Shadowing Practice: Controlled Multi-modal Image Generation for Plant Growth Modeling - Learn English Speaking with Video

Les maken...
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Hello my name is Libana Roscher
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and I want to present our paper Controlled multimodal image generation for plant growth modeling
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which was presented at the international conference on pattern recognition last summer.
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So when we have a look at such an aquaculture field
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and we work with robotics and with machine learning methods for phenotyping
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we can ask ourselves different questions
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so for example we can ask what is on the field
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which is a typical classification task
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or we can ask what is the yield in a specific area
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which is a typical regression task
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or we can ask ourselves what will a plant look like in the future
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and this is a forecasting tasks
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and this is exactly the question we addressed in our paper
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so imagine the following scenario you already know that
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when plants grow closely together this can be very beneficial
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so for example it can increase the biomass the crucial question is now what is actually a good mixture
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and so what you can do is you can test different mixtures then you can collect data
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and then you can learn a model from this information
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and this model relates the information from what we have measured to a parameter like the biomass
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so you have a relationship between the mixtures and the biomass
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and then the good thing is this model can be applied to new mixtures
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and these new mixtures were not tested yet in the field before
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so you get an idea what potential good mixtures are
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And one problem is now that one might be interested in the biomass
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and the other might be interested in the spread of diseases.
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So it would be good to combine this and to get complete information.
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And this can be done if you model the whole appearance with plants, that means if you generate images.
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And with this you are flexible in what you do with the result
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because it can be treated such as an artificial sensor measurement.
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So in order to generate such images we used
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so -called generative adversarial networks illustrated here and this is a neural network consisting of two parts.
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So one part is the generator G and this generator is responsible to generate images.
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The other part is the discriminator, and the discriminator helps to train the generator to generate really good images.
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The discriminator does it by trying to distinguish between real images and images which were generated by the generator.
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So in the best case, the generator generates such good images
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that the discriminator can no longer distinguish between a real image
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and an image which was generated but there's one crucial thing
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which is important for our application
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so we do not want only want to generate an arbitrary
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nice -looking image we want to condition on specific factors on specific things
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and one important thing is we want to condition on the appearance of an of an additional input image
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which is from an earlier point in time
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and the second important thing is we want to condition on
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so -called factors of variation and these factors of variation are factors
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which influence the appearance of the of the plants
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and the novelty of our paper is exactly this part
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because on the one hand we learned the representation
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which is useful for the machine learning model but on the other side it was
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Because we can also easily and intuitively change these factors of variation so that we can produce different scenarios, different outcomes.
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And so here you can see results of
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which we presented in our paper where we generated images with chain factors of variation.
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So our dataset contains UAV images, which were captures in Kampus -Klein -Altendorf,
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where we have different mixtures of spring wheat and faba bean.
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And we also collected different factors of variation, which belong to these mixtures.
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And they are illustrated in green and light blue and in dark blue.
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And on the left, you can see the input image, which is from an early growth stage.
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And this is the real scene from a later growth stage and the measured factors of variation.
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And these images are all generated images.
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So here we change the factors of variation.
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So for example, on the very right image, you can see we set all factors of variation for Faba bean to zero.
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And actually there are no Faba bean plants in the generated image.
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So we can clearly control and manipulate how the images can be generated it
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and from these images we can now derive specific parameters pointing
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us to potential promising mixtures we can later test in the
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field with this thank you very much for listening you can find the paper by scanning the qr code
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and if you are more interested in this research direction you can go to our website
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or you can have a look in my YouTube channel.
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Thanks a lot!

Why Practice Speaking with This Video?

If you’re into language learning and want to level up your speaking skills, this video is a goldmine. It’s packed with clear, academic yet conversational English—perfect for practicing how to explain complex ideas (like plant growth modeling!) in a way that’s easy to follow. The speaker’s pace is steady, making it ideal for the shadowing technique: just pause, repeat, and mimic her tone and rhythm. You’ll not only boost your vocabulary (hello, “generative adversarial networks”!) but also learn to structure arguments smoothly—skills that shine in both professional and casual settings.

Grammar & Expressions in Context

Let’s break down 3 key structures to copy:

  • “So when we have a look at… we can ask ourselves…”: This phrase is a pro move for introducing topics. Use it to transition from observation to questions (e.g., “So when we look at a new recipe, we can ask ourselves: What if we swap sugar for honey?”).
  • “The crucial question is now what…”: Great for highlighting importance. Try it in debates or explanations: “The crucial question is now how we can make this project more sustainable.”
  • “And this can be done if you…”: A simple way to propose solutions. Practice with: “And this can be done if you start planning a week in advance.”

Common Pronunciation Traps

Watch out for these tricky words during pronunciation practice:

  • “Phenotyping”: It’s “fee - no - type - ing,” not “fen - o.” Stress the second syllable to sound natural.
  • “Discriminator”: Break it down: “dis - crim - in - ay - tor.” Don’t rush the “ay” sound at the end.
  • “Multimodal”: Pronounce it “mul - ti - mo - dal,” with equal stress on each part. Avoid slurring the “ti” into “shi.”

Repeat these words slowly, then try them in full sentences. You’ll nail them in no time!

Wat is de Shadowing-techniek?

Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.