http://helminen.co/plant-disease
It could work for a subset of plants, or potentially with a much larger training set - but I think NIR spectral/hyperspectral imaging would be the way forward here with more differentiating data points.
I get the impression that you tried to train a single classifier to diagnose disease in any species in the PlantVillage database.
You might get better results by training a separate classifier for each plant species (or starting with just one species, such as tomato, for which PlantVillage has 10 disease categories). A farmer knows what crop they're growing, so can select the correct classifier when they submit a photo.
Just knowing what questions to ask, what a bight could be would be very helpful to us plebes.
Second, is probably a Camellia japonica... but could be also a Camellia x williamsii. And you need to know that there is a Camellia sazanqua also. A trained human can spot the "too big leaves for sazanqua" in miliseconds (or too much shiny, or suspiciously blue, or photoshopsly faked, etc) but this is not so easy for a program. Search image will not spot the differences and just let you with the most common option.
250,000 images doesn't sound like a large enough training set to be effective on anything but the most common plants.
So even with 5k samples, the 250k image corpus would only have 50 species, using this rule of thumb. A good engineer could pick up DL and build a system that performs to this standard, because the tricks are all written down in the literature.
If they do better, they either exploit unpublished methods, or researched those methods themselves, with their researchers.
We also gave the image regocnition path a thought but it seemed to be quite a tall order. Hopefully they come up with a novel approach on this!
I've looked for an app like that for years, seems yours is, thanks!
It's made by french researchers I think. It doesn't work perfectly but I did identify a lot of plants I don't know with it.
You can snap multiple pictures of the same plant, for example, 2 pictures of the leaves, 1 of a flower and 1 of the bark, and then use the combination to search. You can also submit your observations to have them identified by experts.
Their problems they mention seem to be quite standard for image recognition (scale- and perspective variance), however I could imagine that this could be quite disastrous for plant recognition, given the massive diversity of plants: e.g. a leaf viewed from the side could equally well be a more narrow leaf from a different species.
As they write on [1]: "Our challenge comes from adapting our image recognition platform to recognize different shapes and sizes of the same plants, flowers and trees. We know this is possible, and we are close to cracking it."
[1] https://www.indiegogo.com/projects/plantsnap-identify-plants...
I can immediately think of all kinds of challenges that are really hard to overcome: diseased leaves, different seasons, plus all the usual glare/shading/background issues.
I'd guess there'd be a focus on salient botantical features for classification, and perhaps the human can be enlisted to circle them out. There could be a "twenty questions"-type narrowing down, perhaps using images.