Precision Agriculture with AI

Original article can be found here (source): Artificial Intelligence on Medium

Precision Agriculture with AI

Artificial Intelligence Applications that can Nurture Humanity

In the paper Tackling Climate Change with Machine Learning a series of authors attempt to provide an overview of where machine learning can be applied with high impact in the fight against climate change. One of the points mentioned is forests and farms. Within this point (5) precision agriculture was mentioned.

Within this they give a picture too of a variety of ways that the application of machine learning can help.

Deforestation and unsustainable agriculture has been a practice, and added to this is the burning forests that cover a wide area.

“The large scale of this problem allows for a similar scale of positive impact. […] Precision agriculture could reduce carbon release from the soil and improve crop yield, which in turn could reduce the need for deforestation. Satellite images make it possible to estimate the amount of carbon sequestered in a given area of land, as well as track GHG emissions from it. ML can help monitor the health of forests and peatlands, predict the risk of fire, and contribute to sustainable forestry (Fig. 5).”

They mention as well the Jevons Paradox in this context.

Jevons paradox occurs when technological progress or government policy increases the efficiency with which a resource is used, but the rate of consumption of that resource rises due to increasing demand.

Sophisticated computer vision tools can assist, however they can additionally help degrade the environment if used too efficiently without regard and proper considerations for the environment.

  • According to the article Agriculture is responsible for about 14% of GHG emissions.
  • Globally, agriculture constitutes a $2.4 trillion industry , and there is already a significant economic incentive to increase efficiency.

Land is stripped of trees, tilling exposes topsoil to air (releasing carbon), and since soil is stripped nitrogen-based fertilisers must be added back to the system.

“Synthesizing these fertilizers consumes massive amounts of energy, about 2% of global energy consumption…some of this nitrogen is converted to nitrous oxide, a greenhouse gas that is about 300 times more potent than CO2.”

Thus the industrial production may need advanced tools to help the farmer understand what the land needs – this is known as precision agriculture.

Mentioned in the article are different uses:

  1. Such industrial agriculture approaches are ultimately based on making farmland more uniform and predictable.
  2. This allows it to be managed at scale using basic automation tools like tractors, but can be both more destructive and less productive than approaches that work with the natural heterogeneity of land and crops.
  3. Smarter robotic tools can help enable precision agriculture. RIPPA, a robot under development at the University of Sydney, is equipped with a hyperspectral camera and has the capacity to perform mechanical weeding, targeted pesticide application, and vacuuming of pests. It can cover 5 acres per day on solar energy and collect large datasets for continual improvement. Many other robotic platforms likewise offer opportunities for developing new ML algorithms.
  4. Intelligent irrigation systems can save large amounts of water while reducing pests that thrive under excessive moisture.
  5. ML can also help in disease detection, weed detection, and soil sensing.
  6. ML can guide crop yield prediction and even macroeconomic models that help farmers predict crop demand and decide what to plant at the beginning of the season.
  7. These problems often have minimal hardware requirements, as devices such as Unmanned Aerial Vehicles (UAVs) with hyperspectral cameras can be used for all of these tasks

Summing up there is a possible change that solutions within the field of artificial intelligence can contribute to, if it recognises there needs to be a shift in agriculture:

“Moreover, significantly reducing emissions may require a shift in agricultural paradigms — for example, widespread adoption of regenerative agriculture, silvopasture, and tree intercropping.”

Silvopasture is the intentional combination of trees, forage plants and livestock together as an integrated, intensively-managed system.

Datasets for Training

Being a part of a discussion on remote sensing data on climatechange.ai. I spotted someone who posted about datasets that could be used within the field of artificial intelligence.

This is a treasure trove for anyone who wants to learn or beging thinking about these issues in a practical manner.