Translated from the original article published in French by The Conversation
Agriculture has a heavy responsibility to feed billions of people under difficult conditions: the demography is growing, the climate is becoming less and less favourable, water resources are drying up, and soils are deteriorating. Despite immense progress, it leaves too many empty bellies while overpressuring nature.
To reform this untenable situation, many are encouraging a shift towards local, organic and peasant agriculture. Similarly, since the beginning of the pandemic, policies have been deployed everywhere to increase food self-sufficiency.
Three years ago or so, I wanted to update my workflow to model crop nutrition, identify plant needs and provide an idea of fertilizer dosage. I wrote a blog post to help me put my ideas into place, and why not help others by doing so. I forgot about that post and left it unpublished. I found it back by chance while scanning my profile on NextJournal. Machine learning have evolved tremendously since then, but I think it is still valuable.
In the notebook, I explain how to obtain a plant nutrition recommendation system based on local soil and weather conditions.
There are many tutorials online on how to perform a PCA in Python, but few on how to generate a biplot with conventional scalings. So I did one myself on a Nextjournal notebook. Enjoy!
Get out your tie-dye t-shirt, your patchouli perfume and your incense, then press play to welcome the revelation of the cosmic skull in the form of a large glass dome confining a rainbow in Technicolor.
The computing platform Noteable recently created a plugin that allows interaction with ChatGPT. I opened a notebook, then dropped a dataset. I asked ChatGPT to generate the code in #Rstats to create an AI model on this data set. 30 seconds later, I had an optimized code. It's sick. It completely (and positively) upsets the way of teaching numerical computations. Rather than focusing on writing code, teaching should now focus more on the importance
to ask the right questions and
to understand the meaning of the answers
In a survey of 791 articles in 5 scientific journals, about half of the articles misinterpreted the notion of p-value, yet a gold standard in science (for the wrong reasons). We will be able to really focus on training critical thinking and put aside the ability to type code!
Music is not solely inspirational. Composers use frameworks. Some use western music theory, and any of their respective numerous genre and forms, which comes with their own rich history, culture, rules and patterns. This notebook shows how musical rules and patterns can be extracted from particular mathematical objects called cellular automata. This concept is not new. Genius composer Iannis Xenakis might have been the first to popularize (or even to use) them in music with his orchestral work Horos in 1986 (Solomos, 2013). This article applies a rather simple method to compose nusic with cellular automata, and can be summarized in three steps.
Code is poetry, of course, but it is also music. The music21 Python package for computer musicology not only allows investigating musical scores with data science, but also to write your own scores with mathematical operations. Georges Dimitrov, composer and professor at Concordia University (Montreal, Canada), created the arvo package, which includes well-known minimalistic mathematical operations on notes to create no less rich musical scores. In this notebook, I explain how to compose the piece just below with arvo and music21 with minimalistic procedural composition techniques. You will need to have intermediate understanding of the Python programming language and basic understanding of western music theory.
The paper (Viana et al., 2022), of which Maxime Paré is co-author, compares the production of organic and conventional oats with a life cycle assessment.
The finite element method (FEM) is widely used in engineering to represent mechanisms in space and time. Its mathematical basis is pretty complicated, but some Python packages facilitate problem definition and solving. But don't think FEM is easy even with the simplest FEM package : deep learning is popcorn compared to FEM.
Eco-traditionalism. (C) essicolo