Enabling Farm Easy: AI Trends in Agriculture
Imagine this. Tiny robots pick the harvest, monitor the produce across hundreds of acres of farmlands, and keeping a track of planting cycles, and suggest the right time to sow seeds.
The agricultural industry worldwide is constantly changing while working on data points to analyze different parameters of farming like water usage, soil condition, irrigation cycles, seasonal sunlight, use of seasonal fertilizers, weather conditions, etc. With the help of AI and its mighty tools, farmers and people working across the agricultural landscape are using this data to obtain useful, real-time insights like the right time to sow seeds, carefully selecting crop choices, hybrid seed choices for better harvest produce, pest management, and crop management. Farmers no longer have to worry about the price fluctuation of their crops, the use of herbicide, and manual laborious tasks like handpicking the yields, AI is practically the one-man army magically working its way towards smartly using data to create a win-win situation.
Did you know that The International Federation of Robotics estimated that around 25,000 agricultural robots have already been sold to date? If this is intriguing, let’s delve into the AI trends in agriculture to see the smart progress farmers are making.
PwC India stated that in the AI domain, internet of things (IoT) enabled agricultural (IoTAg) monitoring is slated to be the most rapidly-growing technology field and by 2025, it is projected to be worth $4.5 billion. And the market size in agriculture for AI on a global level was 852.2 million in 2019 and is projected to touch $8,379.5 in 2030.
Revenue impact firm, Markets&Markets has predicted that the spending on Ai technologies and solutions in the agricultural sector is likely to grow from $ 1 billion in 2020 to $ 6 billion in 2026, achieving a Compound Annual Growth Rate (CAGR) of 25.5%.
Farming and the agricultural industry have undoubtedly advanced and transformed in the last 5 decades. From the produce and farm equipment to chemicals and speed, the advancement in technology has made the lives of the farmers a lot more easier and efficient. In this post, let’s take a look at the AI trends in agriculture.
Intelligent use of chemicals
Farmers are using AI systems to improve the quality of the harvest and accuracy. This process is known as precision agriculture where AI sensors accurately detect weeds and explore the type of herbicide to use in that particular region. By focusing on reducing the use of herbicides, farmers are able to reduce costs in the hindsight. In some cases, companies are developing robots that use AI and computer vision to spray chemicals on weeds precisely. In the process, they are eliminating the volume of chemicals used and cutting down on the expenditure drastically.
Right time to sow seeds
They say ‘reap what you sow’ and in the case of the farmers and agricultural businesses have passed on the baton to AI. A good harvest or a good year of produce heavily relies on the time when the seeds are sown. Scientists at International Crops Research Institute for the Semi-Arid Tropics turned to a predictive analytics tool to select a precise date to sow seeds to get maximum yield. The tool also offers insights on other factors like soil health, predicts a week’s weather forecast in addition to providing suggestions on the recommended fertilizers.
Tackling bulk harvest challenge
Gone are the times, when manual laborers were handpicking the produce from the farms. Now, robotic machines have taken up this manual task and are picking bulk harvests more efficiently and quickly. They are not just talking about the bulk harvest but also cleverly improving the size and quality of the produce. A strawberry-picking machine and a vacuum apparatus use a combination of sensor fusion, AI, and machine vision to identify the correct location of the harvest and pick the right fruits from the trees.
Backed with AI, data analytics, and machine learning tools, farmers are now smartly emphasizing data-centric approaches like crop-yielding prediction, visual analytics from drones, and real-time sensor data to redefine the agricultural industry and gain new insights on how farming practices can be more profitable in the coming years.
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