Tractor Drives Itself, Thanks to ESP32 and Open Source

[Coffeetrac]’s ESP32-based Autosteer controller board, complete with OLD OLED display for debugging and easy status reference.
Modern agricultural equipment has come a long way, embracing all kinds of smart features and electronic controls. While some manufacturers would prefer to be the sole gatekeepers of the access to these advanced features, that hasn’t stopped curious and enterprising folks from working on DIY solutions. One such example is this self-steering tractor demo by [Coffeetrac], which demonstrates having a computer plot and guide a tractor through an optimal coverage pattern.

A few different pieces needed to come together to make this all work. At the heart of it all is [Coffeetrac]’s ESP32-based Autosteer controller, which is the hardware that interfaces to the tractor and allows for steering and reading sensors electronically. AgOpenGPS is the software that reads GPS data, interfaces to the Autosteer controller, and tells equipment what to do; it can be thought of as a mission planner.

[Coffeetrac] put it all together with everything controlled by a tablet mounted in the tractor’s cab. The video is embedded below, complete with a “cockpit view” via webcam right alongside the plotted course and sensor data.

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Vinduino Water-Smart Farming – Now with LoRa!

Our five rounds of Hackaday Prize 2018 challenges have just wrapped up, and we’re looking forward to see where the chips fall in the final ranking. While we’re waiting for the winners to be announced at Hackaday Superconference, it’s fun to take a look back at one of our past winners. Watch [Reinier van der Lee] give the latest updates on his Vinduino project (video also embedded after the break) to a Hackaday Los Angeles meetup earlier this year.

Vinduino started with [Reinier]’s desire to better understand what happens to irrigation water under the surface, measuring soil moisture at different depths. This knowledge informs more efficient use of irrigation water, as we’ve previously covered in more detail. What [Reinier] has been focused on is improving usability of the system by networking the sensors wirelessly versus having to walk up and physically attach a reader unit.

His thought started the same as ours – put them on WiFi! But adding WiFi coverage across his entire vineyard was not going to be cost-effective. After experimenting with various communication schemes, he has settled on LoRa. Designed to trade raw bandwidth for long range with low power requirements, it is a perfect match for a network of soil moisture sensors.

In the video [Reinier] gives an overview of LoRa for those who might be unfamiliar. Followed by results of his experiments integrating LoRa functionality into Vinduino, and ending with a call to action for hackers to help grow the LoRa network. It sounds like he’s become quite the champion for the cause! He’s even giving a hands-on workshop at Supercon where you can build your own LoRa connected sensor. (Get tickets here.)

We’re always happy to see open-source hardware projects like Vinduino succeed, transitioning to a product that solve real world problems. We know there are even more promising ideas out there, which is why Hackaday’s sister company Tindie is funding a Project to Product program to help this year’s winners follow in Vinduino’s footsteps. We look forward to sharing more success stories yet to come.

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MoAgriS: A Modular Agriculture System user [Prof. Fartsparkle] aims to impress us again with MoAgriS, a stripped-down rig for bringing crops indoors and providing them with all they need.

This project is an evolution of their submission to last year’s Hackaday Prize, MoRaLiS — a modular lighting system on rails — integrating modules for light, water, airflow, fertilizer and their appropriate sensors. With an emphasis on low-cost, a trio of metal bars serve as the structure, power and data transmission medium with SAM D11 chips shepherding each plant.

Reinforced, angled PCBs extend rails horizontally allowing the modules to be mounted at separate heights. Light module? Up top. Water sensor? Low on the rails above the pot’s rim. You get the idea. 3D printed clamps attach the rails to the plant’s pot with a touch of paint to keep it from sticking out like a sore thumb among the leaves.

Airflow modules replicate wind currents — the lack of which results in thin, fragile stems — and light modules include a soft white LED to accompany and mitigate the full-spectrum LEDs’ pink neon-like glow. To manage watering the plants, [Prof. Fartsparkle] initially wanted to use one pump to distribute water to every plant, but found some smaller pumps at a low enough price-point to make one per plant viable — and simpler to integrate as a module as well!

If you prefer your gardening to take place outdoors, consider a robot assistant to tackle your weeding.

Norman Borlaug Saves A Billion

Everyone loves a hero. Save someone from a burning building, and you’ll get your fifteen minutes of fame. That’s why I’m always surprised that more people don’t know Norman Borlaug, who would have celebrated his 104th birthday on Sunday. He won the Nobel prize in 1970 and there’s good reason to think that his hacking efforts saved about a billion people from starving to death. A billion people. That’s not just a hero, that’s a superhero.

