Staying ahead of the technology curve means strengthening your competitive advantage. That is why we give you data-driven innovation insights into the agriculture sector. This time, you get to discover 5 hand-picked startups enabling localized weather analytics for agriculture.
Out of 300, the Global Startup Heat Map highlights 5 Top Startups enabling Localized Weather Analytics for Agriculture
The insights of this data-driven analysis are derived from the Big Data & Artificial Intelligence-powered StartUs Insights Discovery Platform, covering 2 093 000+ startups & scaleups globally. The platform gives you an exhaustive overview of emerging technologies & relevant startups within a specific field in just a few clicks.
The Global Startup Heat Map below reveals the distribution of the 300 exemplary startups & scaleups we analyzed for this research. Further, it highlights 5 AgriTech startups that we hand-picked based on criteria such as founding year, location, funding raised, and more. You get to explore the solutions of these 5 startups & scaleups in this report. For insights on the other 295 localized weather analytics solutions for agriculture, get in touch.
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Wikilimo provides Hyperlocal Climate Informatics
Weather forecasting for precipitation and temperature levels, alongside historical data, is critical for effective agricultural plans. However, open-access public sources that use distant meteorological stations provide less accurate weather forecasts. Due to this, farmers now use hyperlocal weather analytics solutions that predict local weather conditions. This offers granular level information required for planning and significantly reduces forecasting errors.
Wikilimo is a British startup that offers hyperlocal climate informatics for agricultural applications. The startup’s AGRI SMART solution combines high-resolution earth observations and data from low-cost weather sensors to provide accurate weather forecasts and hyperlocal weather data. AGRI SMART also estimates localized rainfall and provides smart irrigation notifications based on rainfall patterns, thereby allowing farmers to streamline farm planning, operations, and water management. In addition, Wikilimo’s solution enables pest prediction and identification using weather conditions.
Mertani builds Automatic Weather Stations
Conventional irrigation methods follow periodic watering cycles to hydrate crops and soil. This causes significant plant stress at times when the temperature is high and restricts growth. Due to this, startups couple irrigation management with weather monitoring. With localized weather monitoring devices, farmers track and record environmental parameters independently and ensure timely irrigation of plants. This informed decision-making process further improves crop growth and yields.
Indonesian startup Mertani develops an automatic weather station. The startup’s weather station uses various sensors to monitor environmental parameters such as rainfall, temperature, and humidity to determine microclimate conditions. This optimizes operations planning by enabling food growers to improve the field, crop, and harvest management, as well as reduce costs. The microclimate information is also accessible through smartphones at any time for the users.
Saillog offers Localized Weather Forecasts
Fungal diseases in crops cause severe capital losses in the agricultural sector since fungi thrive in humid conditions. Consequently, food producers monitor weather conditions to apply fungicides and curb fungal growth. However, conventional weather forecasting models offer erroneous predictions that affect pesticide applications and, hence, the overall yield. To this end, startups develop crop protection solutions backed by hyperlocal weather analytics that allows farmers to implement long-term integrated pest management (IPM).
Saillog is an Israeli startup that provides a precision farming smartphone app for farm inspectors and supervisors. The startup utilizes satellite-based remote sensing to provide high-precision hyperlocal weather data and forecasts. It further uses this data to send alerts on potential diseases and pests, thereby improving crop protection. Additionally, Saillog’s digital crop management platform reduces operational inefficiency and costs through real-time assessments and artificial intelligence (AI)-based analytics.
Bountiful develops Hyper-Localized Weather Models
Climate change intensifies weather cycles which affects agricultural operations and yields. Therefore, real-time weather information and accurate forecast models are significant to optimize farm operations and productivity. For this purpose, startups create high-precision weather forecasting models that assist farmers in mitigating the risks of climate change. Such solutions also enable farmers to efficiently schedule field events.
Bountiful is a US-based startup that creates hyper-localized weather models for agriculture analytics and yield forecasts. The startup’s Bountiful Agriculture platform combines long-term weather analysis, satellite imaging, and machine learning (ML) to build high-precision, accurate weather prediction models. Moreover, it achieves this by collecting historical and recent data from fields and farmers, along with sensor data. Thus, Bountiful provides accurate yield forecasts and access to localized weather information for agricultural planning.
HD Rain provides High-Resolution Rainfall Measurements
Rainfall measurements are essential in agricultural activities since it determines everything from the cropping system to harvesting. For instance, watering plants and applying pesticides or fertilizers before rainfall leads to water wastage and chemical runoffs, in turn, increasing operational expenses. Therefore, startups provide ultra-local weather data for farmers that enable data-driven decision-making for irrigation management, pesticide application, cropping pattern identification, and more.
French startup HD Rain offers high-resolution rainfall measurements using sensors. The startup’s sensor network measures the influence of rainfall on waves coming from TV satellites. It then applies ML algorithms on this information to measure and predict rainfall up to 2 hours in advance with an accuracy of 500 meters. Besides, HD Rain’s solution is low-cost, fast to deploy, and easily maintainable, enabling farmers to forecast rainfall and reduce expenditure.
Discover more AgriTech Startups
AgriTech startups such as the examples highlighted in this report focus on geospatial intelligence, climate informatics, as well as digital agriculture. While all of these technologies play a major role in advancing the agricultural industry, they only represent the tip of the iceberg. To explore more agricultural technologies, simply get in touch to let us look into your areas of interest. For a more general overview, you can download our free AgriTech Innovation Report to save your time and improve strategic decision-making.
