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AI in Smart Farming

Updated: May 23, 2023



Smart farming using Artificial Intelligence(AI)  is no longer a distant dream, smart farms are here to stay thanks to amazing advancements in AI  devices. Over the past decades, the agriculture sector has undergone significant changes. All thanks to advancements in technology. Today, it’s possible to grow plants even in the most hostile climatic regions. Crops are more resistant to insects, weeds, and climate change than ever before.

What is Smart Farming?

Smart farming means the use of various technologies, including Artificial Intelligence, machine learning and  robotics, to monitor farming operations, reduce human labor and boost the quality and quantity of farm produce.

It’s the integration of modern ICT (information and communication technologies) into agriculture to optimize and streamline crops and livestock production.

Today, their several technologies farmers can leverage to manage their farms, including:

·        Location systems like GPS and Geographical Information Systems (GIS) and Satellite Imagery

·        Sensors for monitoring humidity, water levels, Soil Ph, Sunshine, and temperature

·        Agriculture specific software that merges agronomy and cybernetic to make farm management hassle-free

·        Communication via Cellular IoT solutions and Low-power wide-area networks (LPWANs)

·        Data Analysis systems that provide farmers real-time data on crop and animal health


These technologies provide farmers with full control of the activities in their farms. They also help them make informed decisions that will benefit their crops and livestock.

Smart farming is dependent on Internet of Things (IoT). IoT binds all these technologies together, creating a data-based system that farmers can depend on to manage their farms. The best part is they can do all of this via their smartphone or tablets. They don’t have to travel to their farms regularly. 

Through smart agriculture, farm owners can collect and analyze data to identify problems with their crops. Using the analyzed information, they can decide the best way forward to address the challenges. Whether it’s low levels of water or depletion of essential nutrients, they will know what to do.

The end goal of smart agriculture is to increase crop yield while reducing the cost of production. It also supports the efficient use of resources, ranging from human labor to fertilizer and energy to water consumption.

How to Utilize AI For Yield Prediction and Optimization

Crop yield prediction is difficult because many factors come into play, like environment and genotype. We can only attain accurate yield prediction after understanding how these factors influence crop yields. That’s where artificial intelligence comes in.

By feeding machines with the right datasets, it’s possible to predict crop yield. AI systems can use historical crop yield data and compare with recent data, and over time, accurately determine crop yield.

Accurate yield prediction will allow growers to make data-driven decisions about farm management. Let’s not forget about their finances.

AI system can help farmers identify the amount of light received by their crop’s foliage. If some plants are not receiving enough sunlight, they can alter crop spacing to create enough space for sunlight penetration. Manual observation of the foliage gap is costly and time-consuming.

Visual-enabled AI systems can also observe and analyze the changes of plants daily to determine their growth rate. Such systems can make use of data from infrared sensors, satellite imagery, and thermal cameras.

When farmers apply fertilizers and pesticides, the system can record and measure the response of the crops. Growers can use the data to identify underperforming crops and take the right steps to address the cause of the problem.

AI solutions can get integrated into autonomous tractors to help collect real-time data about soil health, including water levels, temperature, and PH. Farmers can also use drone cameras, satellite captured images, and other sensors to track crop health. When analyzed, the results can help growers identify nutrient deficits in the soil and crop pests & diseases.

Conclusion: Smart Farming using AI

AI and Smart farming are the future of the agriculture industry. They will improve farming by helping to detect crop pests and diseases while increasing the quality of produce. Accurate crop yield prediction via AI will help countries achieve food security.

As you can see, the benefit of Artificial Intelligence in the agriculture industry is undeniable. It allows for more strategic operation, increased efficiency, and reduced production costs. But despite being the future of agriculture, it can’t work alone; it requires other technology.

With that said, AI adoption comes with several challenges, including a lack of diverse datasets and a lengthy learning curve. Other challenges include privacy and security concerns and digital illiteracy.

Even so, AI can help solve bottlenecks in the agriculture industry and foster increased food production.

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