How AI can Help Increase the Agricultural Productivity in 2021

Applied Tech Review | Monday, October 04, 2021

According to the study, the world's population will rise by 2 billion people by 2050, necessitating a 60percentage increase in food output to feed them.

FREMONT, CA: AI, machine learning (ML), and Internet of Things (IoT) sensors that give real-time data for algorithms that enhance agricultural efficiency, crop yields, and food production costs. According to the study, the world's population will rise by 2 billion people by 2050, necessitating a 60percentage increase in food output to feed them. AI and machine learning are already demonstrating the ability to assist fill the gap in projected food demands for an additional 2 billion people worldwide by 2050.

Gaining insight into how weather, seasonal sunshine, migration patterns of animals, birds, and insects, crop-specific fertilizers and pesticides, planting cycles, and irrigation cycles all impact yield is an ideal challenge for machine learning. In addition, excellent data has never been more critical in determining the financial success of a crop cycle. As a result, farmers, co-ops, and agricultural development firms are doubling down on data-centric techniques and broadening the breadth and scale of using AI and machine learning to improve agricultural yields and quality. Let's take a look at some of the ways AI can enhance agriculture in 2021:

Using AI and machine learning-based surveillance technology to monitor real-time video feeds from every crop field recognizes animal or human breaches and promptly sends an alarm

 AI and machine learning minimize the possibility of domestic and wild animals inadvertently destroying crops or committing a break-in or burglary at a remote farm location. With the fast advancements in video analytics enabled by AI and machine learning algorithms, everyone interested in farming can secure the perimeters of their crops and buildings. AI and machine learning video surveillance systems scale just as well for a large-scale agricultural business as they do for a single farm.

AI and machine learning increase agricultural yield prediction by using real-time sensor data and visual analytics data via drones

The amount of data gathered by smart sensors and drones delivering real-time video streaming offers agricultural specialists whole new data sets that they have never had access to previously. For example, it is now feasible to examine growth trends for each crop over time by combining in-ground sensor data of moisture, fertilizer, and natural nutrient levels. Machine learning is ideal for combining huge data sets and providing constraint-based suggestions for agricultural production optimization.

Today, improving agriculture supply chain traceability by reducing obstacles to bringing fresher, safer commodities to market is a must-have

A well-managed track-and-trace system reduces inventory shrinkage by increasing visibility and control throughout supply chains. A cutting-edge track-and-trace system can distinguish between batch, lot, and container level material allocations in inbound shipments. Most modern track-and-trace systems rely on enhanced sensors to better understand the status of each cargo. RFID and IoT sensors are becoming more widespread in the industrial industry.

The most popular uses of AI and machine learning in agriculture are optimizing the correct combination of biodegradable pesticides and restricting their application to only the field areas requiring treatment to minimize costs while boosting yields

 Agricultural AI applications can now determine the most diseased regions in a planting area by combining intelligent sensors with visual data streams from drones. Then, using supervised machine learning algorithms, they can choose the best pesticide combination to prevent pests from spreading and infect healthy crops.

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