No escape even for agrochemicals!

28/09/2017
In this article key points that are covered in depth in the IDTtechEX published report “Agricultural Robots and Drones 2017-2027: Technologies, Markets, Players” by Dr Khasha Ghaffarzadeh and Dr Harry Zervos are discussed. 

New robotics is already quietly transforming many aspects of agriculture, and the agrochemicals business is no exception. Here, intelligent and autonomous robots can enable ultraprecision agriculture, potentially changing the nature of the agrochemicals business. In this process, bulk commodity chemical suppliers will be transformed into speciality chemical companies, whilst many will have to reinvent themselves, learning to view data and artificial intelligence (AI) as a strategic part of their overall crop protection offerings.

Computer vision
Computer vision is already commercially used in agriculture. In one use case, simple row-following algorithms are employed, enabling a tractor-pulled implement to automatically adjust its position. This relieves the pressure on the driver to maintain an ultra-accurate driving path when weeding to avoid inadvertent damage to the crops.

The computer vision technology is however already evolving past this primitive stage. Now, implements are being equipped with full computer systems, enabling them to image small areas, to detect the presence of plants, and to distinguish between crop and weed. The system can then instruct the implement to take a site-specific precision action to, for example, eliminate the weed. In the future, the system has the potential to recognize different crop and weed types, enabling it to take further targeted precision action.

This technology is already commercial, although at a small scale and only for specific crops. The implements are still very much custom built, assembled and ruggedized for agriculture by the start-ups themselves. This situation will continue until the market is proven, forcing the developers to be both hardware and software specialists. Furthermore, the implements are not yet fully reliable and easy to operate, and the upfront machine costs are high, leading the developers to favour a robotic-as-a-service business model.

Nonetheless, the direction of travel is clear: data will increasingly take on a more prominent (strategic) role in agriculture. This is because the latest image processing techniques, based on deep learning, feed on large datasets to train themselves. Indeed, a time-consuming challenge in applying deep learning techniques to agriculture is in assembling large-scale sets of tagged data as training fodder for the algorithms. The industry needs its equivalents of image databases used for facial recognition and developed with the help of internet images and crowd-sourced manual labelling.

In not too distant a future, a series of image processing algorithms will emerge, each focused on some set of crop or weed type. In time, these capabilities will inevitably expand, allowing the algorithms to become applicable to a wider set of circumstances. In parallel, and in tandem with more accumulated data (not just images but other indicators such NDVA too), algorithms will offer more insight into the status of different plants, laying the foundation of ultra-precision farming on an individual plant basis.

Agriculture is a challenging environment for image processing. Seasons, light, and soil conditions change, whilst the plant themselves transform shape as they progress through their different stages of growth. Nonetheless, the accuracy threshold that the algorithms in agriculture must meet are lower than those found in many other applications such as autonomous general driving. This is because an erroneous recognition will, at worse, result in elimination of a few healthy crops, and not in fatalities. This, of course, matters economically but is a not safety critical issue and is thus not a showstopper.

This lower threshold is important because achieving higher levels of accuracy becomes increasingly challenging. This is because after an initial substantial gain in accuracy improvement the algorithms enter the diminishing returns phase where lots more data will be needed for small accuracy gains. Consequently, algorithms can be commercially rolled out in agriculture far sooner, and based on orders of magnitude lower data sizes and with less accuracy, than in many other applications.

Navigational autonomy
Agriculture is already a leading adapter of autonomous mobility technology. Here, the autosteer and autoguide technology, based on outdoor RTK GPS localization, are already well-established. The technology is however already moving towards full level-5 autonomy. The initial versions are likely to retain the cab, enabling the farmer/driver to stay in charge, ready to intervene, during critical tasks such as harvesting. Unmanned cable versions will also emerge when technology reliability is proven and when users begin to define staying in charge as remote fleet supervision.

The evolution towards full unmanned autonomy has major implications. As we have discussed in previous articles, it may give rise to fleets of small, slow, lightweight agricultural robots (agrobots). These fleets today have limited autonomous navigational capability and suffer from limited productivity, both in individual and fleet forms. This will however ultimately change as designs/components become standardized and as the cost of autonomous mobility hardware inevitably goes down a steep learning curve.

Agrobots of the future
Now the silhouette of the agrobots of the future may be seen: small intelligent autonomous mobile robots taking precise action on an individual plant basis. These robots can be connected to the cloud to share learning and data, and to receive updates en mass. These robots can be modular, enabling the introduction of different sensor/actuator units as required. These robots will never be individually as productive as today’s powerful farm vehicles, but can be in fleet form if hardware costs are lowered and the fleet size-to-supervisor ratio is increased.

What this may mean for the agrochemicals business is also emerging. First, data and AI will become an indispensable part of the general field of crop protection, of which agrochemical supply will become only a subset, albeit still a major one. This will mandate a major rethinking of the chemical companies’ business model and skillsets. Second, non-selective blockbuster agrochemicals (together with engineered herbicide resistant seeds) may lose their total dominance. This is because the robots will apply a custom action for each plant, potentially requiring many specialized selective chemicals.

