Innovation Rooted in Soil Biology
SoilMind AI goes beyond conventional soil chemistry by combining agronomic knowledge, biological-input matching and field intelligence to support better decisions at farm level.
Why Soil Chemistry Alone Is Not Enough
A standard soil test provides valuable information about factors such as pH, nitrogen, phosphorus and potassium levels. These measurements help identify what is present in the soil and where potential deficiencies may exist.
What soil tests typically do not provide is guidance on which biological inputs are most appropriate for a particular field, crop and growing season. The decision of what to apply is often left to general recommendations or product availability.
As a result, biological products are frequently applied using the same approach across very different field conditions. This can lead to inconsistent performance and uncertainty about their effectiveness. Understanding soil chemistry is important, but turning that information into practical biological recommendations requires an additional layer of agronomic intelligence.
The Soil Intelligence Graph
The Soil Intelligence Graph brings together multiple layers of agricultural knowledge into a single structured decision framework. Rather than looking at soil chemistry alone, it connects soil parameters, crop requirements, environmental conditions and seasonal factors to build a more complete understanding of each field.
The graph maps these relationships against a documented agronomic rule base and a library of biological inputs. By analysing how these factors interact, SoilMind AI can identify which biological products are most suitable for specific soil and crop conditions.
This approach helps transform raw soil test data into practical recommendations that are relevant to the field, the crop and the growing season.
The Biofertiliser Microbiome Matching Engine
Matching the Right Biology to the Right Field. Most soil systems focus on pH, nitrogen, phosphorus and potassium. SoilMind AI adds another layer: biological-input selection. The platform evaluates field conditions and matches them with suitable biological inputs such as mycorrhizal fungi, rhizobium and PGPR. The recommendation logic considers soil conditions, crop requirements and application timing rather than suggesting the same biological treatment everywhere. The current system begins with a traceable agronomic rule base built from published research and the founder's biofertiliser-production experience.
SOIL CONDITIONS
CROP + ENVIRONMENT
BIOLOGICAL MATCHING
FIELD RECOMMENDATION
Microbiome Matching
We bridge the gap between complex soil data and biological solutions through our advanced AI-driven matching engine.
Biological   Synthesis
Works from soil test values, crop type, location and season.
Precision   Matching
Rule based matching logic authored by the founder from agronomic research and production experience.
Sustainable   Outcomes
Phrase it as the intended outcome rather than a proven one.
A Worked Example
See how a field moves through the SoilMind AI recommendation process, from soil and crop information to a practical biological input recommendation.
Field Input
Value
Field Input
Crop
Rice (Paddy)
Variety, if Relevant
MTU-1061
Region
Kadavakollu Village, Gudivada, Krishna District, Andhra Pradesh
Season
Kharif
Soil Type
Clay Loam / Alluvial Irrigated Soil
Soil pH
8.10 (Slightly Alkaline)
Organic Carbon (%)
0.48 (Low)
Available Nitrogen (kg/ac)
112.5 (Medium)
Available Phosphorus (kg/ac)
18.4 (High)
Available Potassium (kg/ac)
198.0 (Medium)
Electrical Conductivity (dS/m)
0.42 (Normal)
Zinc (ppm)
0.85 (Deficient)
Boron (ppm)
0.32 (Medium)
Iron (ppm)
12.4 (High)
Manganese (ppm)
6.25 (Medium)
SoilMind Recommendation
Recommendation
Value
Recommended Biological Input
Arbuscular Mycorrhizal Fungi (AMF) + PGPR (Plant Growth-Promoting Rhizobacteria)
Why This Input Suits This Field
The soil has low organic carbon and deficiencies in nitrogen, phosphorus, potassium and zinc. AMF improves phosphorus uptake and root growth, while PGPR supports nutrient availability and soil microbial activity. Spread 10 t FYM/compost per hectare and incorporate into the soil.
Application Rate
AMF: 5 kg/acre. PGPR: 2 kg/acre. NPK 120:60:40 kg/ha plus ZnSOâ‚„ 50 kg/ha. Do not mix fertilisers directly with biological inputs.
