Weather Station

Precision agriculture applies technology and data-driven practices to optimize crop production. GPS mapping, soil sensors, and drones for gathering and analyzing crop growing conditions help maximize yields and reduce need for resources such as seeds, water, and chemicals. With precise application, farmers can reduce the use of fertilizers by as much as 40%, which cuts costs and leads to more healthy and organic consumer products. Smart farming doesn’t focus on precise measurements, but on optimizing complex farming systems with the help of technologies. Its main aim is to increase agricultural efficiency, productivity, and profitability. Smart farming involves the adoption of drones, IoT sensors, and weather monitoring stations to help agribusinesses effectively plan and respond to weather-related risks.

Innovative technologies and approaches like precision and smart agriculture have transformed the industry by helping farmers maximize yields while using fewer resources and creating less environmental impact. The rise of both precision agriculture and smart farming has led to the growth of the IoT-based weather station market.

Weather Data for Agriculture

While all weather data has the potential to impact crops in some way, not all of it is equally critical for agriculture. For instance, barometric pressure and UV index usually have a limited impact on crop growth and production, while temperature and precipitation are more critical. By measuring all these conditions and understanding their impact, farmers can manage their crops better and minimize weather-related damage. Let’s look at which weather conditions influence crop growth and production most.
Temperature
High temperatures can cause water stress in plants, reducing their ability to absorb nutrients and leading to yield loss. In contrast, low temperatures can slow growth and delay flowering and fruiting. Agricultural weather forecasting can help farmers deal with temperature fluctuations and take measures to protect their crops—for example, by erecting row covers or increasing irrigation.

Precipitation
Inconsistent rain cycles can lead to soil erosion, flooding, and drought, negatively impacting crop growth. Accurate historical information, real-time monitoring, and weather forecasting can help farmers decide when and how much to irrigate. This helps keep crops in favorable conditions and reduces water consumption.

Sunlight
Weather forecasting highlights low and high sunlight periods, so producers can either increase their watering frequency to compensate for reduced photosynthesis or decrease it to prevent water stress in crops.

Wind
Strong winds can easily damage crops and disrupt pollination. Monitoring wind forecasts can help farmers avoid potential damage by using windbreaks or high-tunnel systems for protection. Accurate wind data can also help determine the best time to apply pest controls and harvest crops to minimize the risk of damage.

Soil moisture
Predicting changes in soil moisture helps farmers decide when to increase tilling to improve soil drainage or reduce it to conserve water in the soil. In addition, a soil moisture forecast is essential for deciding on fertilizer timing and delivery. With favorable soil conditions, fertilizers penetrate the soil more effectively, while overly moist soil causes them to be washed away, wasting money and resources.

Pest and disease forecast
High humidity and warmer temperatures encourage pests and can also lead to disease development in crops. Monitoring these indicators allows farmers to know the likelihood of pest and disease outbreaks and protect their crops appropriately.

IoT-based Weather Monitoring System

Internet of Things (IoT) weather monitoring lets producers do more than just observe the basic weather conditions. A system of interconnected sensors can increase process agility and highlight dependencies between crop quality and conditions, which leads to more accurate predictions and planning.

Here are the main benefits IoT-based weather forecasting technology can bring to agribusinesses. All of these factors make the idea of building an IoT-based weather forecasting solution quite promising. When implemented right, it can lead to better efficiency, higher revenue, greener farming, and better protection from extreme weather conditions.
  • Cost savings. Implementing an IoT weather monitoring system lets farmers lower labor, water, and fertilization expenses. For instance, WiseConn’s DropControl system of interconnected IoT sensors can monitor the correlation of soil moisture and weather conditions and wirelessly control water use. As a result, farmers can save up to 30% on water consumption and minimize the risks of under- or overwatering.

  • Enhanced crop safety. IoT-based weather-tracking technologies can give early warnings of hazards such as frost, hail, heavy rain, strong winds, or extreme heat. This allows farmers to take proactive measures to protect their crops.
  • Better crop management. Automatic crop and weather monitoring minimizes human error and results in more accurate planting with less time and effort. Knowing precise real-time and historical weather patterns for the exact area helps farmers improve sowing, irrigating, fertilizing, and pest control. This results in higher yields and better crop quality.
  • Increased efficiency. Automated weather monitoring systems relieve producers of manual tasks and inaccurate yield predictions. This frees up time and resources for other jobs. Collecting and analyzing weather data in real time also helps farmers respond more quickly to changes in weather conditions.
  • Better climate and weather prediction. By collecting large amounts of weather data, an IoT-based weather monitoring system can improve the accuracy of weather forecasts and climate predictions.

