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Crop protection by monitoring weather conditions
Environmental Business Review | Wednesday, February 09, 2022
With global climate change, Mother Nature looks to be putting extra effort into making things complicated for farmers, pushing them towards climate-resilient agricultural practices.
FREMONT, CA: Because of technologies such as IoT weather stations, weather crowd data, and AI weather prediction, agribusinesses can keep and process numberless data sets to be readied for weather transformations, react to them fast, and promote climate change management initiatives.
Climate changes are irrevocable, and agriculture is the industry most impacted. Certainly, nobody can change the weather, but monitoring and forecasting it can save lots of money for agribusinesses. Here agriculture's predictive weather analytics and weather monitoring technology can help.
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Influence of weather on crops
Fluctuations in weather are a natural thing farmers face from season to season. Fluctuations in weather affect crop yields but don't take farmers by surprise. But with global climate change, Mother Nature looks to be putting extra effort into making things complicated for farmers, pushing them towards climate-resilient agricultural practices.
Weather solutions for agriculture will not resist without this.
Around every solution for smart weather, monitoring relies on data. And we're not just discussing forecasting extreme weather like floods but regular weather conditions in the field that impact crops daily. Because of 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 gives an outlook on conditions for crops and inputs for advanced analytics on conditions determining weather changes.
Wind-Wind direction and speed can alert farmers of a coming storm.
Air pressure is one of the most important measurements for predicting weather changes.
Humidity: This metric is critical, especially in preparing for rain and utilizing water smartly.
What technologies are crucial for successful weather monitoring and forecasting in agriculture?
When choosing technologies for weather forecasting, agribusinesses should consider a mix of agricultural tech solutions that complement each other. For example, real-time data on weather conditions related to the current location and season supports farmers in taking care of soil and crops and handling all weather-related risks. The three principal agriculture technologies contributing to intelligent weather monitoring are smart IoT sensors to collect and analyze data, satellites and weather stations, and AI and machine learning systems for weather predictions.
IoT sensors for weather monitoring
IoT sensors are the foundation for a more connected system for weather tracking in agriculture. These systems are based on a network of connected sensors that gather data in the field. Cloud computing platforms then treat the gathered data to give alarms and notifications on possible weather hazards impacting crops.
Using IoT sensors to monitor the weather
With IoT systems, farmers can get real-time access to information on the environment and soil to plan actions previous to weather changes. For example, when a system accepts distressing data from weather sensors, it can transmit a notification on upcoming frost or rainfall.
Edges of IoT solutions for weather condition monitoring:
• Decrease risks to crops by monitoring severe weather conditions
• Help farmers enhance the application of resources and protect crops
• Rise the quality of products by suggesting the best time for harvesting
• Send notifications to numerous devices and platforms in real-time
• Gather honest data in the field that's relevant to a farm's location and the present season
• Integrate third-party services & enter community data
Satellite data and hardware stations utilized for weather prediction technology in agriculture
Agriculture weather predictive technology permit farmers to use satellites to access geospatial and meteorological data to prepare fields for unusual or severe weather. Satellites can be applied in two ways, first, as a root of data for farmers' weather forecast apps, and second, as transmitters of data collected from agricultural weather stations on Earth. Farmers can take satellite data for diverse purposes and use aerial images to observe crop yields and weather forecasting in agriculture. Nevertheless, this second use case is a bit costly, as satellite data transmission costs nearly $1,000 per kilobyte.
Agribusinesses also utilize satellites for weather forecasting to monitor global climate changes and predict weather disasters like fires and floods. But, most frequently, satellites are controlled by government organizations and therefore aren't flexible enough for custom use cases.
Even they give the overall picture of weather conditions in an area. Assembling 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 following weather changes that can spread possibly dangerous herbicides across the terrain.
AI and machine learning to foretell weather events
Applying AI and machine learning to weather foretelling is the most recent and encouraging technological advancement for agriculture. For example, IBM has made a decision platform for agriculture by executing its IBM Watson technology. As with any AI solution, weather forecasting demands a lot of data to educate machine learning algorithms. This data can be connected to sensors, satellites, and local hardware weather stations to make accurate localized weather predictions. These predictions require fine computing power to process large data sets, and capable storage is requisite to save this data for future use.
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