aXiom Zorn has selected the following components to purchase and use big-data to enhance evidence based decision making and increase productivity of famers through Data collection, Data visualization and Data analytics for trend analysis like the Dynamic evaluations of all agricultural aspects. All these solutions use machine learning and artificial intelligence to derive solutions and reports.
Big Data analysis.
aXiom Zorn uses Big data to provide farmers granular data on rainfall patterns, water cycles, fertilizer requirements, and more. This enables them to make smart decisions, such as what crops to plant for better profitability and when to harvest. The right decisions ultimately improve farm yields.
Our data is made richer with over-laid Digital soil maps. Using content from NARO, aXiom Zorn has encoded soil types up to the sub-county level and all the soil maps are over-laid on the profiles of the farmers.
Amalgamation of weather data from satellite images.
Using profiled farmer GPS coordinates and agronomic expertise, profiled farmer data is compiled including GPS coordinates of farmersâ gardens which are synchronized with weather-based satellite imagery and compared to respective crop calendars to develop applicable content. Despite all this Artificial Intelligence, quality control must be implemented through reviews and validation field teams.
Fertilizer Optimization Tool (FOT).
FOT is an intelligent platform fundamental to increasing farmer yield within their financial means. Using data such as farm size, crop planted and available funds, the tool is able to determine the optimal amounts and costs of fertilizer necessary for the farmers to have a profitable season. Data centric tools like this create a strong market linkage between farmers and input suppliers.
This is used to monitor farmer profiling, crops planted and subscriptions, if any. This dashboard is an access point for the clients to view the data in visual formats and in real-time. Local partners believe that tools like these that harness the power of data would greatly improve structures both at farmer group level and umbrella association levels. Adaptations have been made to improve data quality such as algorithms to eliminate data duplication, decentralization of data to give access to farmer groups through a unique login.
These and more digitized services can be accessed in our Digital Agriculture Reference Bureau (DARB).
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