How can AI improve Agriculture in 2021
AI, machine learning (ML) and the IoT sensors that provide real-time data for algorithms increase agricultural efficiencies, improve crop yields and reduce food production costs.
According to the United Nations’ prediction data on population and hunger, the world’s population will increase by 2 billion people by 2050, requiring a 60% increase in food productivity to feed them. AI and ML are already showing the potential to help close the gap in anticipated food needs for an additional 2 billion people worldwide by 2050.
The following are 3 ways AI has the potential to improve agriculture in 2021:
1. Using AI and machine learning-based surveillance systems to monitor every crop field’s real-time video feeds identifies animal or human breaches, sending an alert immediately. AI and machine learning reduce domestic and wild animals’ potential to accidentally destroy crops or experience a break-in or burglary at a remote farm location.
Given the rapid advances in video analytics fueled by AI and machine learning algorithms, everyone involved in farming can protect their fields and buildings’ perimeters. AI and machine learning video surveillance systems scale just as easily for a large-scale agricultural operation as for an individual farm. Machine-learning based surveillance systems can be programmed or trained over time to identify employees versus vehicles.
2. AI and machine learning improve crop yield prediction through real-time sensor data and visual analytics data from drones. The amount of data being captured by smart sensors and drones providing real-time video streaming provides agricultural experts with entirely new data sets they’ve never had access to before.
It’s now possible to combine in-ground sensor data of moisture, fertilizer and natural nutrient levels to analyze growth patterns for each crop over time. Machine learning is the perfect technology to combine massive data sets and provide constraint-based advice for optimizing crop yields.
3. Yield mapping is an agricultural technique that relies on supervised machine learning algorithms to find patterns in large-scale data sets and understand the orthogonality of them in real-time – all of which is invaluable for crop planning.
It’s possible to know the potential yield rates of a given field before a vegetation cycle is ever started. Using a combination of machine learning techniques to analyze 3D mapping, social condition data from sensors and drone-based data of soil color, agricultural specialists can now predict the potential soil yields for a given crop.
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