SoilSerdem utilizes optimized soil sampling methods to create precise soil fertility maps, enabling agronomists to customize soil management strategies for individual fields. This approach reduces sampling costs and enhances data accuracy, allowing for improved decision-making in precision agriculture.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional soil sampling methods can be costly and time-consuming, often requiring a large number of samples to achieve accurate soil fertility maps. This can limit the ability of agronomists to customize soil management strategies for individual fields, hindering precision agriculture efforts.
Solution
SoilSerdem offers optimized soil sampling methods and a powerful soil mapping engine to create precise soil fertility maps, enabling agronomists to customize soil management strategies for individual fields. Their adaptive sampling plans reduce the number of soil samples and related costs while increasing accuracy by leveraging past field data. The soil mapping engine helps agronomists manage large volumes of spatial data, fill in gaps with reliable inference models, and evaluate spatial outcomes to focus on profitability and efficiency. SoilSerdem empowers agronomists to explore new dimensions of soil health and generate actionable insights for their customers.
Target Audience
SoilSerdem's primary customers are agronomists and AgriBusinesses seeking to improve the utility of their spatial data and implement precision agriculture practices.
Features
- Adaptive sampling plans that reduce the number of soil samples and related costs.
- Soil mapping engine that manages large volumes of spatial data.
- Inference models to fill in gaps in ag data sets.
- Spatial outcome evaluation to focus on profitability and efficiency.
- Tools to explore new dimensions of soil health and generate actionable insights.
- Data management from collection to visualization.