In traditional farming methods, it was a mainstay for the farmer to be out in the field, constantly monitoring the land and condition of crops. But with larger and larger farms, it has become more challenging for farmers to monitor everything everywhere. This is especially true with microfarming, where many remote plots of land may be farmed for different crops, requiring different conditions and precise control of soil and water.
Today, the combination of smart irrigation and control being linked to local sensors, as well as sensing for pH and other environmental conditions, including insolation and local temperature, can stave off many issues that traditionally had been accounted for by “walking the field.” Remote monitoring through smart farming systems enables production yields to increase because farmers have more time to attend to their farm’s real issues: applying their expertise to solving problems with pests, watering in any location, amending soil conditions — all through the use of sensing and automation.
The types of precision farming systems implemented depend on the use of software for management of the business. Control systems manage sensor input, delivering remote information for supply and decision support, as well as automation of machines and equipment for taking action in response to emerging issues and production support. This is not notably different than any other “smart” business model’s success criteria; a standardized approach sets forth the right use of resources for production in real time on the supply side and for meeting stringent constraints coming from the demand side. Thus, in a smart farming system, it’s about managing the supply of land and, based on its condition, setting it forth in the right growing parameters — for example, moisture, fertilizer or material content — to provide production for the right crop that is in demand.
During production, it’s about managing one’s resources to improve the growing process. For example, precision farming systems concern precision seeding using automated tractors to reduce possible loss of seed and optimize spacing of plants to create the highest possible yield per acre. Another example is water, through the use of precision water delivery, such as trickle or subsurface methods, to reduce evaporation and to improve soil moisture content, delivering water only when it is needed through the use of sensors and automation.
On the demand side, smart farming systems are about careful management of the demand forecast and delivering goods to market just in time to reduce waste. Furthermore, it is about managing the delivery channel and ensuring that the transfer of product to the midmarket handler reduces waste through gentle and efficient handling, such as sufficient refrigeration.
Overall, the entire process from farm to table is software-managed and sensor-monitored, reducing overall costs, improving overall yield and quality of the supply, and ultimately the experience for the consumer.