Monitoring Weather Conditions For Crop Protection

Due to technologies like IoT weather stations, weather gathering data, and AI weather prediction, agribusinesses can store and process numberless data sets to be prepared for weather transformations, react to them fast, and promote climate change management initiatives.

FREMONT, CA: Climate changes are irreversible, and agriculture is the industry most affected. No one can alter the weather but monitoring and predicting it can save plenty of money for agribusinesses. This is where agriculture's predictive weather analytics and weather tracking technology can help.

Impact of weather on crops

Variations in weather are a natural thing farmers face from season to season. Variations in weather affect crop yields but don't take farmers by surprise. But with global climate change, Mother Nature appears to be putting extra effort into making things complicated for farmers, pushing them towards climate-resilient agricultural practices.

Weather solutions for agriculture will not survive without this.

Nearly every solution for smart weather monitoring relies on data. And we're not only discussing forecasting extreme weather like floods but regular weather conditions in the field that impact crops daily. Due to technologies like IoT weather stations, weather gathering data, and AI weather prediction, agribusinesses can store and process numberless data sets to be prepared for weather transformations, react to them fast, and promote climate change management initiatives.

The most crucial weather data for agriculture

Rainfall – Examining historical data on rain over defined periods gives bold observations and acts as valuable input for future predictions according to artificial intelligence algorithms.

Temperature – Tracking variations in temperature during the day, month, and year provides an outlook on conditions for crops and inputs for advanced analytics on conditions deciding weather changes.

Wind-Wind direction and speed can warn farmers of a coming storm.

Air pressure is one of the most significant measurements for predicting weather changes.

Humidity – This metric is critical, especially about preparing for rain and using water smartly.

What technologies are important for successful weather monitoring and forecasting in agriculture?

When selecting technologies for weather forecasting, agribusinesses should consider a mix of agricultural tech solutions that complement each other. Applying real-time data on weather conditions related to the current location and season supports farmers take care of soil and crops and handling all weather-related risks. The three main technologies contributing to intelligent weather monitoring for agriculture are smart IoT sensors to gather and analyze data, satellites and weather stations, and AI and machine learning systems for weather predictions.

IoT sensors for weather monitoring

IoT sensors place the foundation for a bigger connected system for weather tracking in agriculture. These systems depend on a network of connected sensors that gather data in the field. Cloud computing platforms then process the gathered data to offer alarms and notifications on potential weather hazards affecting crops.

A connected system of IoT sensors for weather monitoring

Through IoT systems, farmers can get real-time access to information on the environment and soil to plan actions ahead of weather changes. When a system receives distressing data from weather sensors, it can send a notification on forthcoming frost or rainfall.

Benefits of IoT solutions for weather condition monitoring:

• Diminish risks to crops by monitoring severe weather conditions

• Help farmers optimize the application of resources and protect crops

• Increase the quality of products by implying the best time for harvesting

• Send notifications to many devices and platforms in real-time

• Collect honest data in the field that's relevant to a farm's location and the current season

• Incorporate third-party services &access community data

Satellite data and hardware stations employed for weather forecasting technology in agriculture

Agriculture weather predictive technology enables farmers to use satellites to access geospatial and meteorological data to prepare fields for uncommon or severe weather. Satellites can be employed in two ways, first, as a root of data for farmers' weather forecast apps, and second, as transmitters of data gathered from agricultural weather stations on Earth. Farmers can take satellite data for various purposes and utilize aerial images to observe crop yields and weather forecasting in agriculture. However, this second use case is a bit expensive, as satellite data transmission costs nearly $1,000 per kilobyte.

Agribusinesses also use satellites for weather forecasting to monitor global climate changes and predict weather disasters like fires and floods. Most frequently, satellites are controlled by government organizations and therefore aren't flexible enough for custom use cases.

Even they provide the overall picture of weather conditions in an area. Gathering satellite images and data empowers AgriTech applications to help predict crop yields based on weather conditions and field monitoring. It also helps plan smart irrigation based on weather changes that can spread potentially dangerous herbicides across the terrain.

AI and machine learning to forecast weather events

Applying AI and machine learning to weather forecasting is the most recent and promising technological advancement for agriculture. For instance, IBM has created a decision platform for agriculture by executing its IBM Watson technology. As with any AI solution, weather forecasting needs a lot of data to teach machine learning algorithms. This data can be from connected sensors, satellites, and local hardware weather stations to create accurate localized weather predictions. These predictions need great computing power to process large data sets, and capable storage is necessary to save this data for future use.

As deep learning algorithms depend most on the quality of training data, data quality and labeling are critical for accurate predictions. Grading data and recognizing weather patterns should help draw accurate insights on determining weather conditions after training a deep learning model.

The rise in accurate data sources plays a big role in successful weather prediction. Over 1,000 weather monitoring satellites are currently orbiting the Earth, and thousands of weather stations are on Earth's surface. The most recent acquisition is IoT-connected sensors installed by individual farmers in their fields. These offer enough inputs to teach algorithms how to differentiate between cloud patterns, identify the consequences of the least temperature and humidity changes, and discover potential hazards according to changes in wind direction that may carry weather fronts from other terrains.