To understand why that claim is made, you have to go back to the 1970s. The population was growing and was approaching an unprecedented four billion people. Common wisdom was that the Earth couldn’t sustain that many people. Concerns about pollution were rampant and there were many influential thinkers who felt that we would not be able to grow enough food to feed everyone.

Paul Ehrlich, in particular, was a Stanford University biologist who wrote a book “The Population Bomb.” His forecast of hundreds of millions starving to death in the 1970s and 1980s, including 65 million Americans, were taken very seriously. He also predicted doom for India and that England would not exist by the year 2000.

Here we are 40 or 50 years later and while there are hungry people all over the world, there isn’t a global famine of the proportions many people thought was imminent. What happened? People are pretty good problem solvers and Norman Borlaug — along with others — created what’s known as the Green Revolution.

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The Weedinator Returns

We are delighted to see The Weedinator as an entry for the 2018 Hackaday Prize! Innovations in agriculture are great opportunities to build something to improve our world. [TegwynTwmffat]’s Weedinator is an autonomous, electric platform aimed at small farms to take care of cultivating, tilling, and weeding seedbeds. The cost of this kind of labor can push smaller farms out of sustainability if it has to be done by people.

Greater efficiency in agriculture is traditionally all about multiplying the work a single person can do, and usually takes the form or bigger and heavier equipment that can do more at once and in less time. But with an autonomous robotic platform, the robot doesn’t get tired or bored so it doesn’t matter if the smaller platform needs to make multiple passes to cover a field or accomplish a task. In fact, smaller often means more maneuverable, more manageable, and more energy-efficient when it comes to a small farm.

The Original Weedinator was a contender for the 2017 Hackaday Prize and we’re deeply excited to see it return with an updated design and new people joining their team for 2018. Remember, there’s money set aside to help bootstrap promising concepts and all you really need to get started is an idea, an image, and documentation. There’s no better opportunity to dust off that idea and see if it has legs.

Hyperspectral Imaging – Seeing the Unseeable

They say that a picture is worth a thousand words. But what is a picture exactly? One definition would be a perfect reflection of what we see, like one taken with a basic camera. Our view of the natural world is constrained to a bandwidth of 400 to 700 nanometers within the electromagnetic spectrum, so our cameras produce images within this same bandwidth.

Image via Cosmos Magazine.

For example, if I take a picture of a yellow flower with my phone, the image will look just about how I saw it with my own eyes. But what if we could see the flower from a different part of the electromagnetic spectrum? What if we could see less than 400 nm or greater than 700 nm? A bee, like many other insects, can see in the ultraviolet part of the spectrum which occupies the area below 400 nm. This “yellow” flower looks drastically different to us versus a bee.

In this article, we’re going to explore how images can be produced to show spectral information outside of our limited visual capacity, and take a look at the multi-spectral cameras used to make them.  We’ll find that while it may be true that an image is worth a thousand words, it is also true that an image taken with a hyperspectral camera can be worth hundreds of thousands, if not millions, of useful data points. Continue reading “Hyperspectral Imaging – Seeing the Unseeable”

Hackaday Prize Entry: Crop Data For Improved Yields

As the world’s population continues to increase, more food will be needed for all the extra mouths to feed. Unfortunately, there’s not a whole lot of untapped available farmland. To produce extra food, crop yields need to increase. [Vignesh Ravichandran] is tackling this with the Farmcorder – a device for detecting crop nutrition levels.

The device centers around using spectroscopy to measure the chlorophyll content of leaves. This information can then be used to make educated decisions on the fertilizer required to maximize plant yield. In the past, this has been achieved with expensive bespoke devices, or, at the other end of the spectrum, simple paper color charts.

[Vignesh]’s project takes this to the next level, integrating a spectroscopy package with a GPS and logging over the GSM mobile network. This would allow farmers to easily take measurements out in the field and log them by location, allowing fertilizer application to be dialed in on a per-location basis.  The leaf sensor package is particularly impressive. Relying on a TSL2561 sensor IC, the samples are lit with 650nm and 940nm LEDs. The sensor readings can then be used to calculate the chlorophyll levels in the leaves.

It’s a project that sets out to tackle a serious world problem and uses off-the-shelf parts and some hacker know-how to do so. We hope to see this hardware on farms across the world in the near future!