Agriculture is currently experiencing numerous difficulties. On the one extreme, the world’s expanding population raises food demand. On the other hand, Stricter sustainability standards result in a reduction in agricultural land and the usage of agrochemicals and fertilizers. Lower agricultural product margins, a scarcity of competent personnel, and the resulting cost pressures need greater degrees of automation. Droughts and excessive rainfall, for example, are made more challenging by climate-related extreme weather occurrences.
Farmers may solve these challenges with the help of smart farming data by incorporating new technology into their agricultural methods, such as robots and automation, thereby improving their products’ efficiency and sustainability.
Here’s a rundown of the major advantages of smart farming:
Water utilization is optimized through soil and weather-related sensors.
Reduced Operational Costs
Human error, consumption of resources, and overall expenditures are all reduced via automation.
Crop treatment optimization has a direct impact on production rates.
Productivity has improved
Farmers can use smart farming data analysis to improve the quality of their products by adjusting their operations.
Insights into production and real-time data
Farmers may make better judgments with real-time insights into agricultural operations and conditions.
Improved Livestock Production
GPS tracking & sensors aid in the monitoring and managing of cattle health and whereabouts.
Environmental Impact is Reduced
Land and water conservation efforts have a favorable impact on the ecosystem.
- Farm and field evaluations that are accurate
- Crop output and farm value can be reliably predicted by tracking production rates.
- Monitoring from afar
- Farmers make real-time decisions using the Internet of Things anywhere in the world.
- Equipment Inspection
- Production rates, problem prediction, and labor effectiveness can all be used to manage equipment.
- As previously said, smart farming data can assist farmers in making the most efficient use of resources, reducing the environment of food production operations, and improving global food security. The Smart Farming Techniques section will describe how farmers are implementing smart farming data.
- Farmers also have access to vast data thanks to certain smart agriculture practices. They can use this information to make better decisions, increasing production and overall profitability.
- Smart Farming Technologies
Smart farming data relies heavily on software & sensors to control and maintain farm processes. Therefore, automation is critical. This aids farmers in improving the quality and availability of their products, lowering costs, and improving consumer satisfaction. Regardless of the farm’s size or output, all is done while supporting efficient, high-quality, and sustainable agriculture.
The following is a rundown of the most common smart farming technologies:
Thanks to the Internet of Things, farmers may remotely monitor their farm’s operations using connected equipment. Farmers, for example, can use their cell phones to monitor animals and crops from the comfort of their living rooms while also collecting critical data or information that will help them make better decisions.
Drones are becoming increasingly popular on farms, and for a good reason. Drones can not only monitor crops by floating over them, but they can also assist farmers in more sustainable farm management. Drones can even spot animals before fieldwork begins, potentially sparing many lives every year.
Drones can now do many tasks that formerly required human labor, which is a big step forward in addressing the current labor shortage. Here are a few examples of simple activities that drones can perform:
Weed, pest, and crop disease treatment from the air
Monitoring animals, crops, and soil conditions
Using sensors placed across their farms, farmers may receive precise information about a wide range of variables, such as acidity levels and soil temperature. Sensors can also weather forecast conditions for the coming days and weeks, and farmers worldwide to take proper precautions to preserve their crops and livestock.
On farms nowadays, there are several different sorts of robots. The milking robot is one of the most prevalent farm robots, and it can milk cows mechanically, as the name says. Other robots can efficiently pick weeds, sow seeds, and harvest crops, increasing yields and profitability.
Autonomous Electric Tractors
And although driverless electric tractors are in their early stages of development, the technology has great promise for the agriculture business. Not only do they work effectively and independently, but their breakthrough low-emission technology also protects the soil and the environment.
Blockchain technology is being used to store data and information securely in one central location. Farmers are increasingly relying on the combination of blockchain technology. The Internet of Things. This aids farmers in creating a secure environment for data storage and processing. For example, smart sensors in a greenhouse may act as a local blockchain that the farm owner can control from a central location.
Smart farming, as previously said, allows farmers to easily organize and control their crops and livestock from any device and from anywhere in the world. Farmers are given specialized smart farming apps to help them do so. Depending on the program, it may also be feasible to include statistics in these apps, making it much easier for farmers to make informed decisions.
To put the current state of technology in agriculture in context, Bitkom research found that 80 percent of farmers in Germany are already employing digital technologies. Intelligent nutrition systems and GPS-controlled farm gear were two technologies that stood out during the research.
Precision farming increases efficiency and sustainability
Precision farming is a subtype of smart farming that discusses mobile technology to monitor and optimize agricultural production operations. For example, fertilizers are applied in precision farming based on sensors, satellite, or drone data that measures changing field conditions. Based primarily on machine learning and artificial intelligence, an algorithm calculates the best fertilizer application based on the data (Smart Crop). Crop protection and fertilizer use that is more efficient saves money and helps farmers cope with fertilizer requirements. The data generated can be used by agricultural machinery manufacturers to improve their services. It also benefits the ecosystem by lowering nitrate levels in the soil and safeguarding nearby wild plants and insects. On the other hand, miscalculations result in the overfertilization of fields, leading to crop failure and substantial environmental implications. The system is referred to as a cognitive system since it can generate its own decisions. The Fraunhofer IKS has a key competency in improving and securing cognitive systems, which is important for autonomous driving for Industry 4.0.
The adoption of big smart farming data in agriculture leads to less food waste, much more informed decisions, and improved budgeting for farmers. Technology that solves food and agriculture problems will save almost USD 2.3 trillion. Increased farming output enhanced operational efficiency, and less food waste can result from better-informed decisions.
Farmers who have been farming the land for generations and have had this practice passed down through generations can benefit greatly from outside expertise based on data. They frequently had to make decisions based on gut instinct or unclear projections. Consultation with other farmers often does not lead to a resolution, especially if everyone’s knowledge is derived from their family and each farm has different obstacles. Bringing in a third-party expert, on the other hand, can be costly.
Putting technology to work solves the problem of getting an objective view of the situation. Even if constructing the infrastructure costs more than expected, the money invested will pay off quickly when the yield is more and better than in prior years.