computer science

Kindwise

Kindwise is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Kindwise rather than just read about it. In short: FlowerChecker, also known as Kindwise, is a company that uses machine learning to identify natural objects from images. This includes plants and their diseases, but also insects and mushrooms.

Key takeaways

Kindwise belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Kindwise to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Kindwise from memory before moving on to harder problems.

Reference excerpt

FlowerChecker, also known as Kindwise, is a company that uses machine learning to identify natural objects from images. This includes plants and their diseases, but also insects and mushrooms. It is based in Brno, Czech Republic. It was founded in 2014 by Ondřej Veselý, Jiří Řihák, and Ondřej Vild, at the time Ph.D. students.

Features & Tools FlowerChecker offers multiple products. Plant.id is a machine learning-based plant identification API launched in 2018, with the plant disease identification API, plant.health, released in April 2022. The plant.id API is suitable for integration into other software, such as mobile apps or urban trees from remote-sensing imagery. Other products include insect.id, mushroom.id and crop.health are machine learning-based identification APIs for the identification of insects, fungi and economically important plants, respectively, and include also online public demos. The FlowerChecker app was discontinued in October 2024 after 10 years of successful operation. In 2025, the company, together with the Department of Forest Ecology at the Research Institute for Landscape (VÚK), began developing Forestum.ai, a tool for automated forest inventory and growth prediction. The two-year project (2025–2027) is funded by the European Union through the Czech Ministry of Industry and Trade under the OP TAK programme. Using remote sensing methods, the system combines UAV photogrammetry, terrestrial and aerial LiDAR scanning, and machine learning to evaluate forest conditions down to the level of individual trees, aiming to replace time-consuming manual field surveys. It is designed as a set of seven modules covering tasks such as automatic timber-volume estimation, individual tree detection, tree recognition to genus level, and crown analysis; one module is also intended to estimate timber volume from ordinary RGB drone imagery without LiDAR, making the technology more accessible to smaller forest owners. A prediction module simulates the annual development of forest stands up to the year 2100 under various climate scenarios.

Recognition In 2019, FlowerChecker won the Idea of the Year award in the AI Awards organized by the Confederation of Industry of the Czech Republic. In 2020, an academic study comparing ten free automated image recognition apps showed that plant.id's performance excelled in most of the parameters studied. In an independent study comparing different image-based species recognition models and their suitability for recognizing invasive alien species, the plant.id achieved the highest accuracy compared to other tools. In a subsequent study, plant.id was utilized to evaluate urban forest biodiversity using remote-sensing imagery, achieving the highest accuracy in tree species identification among compared methods. The technology has also been referenced as an example of practical integration of AI-based plant identification into cross-platform precision agriculture systems.

Research activities In 2022, the company entered a consortium to develop a weeder capable of in-row weed detection and removal. The project (code FW06010647, 2023–2026), led by the machinery company SMS CZ, s.r.o., develops a robotic hoeing device that uses camera systems and neural-network image analysis to recognise crops in real time and mechanically remove weeds, reducing herbicide use; its total eligible costs are CZK 23.55 million, of which CZK 16.09 million is provided by the Technology Agency of the Czech Republic under the TREND programme. In 2025, Kindwise was reported as the main grant recipient in a Technology Agency of the Czech Republic-supported project focused on the detection and removal of invasive plant species. The project, initiated by the conservation organization Česká krajina, uses drones to capture high-resolution imagery of protected sites and trains neural-network models to recognize invasive plants such as lupine, goldenrod and boxelder maple. The system is intended to send detected locations to a ground robot, developed by researchers at the Czech University of Life Sciences Prague, for mechanical removal of the plants while also recognizing protected species to avoid damaging them.

References

Worked examples

Example 1 — a first encounter with Kindwise

Start with the simplest possible case. Write down what Kindwise claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Kindwise before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Kindwise ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Kindwise

In research: Kindwise appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Kindwise in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Kindwise is common in secondary-school and first-year university syllabi. It links to neighbouring topics Android (operating system) software, Applied machine learning, Czech companies established in 2014, so understanding it makes those chapters shorter.
In everyday life: Look for Kindwise outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Kindwise in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Kindwise means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Kindwise out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Kindwise in simple terms?

FlowerChecker, also known as Kindwise, is a company that uses machine learning to identify natural objects from images. This includes plants and their diseases, but also insects and mushrooms.

Why does Kindwise matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Kindwise?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Kindwise.