Revolutionizing Farming with AI: How Advanced Weather Stations Are Empowering Farmers

American crop yields are experiencing extreme difficulty due to repeated droughts and other extreme weather events. 2012 was a new low in terms of yields and, according to the Washington Post, 2022 was just as bad with 2023 matching those levels. While the main way to fight this effect is to curtail climate change, there are other efforts that farmers can take to make change today. One of the key ways to achieve that is through the use of modern technology and, specifically, artificial intelligence, which is helping farmers to make smart decisions around weather and crop management.

 

Weather and unmanned vehicles

Of course, weather aircraft have long been a part of the picture when it comes to predicting weather. Balloons were some of the first tools used in any sector, and remain an important part of the weather prediction cycle. Innovation has been forthcoming, and the National Oceanic and Atmospheric Administration (NOAA) now utilizes their own fleet of high-tech, high-altitude weather craft, both unmanned and piloted (such as jets), to help provide high quality weather data.

 

This weather data is freely shared and is used to help farmers make wise decisions on their crops and yields. What has been less widely available are private micro-weather tools. According to Aerotime Hub, these craft are now becoming more accessible, which means farmers can do their own research to find services which can provide the best possible flexibility and expertise for their specific area of operation. When paired with NOAA macrodata, this is an exciting proposition in the search for increased yields and resilience against the challenges of climate change. Furthermore, with AI now in tow, prediction is getting even better.

 

Implementing AI with aircraft

Embracing the seasonal scale is important. Artificial intelligence (AI) has already been widely used in farming and weather predictions, but perhaps not at the level required by farmers. This has changed with the likes of startups such as Salient. According to The Economist, these tools use vast amounts of historic data to make predictions of weather cycles based on seasonal levels, which gives the opportunity to predict climate models at a local scale.

 

This is essential for farmers because it will finally mean they can have models to rely on in the long-term. Making those predictions is an important part of improving yields - after all, farming is a year-round enterprise, and it’s not a case of the weather being acceptable on x days. Instead, farmers need to know what the climate is likely to look like year-round, and which days or weeks will have the greatest level of extreme weather or variance. AI helps to do this, and now, with live monitoring paired with unmanned drones, predictions can be done ‘on the go’.

 

Conducting inspections

Over in the UK, research agency Cambridge Independent have started to look at a drone system that uses real time AI monitoring to help perform maintenance. The potential uses of this technology can be exciting for farmers. Consider an AI-powered drone that has the data from weather cycles and advanced weather stations on board, but can combine it with a smart system that knows what signs to look for in a crop to determine whether or not it’s in good health.

 

The experience of farmers is of course crucial, but consider how a drone can provide an added level of monitoring. By being able to see from an advanced view the exact look and quality of a crop and how that meets with expectations derived from the ongoing weather and climate forecasts, it can create a holistic view that empowers farmers to make the right decision in respect of their crop. This takes out a lot of the heavy lifting, allowing farmers to focus on quality work and ensuring their crops have been planted and maintained in the correct fashion.

 

Enabling farmers is the key. They hold the expertise and knowledge of how to make the most out of a crop - it’s the challenge of technologists to create a framework around which they can work. Using data, advanced technologies and a desire to bring together different standards, they can absolutely achieve this and help to improve yields.

 

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