e.g. fungal vs. bacterial vs. abiotic) in diagnostic scenarios. Designing rubrics that surface these gaps without leaking proprietary data or methodology is itself a design challenge.

Takeaways. (1) Taxonomic and procedural coverage matter. Coarse categories or small class sets miss the diversity needed for real-world diagnostics; including procedural and “next-step” style tasks better reflects how advisors and farmers reason. (2) Multiple views can help. In identification tasks, providing several organ or context views (rather than a single image) can improve consistency; a lesson that transfers to how we might design interfaces and data pipelines. (3) Benchmarks are as much about failure modes as about scores. The most useful outcome is often a clearer map of where models fail (e.g. cross-domain integration of visual cues and causal knowledge) rather than a single aggregate number.

This is a short, high-level note on conceptual lessons: no code, no datasets, no metrics or architectures. I hope it is useful to anyone thinking about how to evaluate and improve multimodal AI for agriculture.