These will not happen overnight. The current approach is highly productive, particularly over large areas, and off-patent generic chemicals will further drive costs down. The robots are low-lying today, constricting them to short crops. Achieving precision spraying using high boys will be a mechanical and control engineering challenge. But these changes will come, diffusing into general use step by step and plant by plant. True, this is a long term game, but playing it cannot be kicked into the long grass for long.

@IDTechEx #Robotics #Agriculture #PAuto
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Simulating agricultural climate change scenarios.

19/09/2017
Extreme weather, believed to result from climate change and increased atmospheric CO2 levels, is a concern for many. And beyond extreme events, global warming is also expected to impact agriculture.(Charlotte Observer, 7 Sept 2017)

Although it is expected that climate change will significantly affect agriculture and cause decreases in crop yields, the full effects of climate change on agriculture and human food supplies are not yet understood. (1, 2 & 3 below)

Simulating a Changing Climate
To fully understand the effects that changes in temperature, CO2, and water availability caused by climate change may have on crop growth and food availability, scientists often employ controlled growth chambers to grow plants in conditions that simulate the expected atmospheric conditions at the end of the century. Growth chambers enable precise control of CO2 levels, temperature, water availability, humidity, soil quality and light quality, enabling researchers to study how plant growth changes in elevated CO2 levels, elevated temperatures, and altered water availability.

However, plant behavior in the field often differs significantly from in growth chambers. Due to differences in light quality, light intensity, temperature fluctuations, evaporative demand, and other biotic and abiotic stress factors, the growth of plants in small, controlled growth chambers doesn’t always adequately reflect plant growth in the field and the less realistic the experimental conditions used during climate change simulation experiments, the less likely the resultant predictions will reflect reality.3

Over the past 30 years, there have been several attempts to more closely simulate climate change growing scenarios including open top chambers, free air CO2 enrichment, temperature gradient tunnels and free air temperature increases, though each of these methods has significant drawbacks.

For example, chamber-less CO2 exposure systems do not allow rigorous control of gas concentrations, while other systems suffer from “chamber effects” included changes in wind velocity, humidity, temperature, light quality and soil quality.3,4

Recently, researchers in Spain have reported growth chamber greenhouses and temperature gradient greenhouses, designed to remove some of the disadvantages of simulating the effects of climate change on crop growth in growth chambers. A paper reporting their methodology was published in Plant Science in 2014 and describes how they used growth chamber greenhouses and temperature gradient greenhouses to simulate climate change scenarios and investigate plant responses.3

Choosing the Right Growth Chamber
Growth chamber and temperature gradient greenhouses offer increased working area compared with traditional growth chambers, enabling them to work as greenhouses without the need for isolation panels, while still enabling precise control of CO2 concentration, temperature, water availability, and other environmental factors.

Such greenhouses have been used to study the potential effects of climate change on the growth of lettuce, alfalfa, and grapevine.

CO2 Sensors for Climate Change Research
For researchers to study the effects of climate change on plant growth using growth chambers or greenhouses, highly accurate CO2 measurements are required.

The Spanish team used the Edinburgh Sensors Guardian sensor in their greenhouses to provide precise, reliable CO2 measurements. Edinburg Sensors is a customer-focused provider of high-quality gas sensing solutions that have been providing gas sensors to the research community since the 1980s.3,5

The Guardian NG from Edinburgh Sensors provides accurate CO2 measurements in research greenhouses mimicking climate change scenarios. The Edinburgh Sensors Guardian NG provides near-analyzer quality continuous measurement of CO2 concentrations. The CO2 detection range is 0-3000 ppm, and the sensor can operate in 0-95% relative humidity and temperatures of 0-45 °C, making it ideal for use in greenhouses with conditions intended to mimic climate change scenarios.

Furthermore, the Guardian NG is easy to install as a stand-alone product in greenhouses to measure CO2, or in combination with CO2 controllers as done by the Spanish team in their growth control and temperature gradient greenhouses.4,6 Conclusions Simulating climate change scenarios in with elevated CO2 concentrations is essential for understanding the potential effects of climate change on plant growth and crop yields. Accurate CO2 concentration measurements are essential for such studies, and the Edinburgh Sensors Guardian NG is an excellent option for researchers building research greenhouses for climate change simulation.

References

  1. Walthall CL, Hatfield J, Backlund P, et al. ‘Climate Change and Agriculture in the United States: Effects and Adaptation.’ USDA Technical Bulletin 1935, 2012. Available from: http://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1000&context=ge_at_reports
  2. https://www.co2.earth/2100-projections Accessed September 7th, 2017.
  3. Morales F, Pascual I, Sánchez-Díaz M, Aguirreolea J, Irigoyen JJ, Goicoechea N Antolín MC, Oyarzun M, Urdiain A, ‘Methodological advances: Using greenhouses to simulate climate change scenarios’ Plant Science 226:30-40, 2014.
  4. Aguirreolea J, Irigoyen JJ, Perez P, Martinez-Carrasco R, Sánchez-Díaz M, ‘The use of temperature gradient tunnels for studying the combined effect of CO2, temperature and water availability in N2 fixing alfalfa plants’ Annals of Applied Biology, 146:51-60, 2005.
  5. https://edinburghsensors.com/products/gas-monitors/guardian-ng/ Accessed September 7th, 2017.
@Edinst #PAuto #Food