Future of Farming: AI Transforming Agriculture

Artificial intelligence (AI) has been a part of our lives for far longer than many of us realize. Now AI will be a part of Agriculture. Farming has been transformed by technology over the years, and technical advancements have impacted the agriculture industry in a variety of ways. Let’s see how AI is Transforming the Agriculture industry.

Agriculture is vital to the economy. Agriculture automation is a major concern and a rapidly developing concern around the world. The world’s population is constantly growing, and with it comes increased demand for food and work.

The traditional methods utilized by farmers were insufficient to meet these demands. As a result, new automated procedures were developed. These new methods met food demands while simultaneously providing work opportunities for billions of people.

AI has entered a new phase in agriculture. This technology has protected crop yields against a variety of conditions such as climate change, population increase, job troubles, and food security concerns. These technologies reduce the use of water, pesticides, and herbicides, while also preserving soil fertility. They also aid in the efficient use of labour, increasing production and quality.

It is assisting various industries in increasing efficiency and output. AI in agriculture is assisting farmers in reducing negative environmental consequences and increasing efficiency. The agriculture business has adopted AI to improve overall results.

AI Technology has the power to change the way people think about agriculture by allowing farmers to obtain higher results with less effort while also offering numerous other benefits. However, AI is not a self-contained technology. AI can enhance currently established technology as the next step in the transition from traditional to innovative farming.

AI Transforming the future of Farming

These approaches are widely utilized in agricultural, food, and bio-system engineering to optimize production and operation processes, as well as to tackle a number of challenges in the farming business.

Robot for Fruit Picking

The robot can pick the same quantity of fruits as 30 human employees in a single day. The robot is fitted with camera systems that can do image recognition on the fruits in order to determine whether they are ready to be picked.

Crop analysis using drone and satellite imagery

Consider a drone flying above crops and capturing the entire field. It generates a complete report by evaluating the pictures. This report indicates whether or not the plants have been infected with the disease and whether or not herbicide is required.

Some companies are doing this by scanning 50 acres of fields in 24 minutes and producing a 95 percent accurate health assessment, while others are accomplishing the same thing by integrating drone and satellite imagery.

Weed identifying and elimination

The tractor sweeps the fields, and the camera’s onboard computer system runs deep learning algorithms that can identify weeds and spray herbicide where it’s needed.

This method involves mounting a camera system on the back of a tractor and using only 10% of the herbicides that would have been used if the standard way of spraying the entire field with herbicide had been employed.

Weather forecasting in real-time

Weather events are responsible for 90% of crop losses. And 25 percent of those losses could have been avoided with the use of weather forecasting software. Crop output is influenced by a variety of factors including temperature, rain, humidity, and solar radiation.

AI can be used to aggregate data from satellites, on-ground sensors, and weather stations to provide improved weather forecasts and advise farmers on when to plant and harvest their crops.

Defects in soil

It gives an indication of the sorts of bacteria present in the soil by evaluating a soil sample. Based on this information, specialists can indicate which fertilizers should be used to improve the soil’s quality and whether the soil has any flaws that need to be addressed.

How AI interact with other technologies

Other technologies, such as large data, sensors, and software, are required for AI to function. Similarly, AI is required for the proper operation of other technologies. In the case of huge data, for example, the data isn’t especially valuable. What matters is how it’s processed and whether or not it’s useful.

Big data for making well-informed decisions

Farmers can acquire credible recommendations based on well-sorted real-time information on crop demands by combining AI and big data. As a result, guessing will be eliminated, allowing for more exact farming methods such as irrigation, fertilization, crop protection, and harvesting.

IoT sensors for data collection and analysis

Farmers may acquire more accurate information faster by combining AI agricultural technologies with IoT sensors and software. Better data equals better judgments and less trial and error time and money.

Robotics and automation to reduce manual labour

One of the most common challenges in farming is a manpower shortage, which can be solved with artificial intelligence, autonomous tractors, and the Internet of Things. Because these technologies are more accurate and so eliminate errors, they have the potential to be cost-effective. AI, autonomous tractors, and the Internet of Things, when combined, are the key to precision agriculture.

AI in Agriculture: The Future

Because most cutting-edge technology is only employed on large, well-connected farms, the future of AI in agriculture will require a major effort on universal access. Machine learning, automated agricultural products, and data science in farming will have a bright future if their reach and connectivity are extended to even tiny farms in distant locations throughout the world.