- Agriculture is humanity's oldest industry.
And until the down of the Industrial Age, 200 years back,
it was also the world's biggest.
Agriculture 1.0 was the era of manual farming.
Assisted by a cow or a horse,
the farmer toiled on the farm physically.
Industrialization launched Agriculture 2.0,
as farm machines started replacing
much of the work done by human labor.
We are currently in the thick of Agriculture 3.0.
Biotechnology has given farmers genetically modified seeds
that are resistant to certain types of pests
and harsh weather conditions.
AI is now ushering in Agriculture 4.0,
the age of precision agriculture.
The central idea behind precision agriculture
is that the farm is not one homogenous aggregate.
Rather, there is a significant variation
across the farm relating to soil conditions,
pests, even the seed that is dropped on the soil.
Thus, if one could, it is better to sense and respond
to microconditions down to the level
of tiny area on the ground or a single plant.
The benefits to the farmer
would be more efficient use of inputs,
such as water and chemicals,
and higher output in terms of both crop quality
and crop yield.
AI technologies, such as computer vision,
leveraging the data from IoT devices and robotics,
excel at precisely these type of sensing
and responding capabilities.
These technologies are now being deployed
on the ground, as well as in the sky.
On the ground, companies such as John Deere
have developed smart tractors and smart harvesters.
Cameras to a tractor take pictures
of everything they see underneath.
AI trained to distinguish between a crop and a weed
then directs an automated sprayer to apply herbicide
on just the weed.
Smart harvesters utilize digital cameras, as well.
Computer vision systems analyze images of grain,
and if they're being damaged,
adjust the machine settings in real time
so that the grain is harvested without blemishes.
These computer vision systems can be trained
on millions of images to be highly accurate.
Farmers are also beginning to embed
IoT studded sensors into the soil.
Data from moisture sensors can be used
to control remote irrigation systems more precisely.
Similarly, data from sensors
that can measure nitrogen, phosphorous, and potassium
can be used to control the application of fertilizer
to only the spots that need it.
In the sky, agricultural drones are being deployed
to both map and survey the crops
and then apply fertilizers and pesticide
in a targeted manner.
For mapping and surveying,
the drones are fitted with GPS systems and digital cameras,
which can be programmed to stay
within the farm's boundaries, follow certain path,
and take images in a specified manner.
Using computer vision, these images can be analyzed
to learn the condition of the crops
in any micro-area of the farm.
Armed with this knowledge,
the farmer can take more intelligent actions
regarding when and where to turn the irrigation system on
and using drones fitted with tanks and nozzles,
when and where to spray fertilizers and pesticides.
Intelligent farm machines, IoT sensors, and drones
complement, as well as substitute, for each other.
The optimal choice depends on the size of the farm
and economic factors, such as the trade-off
between upfront capital investment
versus ongoing operating expenses.
AI-based predictive analytics
can help farmers make better forecasts
about future demand and price fluctuations,
as well as potential crop yield.
As a result, they can optimize planning of farm operations.
For societies, deploying AI technologies
for more sustainable and productive farming
not only increases global food production,
but also protects our scarce resources.