Wednesday, January 18, 2017

How Regenerative Agriculture and Robotics can benefit each other

I've come around to the view that the best and most inclusive term for high-concept farming which is both sustainably productive and ecologically responsible is Regenerative Agriculture. It implies all that is meant by permaculture, agroecology, carbon farming, and organic farming, but goes beyond these to focus on living matter in the soil, and in this is closely aligned with the term biodynamic. That said, I'm not prepared to argue the point; I only say this by way of explaining why I've chosen to use this term here.

These are perilous times, perhaps not to the extent portrayed by tabloid journalism, or suggested by the recent abundance of misinformation and misinformed opinion on social media, but perilous nonetheless, and there are plenty of squeaky wheels around all competing for grease. In this political-economic environment, making a case for adequate funding (whether public or private) has become increasingly difficult. The cost-benefit ratio or colloquially ‘bang for the buck’ is again the primary metric driving investment, with benefit usually measured in terms of potential profit where private investment is concerned.

The synergy of substantive collaboration (or ‘co-leverage’) can be all-important in determining which projects get funded. If you can show that advances in your field are synergistic with advances in another field, such that each furthers the goals of the other, and your proposal crosses those boundaries, you stand a better chance of attracting investment than if you go it alone.

I believe such a synergy exists, or could easily be made to exist, between robotics and regenerative agriculture.

Robotics needs a market into which it can sell a large volume of many kinds of machinery, and from which it can gain abundant experience, without the burden of liability presented, for example, by autonomous vehicles operating on streets and highways. Agriculture is good for this, in terms of scale, tolerance for beta-ware, and the opportunity for gaining experience with new approaches and new technologies, and operating within complex environments. Regenerative agriculture is particularly ideal, because the need is for small-scale, context-sensitive equipment, which, if it malfunctions, will only cause minimal damage, and the complexity of the environments involved is at least an order of magnitude greater than with conventional agriculture.

Regenerative agriculture needs a way of scaling up its best practices, so they can be deployed across millions of hectares, without resorting to compromises involving heavy equipment and soil compression, or herbicides, and without waiting for millions of people to decide they want to go back to the land and work it by hand. Because many of those best practices involve attention to detail, that scalability can only be achieved through robotics. On the other hand, given equipment that automates not only the work of regenerative agriculture but data collection, the stage would be set for pushing the state of the art, to evolve even better practices and to further develop crop genomes based on phenotype (how plants actually perform under diverse conditions).

Together, robotics and regenerative agriculture can do much to drive each other's development, and to improve humankind's prospects for the future. But, of course, the near-term bottom line is that, together, they can make a more compelling case for adequate funding.

Sunday, January 15, 2017

How technology can help keep farmers relevant

I've long believed that Augmented Reality (AR) and robotics are closely related. Both model their environments to some degree. Robotics uses that model to guide the behavior of a machine, whereas AR uses it to provide an enhanced sensory experience to a human.

The exact nature of that enhanced experience is bounded only by available sensory, computational, and display (audio, haptic, ...) hardware, and by how the data gathered can be usefully transformed into overlays that augment the natural perception of the human user. What is useful is a function of both the content of those overlays and the latency, how much lag time is introduced by the computations involved in generating the overlays. Faster computational hardware can produce more detailed overlays with the same latency or the same overlays with lower latency than slower hardware.

One important application for AR is making it safer and easier for a human to work in collaboration with robotic hardware. For example, a robot might provide the path it intends to follow and the 3D space through which it intends to pass, and that information might be converted in an AR display into highlighting of anything occupying that space. Or perhaps a machine wants to direct the attention of its human counterpart to some particular element of the environment, say one specific plant. That too could be highlighted in the display.

While these examples only scratch the surface of what is possible, they do serve to illustrate that the content of the AR overlays need not be generated entirely from data gathered by sensors attached to the display itself, but can be provided by other sources, including but not limited to other nearby devices. Those sources might include aerial or satellite imagery and information from databases. In the farming context, they might include 3D soil maps produced from core samples.

Examples of overlays that might be useful for a farmer include thermal imagery, current soil moisture content, soil surface porosity and water absorption capacity, exaggerated vertical relief and what to expect in the way of runoff and resulting erosion for various precipitation scenarios, highlighting all plants of a particular species, all plants exhibiting nutrient deficiencies or other trauma, highlighting bare soil (no mulch or plant cover), the presence, activity, and impact of various types of animals. This list could go on and on.

Machines may be better at doing particular manipulations of data, finding correlations, and even at answering well-specified questions, but they're not so good at asking meaningful questions, much less at thinking outside the box. For this reason, the combination of human and machine is more powerful than either alone.

It's still very early days in AR, and there's a great deal of room for improvement. One development that is likely to occur sooner rather than later is voice operation, enabling hands-free control of the AR experience, including which overlays are active and how they are combined. With voice control, a farmer should be able to walk through a field, say what he wants to see, and make modifications to the plan controlling the robotic machinery that actually operates the farm, or issue commands for execution by the first available machine. For most, this will be a more intimate and far richer connection to their land than what they currently experience.

Saturday, January 14, 2017

What's left for farmers to do when machines ‘do it all’?

Let's assume, for a moment, that the vision I've laid out in this blog is ridiculously successful, and, over the next few decades, robotic devices take over all aspects of tending land and crops and handling material inputs and produce, and do it using increasingly sustainable (regenerative) practices that begin the process of retaining and enhancing biological diversity and reviving overworked soils. What's left for farmers to do? Will there even be a need for humans on farms?

