How IoT can usher the next Agricultural Revolution

IoT in Agriculture

According to the United Nations Food and Agriculture Organization, the world would need to produce 70% more food by 2050 due to the exponential growth of the world population, declining agricultural lands, and depletion of finite natural resources, making it imperative to increase farm productivity. To add to the problem is the increase in demand for freshwater and decline in the yield of some essential crops. The changing nature of the agricultural labourers is a further barrier for the farming sector. Also, there exists the problem of reduction in the agricultural labour force in several nations. The demand for physical labour and other agricultural issues has encouraged the introduction of internet connectivity solutions in farming techniques.


The Internet of Things, or IoT, is the collective network of interconnected devices as well as the technology that enables inter-device and cloud communication. Robots, drones, remote sensors, computer imagery, and ever-evolving machine learning and analytical tools are used in IoT in agriculture to monitor crops, survey, and map fields, and give farmers the information they may use to make time- and money-saving farm management decisions.

Let us examine the IoT-based smart farming applications that are transforming the agricultural industry.

Precision Farming:

Precision farming focuses on the small spatial changes within a farm to maximize output. Variable-Rate Application is the concept that sets precision farming apart from more conventional techniques (VRA). Inputs are provided in the quantities required, where required, and when required. Precision farming may optimize every stage of the agricultural process, increasing productivity and lowering costs simultaneously. Precision Agriculture involves obtaining accurate information from the sensors placed in the fields regarding the state of the soil, crops, and weather.

Livestock Monitoring:

IoT-enabled devices are used in livestock management, also known as livestock monitoring, to track and keep tabs on the health of animals, most frequently cattle. Battery-powered sensors that are worn on an animal’s collar or tag track the animal’s location, temperature, blood pressure, and heart rate and wirelessly transmit the information to farming equipment in almost real time. This enables farmers to check on the well-being and whereabouts of each animal in their herd from any location and to get notifications if anything deviates from the usual range. In addition to monitoring health, livestock monitoring systems can also collect and store historical data on preferred grazing locations using GPS tracking or monitor temperature to pinpoint the height of the mating season.

Agricultural Drones:

Unmanned aerial systems (UAVs) known as agricultural drones are used to monitor crop growth, boost crop yield, and optimize agriculture operations. With drone farming, sensors and digital imaging capabilities can provide farmers with a richer image of their farms. Gaining information from an agriculture drone and using it to increase crop yields and farm productivity could be helpful.

IoT in Agriculture

Greenhouse Technology:

Greenhouse Automation System is a technology method that will help farmers in rural areas by automatically monitoring and controlling the greenhouse environment. It replaces the function of direct human monitoring. A greenhouse is a structure used for controlled plant growth. There is a tremendous demand to build greenhouses today because of urbanization and the scarcity of available land. These greenhouses will be used primarily for producing crops. Thanks to technological advancements, we can utilize IoT to remotely control and monitor several greenhouses.

Digital imaging:

This method of imaging primarily uses sensor cameras that are positioned across the farm to provide photos that are then processed digitally. Utilizing photographs from the database to compare with images of crops to determine the size, shape, colour, and growth, image processing mixed with machine learning adjusts the quality. Sorting and grading produce based on its colour, shape, and size can be facilitated using computer imaging. The mapping of irrigated lands is aided by irrigation throughout time. This aids in making the decision to harvest or not during the pre-harvest season.

Predictive Analytics:

Agricultural predictive analytics technologies evaluate a wide range of recent and historical agricultural, biological, climatic, and hydrological data from multiple sources to create predictions about future outcomes on the farm. These tools may use data mining, predictive modeling, and machine learning.

These forecasts offer farmers useful information that can be used to create models that will enhance agronomic performance, control inputs, optimize the use of resources, forecast market conditions, reduce carbon footprints, and prepare for production and issues in the near and distant future.

Aside from the IoT agriculture use cases mentioned, other significant prospects include vehicle tracking (or perhaps automation), storage management, logistics, etc.

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