The iNaturalist community itself is fascinating and quite transparent with detailed site stats ( ) and a clear mission statement ( ): On functionality alone it's extremely rare for Seek not to recognize an organism (yeah, it's not just plants) and when it is, I simply send the observation to iNaturalist whose network of naturalists both amateur and professional usually manage to definitively identify my observations within a few hours. The former is a Rails app, and the latter a React Native app with no account registration system making it safe and legal to use for children since all observations are stored in-device by default unless you chose to send them to your iNaturalist account to share with the community. Moreover both iNaturalist ( ) and Seek ( ) are open source. INaturalist and their Seek app ( ) is a joint venture between the California Academy of Sciences and the National Geographic Society: Understandably people are realizing that it's now much easier to discover what plants (and animals, and fungus, etc.) exist around you and whether they're special in some way.īut when picking apps to accomplish that task, I suggest selecting for those that respect your privacy (because a lot of these plant observations involve GPS location), are clear about how the machine learning datasets are trained and what's being done with the data you're supplying, and lastly how these apps and companies are funded. This is only marginally useful but could have been so much better if it tried to identify structure of the plant. It relies at you looking at the suggested solution, and then, Hey!, here you have five more, maybe your plant is somewhere on the list? The above algorithm returns completely nonsensical results for my searches, plants that have completely different structure and coloration and nobody would ever mistake them. So now rather than looking at the leaves it uses light direction or photo grain to tell if it is succulent or not? Or maybe different cameras were used to photograph different types of plants? Maybe it has learned to look at the lighting direction because half of the data set was non-suculents with light from the left and half was succulents with light from the right, because it came from a different facility? On the other hand AI will develop its own classification method but one that has unknown faults in it. If you can identify separate features of a leaf and how the leaf grows out of the stem you can basically look it up in a table and tell what kind of tree you are looking at, without need for guessing. This research is conducted using the case study of the limestone mining plant in Poland.AIs are useful for "fuzzy" problems, but are not very good at doing precise things with high reliability.Įvery plant has baked in restrictions on how it grows. The comprehensive description of workspace creation for field surveys using ArcGIS Online (Esri, 2019) and Collector for ArcGIS (Esri, 2019a) is presented in this work. The review includes environmental citizen science apps as a particular example of data collection applications. We briefly present our review result of mobile applications for field mapping as the first global overview within scientific journals. Such software could ameliorate their workflow, accuracy and quality of data, which can help to achieve better research results. The purpose of this paper is to bring further awareness about such technologies to environmental researchers as they could benefit from using dedicated software available for mobile devices. Although the possibility of using mobile devices and dedicated applications for field mapping has an increasing trend, there is very little attention dedicated to them in methodology sections of research works. dedicated Handheld devices such as consumer-grade GPS (Global Positioning System) dedicated for tourism purposes have been substituted by smartphones and tablets with integrated GPS receiver so that field mapping became much easier and more readily available to a broader audience. Among those technologies there are mobile mapping devices and applications which have been gaining audience in recent time. Lately, more and more common digital methods and new technologies have been gaining interest and popularity within the environmental science community. Throughout time, environmental researchers have been using different tools and methods to determine and record location/description of observations with all limitations on their use. Geographical location and object description is an important aspect in field research, regardless of the spatial scale.
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