Well, in that improbable scenario, once the process has run its course and there's no room left for qualitative improvement, and the machines are self-repairing and the algorithms are self-optimizing for constantly changing conditions, perhaps, strictly speaking, the answer would be "no, not really, not a need in the sense of an indispensable component." A farm so equipped might run itself for months or years at a time without human intervention.

There are two problems with this, however. First, that level of automation is unlikely to happen in the lifetime of anyone reading this, and for the foreseeable future there will be need for human involvement in design and management decisions, making use of their own senses in combination with data to drive improvements, as well as filling in the gaps in the perceptual and physical capabilities of the machines.

The other objection has no expiration date. It's about whether a human presence brings anything valuable to land husbandry, and ultimately whether people are allowed to forget where their food comes from, such that, if all of the machines were to suddenly shut down or break, no one would have a clue as to how to proceed. There will always be a need for humans who understand why the machines make the choices they do, and that understanding will always be best achieved when augmented by being physically present, walking fields, examining details with a magnifying glass, experiencing the sounds and smells directly, and lending a hand with some of the work, even when it could be done faster and more precisely by one of the machines, just to have the experience of participating in the process.

Without such people who remain deeply connected to the land and our use of it to act as interpreters, those who live in urban areas will have only feeds from the machines themselves and will probably lose interest, and perhaps also lose the will to maintain support for environmentally responsible policies in the face of periodic calls for austerity and ever-increasing efficiency. Actually, we're faced with that challenge right now, so maybe there are already too few people on the land, or those who are engaged in farming don't have the time for learning about all of the ‘extraneous’ factors not obviously relevant to their low-margin agribusiness operations, or for acting as interpreters for others.

Sunday, January 01, 2017

The coming age of AI: What is NOT inevitable

In a Backchannel post Steven Levy excerpts “the seven stages of robot replacement” from Kevin Kelly's book The Inevitable: Understanding the 12 technological forces that will shape our future, and briefly lays out the main thesis of the book, which is that AI-driven transformation is coming, whether we're ready for it or not. (Caveat, I have not read the book itself and cannot speak to what all it may or may not include.)

Artificial Intelligence is in the process of arriving, a process which began a few years ago and which is likely to take another few years or decades to play out, but that it will play out is inevitable, and that this will mean a degree of social and economic disruption is equally inevitable. What remains in question is how that arrival will effect everything else, which will be in no small part a function of the forces and specific decisions shaping the translation between machine perception and behavior.

If that translation is optimized for maximum profit for those funding the development, production, and marketing of these technologies, will it reflect concerns which don't rate as priorities for those who are their direct customers, those who buy and operate the machines, presumably hoping to optimize the profitability of their own businesses? These customers must operate in their respective markets, dependent on the demands of their own customers and the cooperation of other suppliers, each driven by their own interests. If their margins are thin, they may have no patience for machine behavior driven by what, for them, are secondary or tertiary concerns, which, in the context of agriculture, could very easily include soil health and erosion control, if catering to those concerns makes the difference between profit and loss in the current year.

AI and robotics could enable the scalability of ecologically sound practices in agriculture, but will they? There's more at play here than the inevitable march of technology. While the agricultural context is really only an example, it is one where concerns other than short-term profitability are all too likely to fail to make the cut as priorities. This would be less worrisome if those other concerns didn't also represent our best hope for halting the loss of biodiversity and soil fertility, for stemming the accumulation of greenhouse gases in the atmosphere, and for improving human nutrition.

AI is inevitable, but the most desirable benefits from it are not, and it will take more than market forces to ensure they don't remain forever beyond reach.

Sunday, September 11, 2016

Daniel Schmoldt of USDA/NIFA presenting at NREC 20th anniversary seminar

Streamed live on Sep 8, 2016 - “Daniel Schmoldt completed his academic training in 1987 from the University of Wisconsin-Madison with degrees in mathematics, computer science, and forest science. The latter included completion of both Masters and Ph.D. programs. From 1987 until 2001, he held several research scientist positions with the U.S. Forest Service while conducting research in a variety of forestry areas: wildfire management, atmospheric deposition, artificial intelligence, decision support systems, ecosystem management, machine vision systems, and automation in forest products utilization. From 1997-2004, he served as Joint Editor-in-Chief for the Elsevier journal, Computers and Electronics in Agriculture, and remains on their editorial board. Since 2001, he has filled a newly created position as National Program Leader for Instrumentation and Sensors with the National Institute of Food and Agriculture, and helps to prioritize, develop, focus, and coordinate USDA research, education, and extension programs covering the development of sensors, instrumentation, and automation technologies related to precision agriculture/forestry, robotics, processing of agricultural and forest products, detection of contaminants in agricultural products, and monitoring and management of air, soil, and water quality. His current $100M+ portfolio of grant programs include specialty crops, agroclimatology, robotics, engineering, nanotechnology, and cyber-physical systems. Finally, he currently serves as the USDA representative to several Office of Science and Technology Policy working groups on engineering and technology.”

Saturday, September 03, 2016

Sensing and Sensory Response in Plants

Presented for a general audience, this video is a gentle introduction to the ability of plants to detect and respond to aspects of their environments.

"Pesticides include both insecticides and herbicides"

In an article titled “How GMOs Cut The Use Of Pesticides — And Perhaps Boosted It Again” on NPR.org, Dan Charles writes “Pesticides include both insecticides and herbicides”.

To this point, I have used ‘pesticide’ and ‘herbicide’ as disjunct terms, rather than treating pesticides as being inclusive of herbicides, thinking of ‘pesticide’ as referring to any chemical agent applied for the purpose of controlling animal pests, but this may not be strictly correct.

In any case, it is at variance with one authoritative interpretation of these terms.