Identify Plants with an App
plantsnap.net
plantsnap.net
That is mostly true. The existence of animals with diplomas[1] is the exception to the rule.
1. https://en.wikipedia.org/wiki/List_of_animals_with_fraudulen...
"An expert in or student of the branch of science concerning living organisms."
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.
Plants in Voynich manuscript aren't real, can't even be classified in a family, but strangely still look familiar to us because they are "frankenplants". You can have exactly the same problem in photos of wild plants. It only takes the leaves of a climber growing over the flowers of other plant, or different flowers and fruits mixed together; and you'll have a new species. A very tricky one to identify. After scratching the head for a while humans can sense that something is wrong... machines normally can't see the problem. An (in)famous case is the photo of two juxtaposed black people arranged casually in a geometry that was tagged by the machine as 'gorilla'.
This is, of course, a critical challenge in data science and is definitely not a trivial one to solve.
I think we have a tendency to treat AI as a silver bullet when we should be treating it as a tool we can use to help augment what we're already doing.
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.
In this respect, as the GP says, image search is currently the best option
It works ok, send pretty beta right now. I pointed my camera at a business card and pressed the home button. Google Assistant appeared and I asked it to tell me about what was on my screen. It noticed one piece of the address, and brought up a listing for that general area, but there was no way to further inquire into any of the rest of the card.
Updated
May 28, 2014
Kinda dead...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.
It's also nice that life naturally fits into nested hierarchies because of evolution. So if you can recognize what family it belongs to, then that narrows down the possible sub families it can belong to. That in turn narrows down the possible clades it can belong to, etc, which narrows down the exact species. You couldn't find a more perfect use case for hierarchical softmax!
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!
I made it as an experiment, results vary and can be awfully wrong but it's funny to see the confusions the neural network can make. Funny until somebody eat a deadly mushroom because of me, I guess. I already put plenty of warnings, so fingers crossed, but it's hard to be sure it won't be misused.
It's a given all guides have false information, it then begs the question are guides worse than no guides? I think this can be easily answered more information is better.
You can give people information but you can't understand it for them.
kudos for making the app :)
What do you think your app would identify this as? Carpet mushroom? http://imgur.com/a/Rqc64
I volunteered at Audubon for a decade. I've got all the books, charts. I hike and camp. I can't identify rocks, birds, fish, trees, clouds, etc to save my life.
Square peg, round hole.
What I'm realizing is that it takes active participation and interest in indentifying things to really learn their names and their histories, not just a passing interest.
The Cornell Lab of Ornithology has a site [1] and an app that is amazing for identifying birds. My favorite feature is how it also lists birds that are similar, which makes it a lot easier to find the specific bird you're looking for. I then compare visual information with the auditory information of the birds calls to make a concrete identification.
I am not sure I even know enough to ask the right questions on this. But what tools, languages, computing resources, etc do you use?
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.
Pl@ntNet only works for the plants of the French metropolitan flora.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...
They may be using camera phone images, but they are well framed images of very distinctive features. Trying to recognize a literal tree in a forest is going to be far harder, and recognizing a tree based on a dead leaf (as a photo towards the bottom suggests) can be very very tricky. Also I have to admit to being concerned that adding "any known plant" won't kill accuracy. The more classification endpoints the less likely you are to get a decent result, and already you have to deal with far greater scale differences than are usual (basically it would be like identifying the breed of dog from anything from a full picture to a photo of a single claw).
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.
https://play.google.com/store/apps/details?id=com.pingou.cha...
https://itunes.apple.com/us/app/champignouf/id1227854971
ps: any improvement suggestion is welcome!
Google Image is really working quite good. I made a lot of test. The only problem is about there's TOS. I can easily write something but I don't want to be flagged because I don't want use there API.
I'm bribable for the job... :)
But on a more serious note, I would appreciate that as well, but it might be harder to take a good picture of a bug by which it is identifiable?
I'm always impressed by these type of apps though and would love making one myself