IoT Weather Station

> 0
($ Thousand) Market

Monitoring and Forecasting

Weather monitoring and forecasting in agriculture require a combination of accurate data collection, reliable technology, and effective decision-making processes. Accurate collection requires a robust network of weather monitoring stations, including ground-based and satellite-based sensors. Advanced technologies like AI and ML can then analyze and find patterns in the information and improve the accuracy of forecasts.

IoT Sensors

An effective weather monitoring system has two main components:
  • Accurate and reliable sensors
  • An appropriate data transmission channel

IoT has the potential to improve the efficiency, productivity, and sustainability of agriculture and lay the foundation for building agricultural weather monitoring systems.

Sensors can collect a wide range of weather data —including temperature, humidity, wind speed and direction, precipitation, and soil moisture— and transmit it wirelessly to a cloud server.

The collected data can then be used to:
  • inform farmers of current weather conditions and potential threats which may affect crops
  • create accurate and localized weather forecasts that are specific to a particular farm or field

Let’s start with IoT sensors. The market can offer a wide variety of sensors for any agricultural need. Sensors can be placed in the field or even on specific crops, allowing farmers to monitor the weather in real time. Some examples of sensor types are:

  • Optical sensors. These detect and measure light in various forms and wavelengths to gather information about crops, soil, and other elements of an agricultural environment. Optical sensors are used to monitor crop heat, soil moisture, and the general environment, including light intensity, temperature, and humidity.
  • Electrochemical sensors. These use electrical signals to measure and monitor the chemical properties of soil and water. Farmers can integrate electrochemical sensors into precision agriculture equipment and water quality monitoring systems to gather data about pH, soil nutrients, and the presence of pesticides and herbicides in soil and water.
  • Mechanical soil sensors. These physically interact with the soil to measure its physical properties, including soil moisture, texture, and compaction. Farmers can use mechanical sensors to generate maps and reports about soil conditions, which are vital for optimizing crop growth and soil health.
  • Temperature sensors. Measure temperature and transfer the data they collect over the chosen transmission channel. These sensors are useful for crop, soil, and greenhouse temperature monitoring.

Weather Forecasting

Satellite remote sensing and monitoring for agricultural weather forecasting. Satellite remote sensing works by using multispectral cameras and sensors fitted on satellites to detect reflected, absorbed, and transmitted electromagnetic energy which hits the crops. The correlation between these three parameters defines the spectral signature of the plants, which is unique to each crop type and identifies stressed areas.

Farmers can use satellite remote sensing to measure parameters such as reflected sunlight, air quality, and crop health. For instance, optical visible and infrared (VIR) sensing lets growers see their crops beyond visible wavelengths and find crop health, damage, and stress areas.

Agribusinesses can also use this technology to collect data about the physical, chemical, and biological properties of soil. Ongoing observation can promote better irrigation, fertilization, and crop selection.

In contrast to remote sensing, satellite monitoring involves continuously observing and collecting data from satellites to track changes in weather patterns and environmental conditions. Satellites can cover large areas, providing accurate and real-time information about weather conditions over specifically chosen areas. This data can serve to make short-term weather forecasts and provide long-term climate information.

Paired with data from IoT sensors, satellite images can help farmers monitor the condition of the soil in different areas, identify productivity zones, and find problem areas. Thanks to this, they can create maps for differentiated sowing and fertilization.

Predict Weather Events

AI and machine learning to predict weather events. The application of AI and ML have a huge potential to help farmers boost the accuracy of weather predictions. However, they’re also data-hungry. Luckily, IoT sensors, satellites, and local weather stations produce terabytes of data. When sorted and labeled right, this data can help build ML models to recognize and accurately predict the impact of weather conditions on crops.

In addition to historical and seasonal patterns, these models can consider current weather conditions and long-term climate trends. All this data feeds into algorithms that result in early warnings, helping farmers stay on top of potential weather-related risks and manage their crops accordingly. For instance, by using machine learning algorithms to analyze soil moisture data in correlation with weather forecasts, producers can make smarter decisions about the amount of water they need to apply to their crops. It helps reduce water waste and increase watering efficiency.

Advantages of Using an IoT

Concerns about climate change and its effects on farming have fueled the rise of precise and smart agricultural practices. These practices are built on advanced technologies such as IoT, satellite remote sensing, artificial intelligence, and machine learning.

Using different kinds of IoT sensors as a part of the agricultural weather monitoring solution lets farmers gather and analyze information on temperature, precipitation, soil structure, and other crop-growing conditions. Feeding this data to ML algorithms helps find patterns and correlations between two or several weather factors and take proactive measures, like additional irrigation or pest control, right when they are needed.

As a result, agribusinesses can minimize the disrupting impact of weather conditions, improve farming and growth efficiency, and minimize expenses.