Section 1

The test: 30 photos, four apps, blind grading

I ran the same 30 houseplant photos through PictureThis (paid), Pl@ntNet (free), Google Lens (free, built into Android and iOS), and iNaturalist Seek (free, by the same team behind iNaturalist) in April 2026. Photos came from a personal collection of 60+ plants, plus 10 deliberately tricky shots — juvenile leaves, hybrids, mislabelled cuttings.

Each app got the photo and nothing else. I logged the first identification it returned, the confidence score where shown, and whether the answer was correct. Categories: 10 common species, 8 tricky look-alikes, 6 cultivars, 4 rare species, and 2 edge cases (damaged or partial photos).

Section 2

Common houseplants — all four pass

Pothos, Monstera deliciosa, snake plant, ZZ, peace lily, fiddle leaf fig, rubber plant, jade, spider plant, and aloe. Under reasonable light, all four apps got these right.

  • ·PictureThis: 10/10. Care recommendations attached and actually correct.
  • ·Pl@ntNet: 10/10. Returns Latin name first; reads more botanical.
  • ·Google Lens: 9/10. Misidentified a Pothos 'N'Joy' as a generic Pothos cultivar — close but imprecise.
  • ·iNaturalist Seek: 9/10. Hesitated on the jade plant (returned at genus level, not species).
Section 3

Tricky look-alikes — Pl@ntNet and PictureThis pull ahead

Pothos vs Philodendron hederaceum, Mini Monstera vs real Monstera, Pilea vs Peperomia, Calathea vs Maranta vs Stromanthe. This is where free vs paid actually matters.

  • ·Pl@ntNet: 7/8. Community-verified image matches handle look-alikes well — people who labelled thousands of pothos correctly are a stronger signal than feature inference.
  • ·PictureThis: 6/8. Confident on 7, wrong on 2. The premium UX hides uncertainty better than Pl@ntNet does.
  • ·Google Lens: 4/8. Defaults to common species — pothos for everything heart-shaped.
  • ·iNaturalist Seek: 5/8. Better on outdoor plants than indoor cultivars; the model isn't trained as heavily on commercial houseplants.
Section 4

Cultivars and variegated forms — everyone struggles

Philodendron 'Birkin' vs 'Pink Princess', Monstera 'Thai Constellation' vs 'Albo Borsigiana', Pothos 'Marble Queen' vs 'N'Joy', Alocasia 'Polly' vs 'Sumo'. The wheels come off here for every app.

  • ·PictureThis: 4/6. The most likely to return a cultivar name (its training data is paid-curated), but confidently wrong on 2.
  • ·Pl@ntNet: 3/6. Often returns the parent species without flagging the cultivar.
  • ·Google Lens: 2/6. Image search routes to the closest visual match, which for cultivars is often the wrong cultivar.
  • ·iNaturalist Seek: 2/6. Cultivars are explicitly out of scope — Seek tends to return at species level for indoor plants.
Section 5

Rare species — community datasets win

Anthurium warocqueanum, Philodendron gloriosum, Alocasia jacklyn, Scindapsus treubii 'Moonlight'. Not rare in collector circles but rare in app training sets.

  • ·Pl@ntNet: 3/4. Collector-submitted photos dominate this tier of the dataset.
  • ·PictureThis: 2/4. Closes the gap on care advice once correctly identified.
  • ·Google Lens: 1/4. Falls back to common species.
  • ·iNaturalist Seek: 1/4. The same Seek dataset that handles wild biodiversity well is thin on tropical houseplant collectors.
Section 6

Edge cases — damaged or partial photos

A brown-spotted Monstera leaf and a freshly-cut pothos cutting (no roots visible). PictureThis correctly identified both and added a diagnostic suggestion (overwatering for the spotted Monstera). Pl@ntNet returned both species correctly without commenting on health. Google Lens identified the spotted leaf as Monstera but the rootless cutting confused it. Seek returned both at genus level, which is honest given the input.

Section 7

Headline scores and the calibration gap

Across 30 photos: Pl@ntNet 24/30 (80%), PictureThis 23/30 (77%), Google Lens 19/30 (63%), iNaturalist Seek 18/30 (60%). Raw accuracy is only half the story.

On calibration — the rate at which an app returned a confident wrong answer with no uncertainty signal — PictureThis was the worst (5 confident wrong), Google Lens 4, Pl@ntNet 3, Seek 2. An app that says 'low confidence' on a wrong answer is more useful than one that 'always sounds right'. Pl@ntNet and Seek score well here because they show match probability with their answers; PictureThis hides this in the paid tier UI.

Section 8

Cost and UX, side by side

Free vs paid is the other axis that matters when picking a daily driver.

  • ·Pl@ntNet: Free, ad-supported. Open-source backend (the project is run by a French research consortium). Best UX for botanists; the species-first display assumes you know what a Latin binomial is.
  • ·PictureThis: 7-day free trial, then ~£30/year on iOS. Best UX for beginners — care advice, watering reminders, and disease diagnosis are all bundled. Aggressive paywall; many users cancel within the trial.
  • ·Google Lens: Free, built into Google Search and most Android phones. Lowest friction (long-press an image anywhere on the phone). Best for one-off identifications, weakest for less-common species.
  • ·iNaturalist Seek: Free, no ads, no account required. Best for native plants outdoors and biodiversity logging. Houseplant accuracy is a side effect, not a target.
Section 9

Which app for which job

The test points to a clear division of labour rather than a single winner.

  • ·Daily driver for houseplants: Pl@ntNet (free, accurate, calibrated). Cross-check tricky cases with PictureThis if you have it.
  • ·One-off identification on the go: Google Lens. It is already on the phone.
  • ·Outdoor or wild plant ID: iNaturalist Seek. Designed for biodiversity, not commercial houseplants.
  • ·Care advice after ID: PictureThis or any chatbot like ChatGPT or Claude — apps are stronger on care synthesis than Pl@ntNet's species-first UX.
  • ·Pet-safety decisions: never one app alone. Cross-reference the ASPCA toxic plants database. See pet-safe houseplants.
  • ·Buying expensive cultivars: photo + Pl@ntNet + a specialist community (r/aroids, dedicated Facebook groups). Apps are not yet reliable enough at the cultivar level for a £200 purchase.
Section 10

The two-app rule

The most reliable approach across all 30 photos was running two apps and trusting the answer only when both agreed. Combined Pl@ntNet + PictureThis hit 27/30 (90%) when both agreed; the disagreements clustered exactly on the cultivars and look-alikes you would expect. For unlabelled plants you are about to buy, propagate, or expose to a pet, the two-app rule turns a 77–80% single-app answer into a 90% combined one.

If Pl@ntNet and PictureThis disagree, photograph the petiole, leaf underside, and growth habit (see the photo identification guide) and cross-check the leaf shape families manually before accepting either answer.