Application Method
Mix AMF with well-decomposed farmyard manure and apply to the soil before transplanting. Apply PGPR through seedling root dipping or soil application.
Application Timing
At land preparation or immediately before transplanting rice seedlings.
Supporting Nutrient Guidance
Apply recommended NPK fertiliser according to the soil test. Add zinc sulphate where zinc deficiency is confirmed and incorporate compost or farmyard manure. Apply 375 kg SSP, 67 kg MOP, 50 kg ZnSOâ‚„ and 10 tonnes FYM/compost per hectare as basal inputs. Apply 261 kg urea per hectare in three split applications of 87 kg each.
What You Receive
Every recommendation is designed to provide practical guidance that can be applied directly to field management decisions.
Recommended Biological Input
Receive a recommended biological product based on soil, crop and seasonal conditions.
Application Rate
Clear guidance on how much of the selected biological input should be applied.
Application Timing
Recommendations on when the biological input should be applied for the greatest effectiveness.
Supporting Nutrient Guidance
Additional nutrient management guidance to support the biological recommendation.
Cautions & Interactions
Important considerations, compatibility notes and factors that may influence performance.
Field Record History
A record of recommendations and decisions maintained against each field for future reference.
Why Soil Chemistry Alone Is Not Enough
A standard soil test provides valuable information about factors such as pH, nitrogen, phosphorus and potassium levels. These measurements help identify what is present in the soil and where potential deficiencies may exist.
What soil tests typically do not provide is guidance on which biological inputs are most appropriate for a particular field, crop and growing season. The decision of what to apply is often left to general recommendations or product availability.
As a result, biological products are frequently applied using the same approach across very different field conditions. This can lead to inconsistent performance and uncertainty about their effectiveness. Understanding soil chemistry is important, but turning that information into practical biological recommendations requires an additional layer of agronomic intelligence.
Built Offline First
Designed for real-world farming environments where internet connectivity cannot always be relied upon.
Many of the farms that need agronomic guidance most operate in areas with limited or inconsistent connectivity. SoilMind AI is being built with an offline-first architecture so that recommendations can be generated directly on the device without requiring a continuous internet connection. The agronomic rule base is stored locally, allowing soil and field information to be processed on the device itself. Once a recommendation has been generated, data can be synchronised automatically when connectivity becomes available. This approach helps ensure that practical guidance remains accessible wherever it is needed.
Agronomic logic stored directly on the device.
Device with Local Rule Base
Recommendation Generated Offline
Recommendations produced without requiring internet connectivity.
Sync When Connected
Data synchronises automatically when a connection becomes available.
Where the Rule Base Comes From
The SoilMind AI rule base combines published agronomic research with hands-on commercial biofertiliser production experience.
Founder Validated: Every rule within the SoilMind AI core is personally reviewed to ensure alignment with hands-on field intelligence and commercial SOPs.
Agronomic Foundations
The matching logic is derived from a rigorous synthesis of published global agronomic research, ensuring evidence-based soil health management.
GMP & SOP Excellence
Insights integrated from commercial biofertiliser manufacturing, validated through Standard Operating Procedures and founder-led quality control.
Stage 1
Rule Base Authored
Agronomic matching logic documented from published research, biological input studies and practical commercial biofertiliser production experience.
Stage 1
Rule Base Authored
Agronomic matching logic documented from published research, biological input studies and practical commercial biofertiliser production experience.
A phased approach to building, testing and deploying practical soil intelligence for real-world agricultural use.
Development Roadmap
Stage 2
Platform Build
Development of the SoilMind AI application, including recommendation workflows, field data management and offline-first functionality.
Stage 3
Field Evaluation
Pilot evaluations and feedback gathering to validate recommendations, improve usability and assess performance under real farming conditions.
Stage 4
UK Launch
Planned public launch of SoilMind AI, making soil intelligence recommendations available to farmers, advisors and agricultural organisations.
Want to see SoilMind AI applied to your fields?
Speak with us about early field evaluations, pilot opportunities and how SoilMind AI is being developed to support practical biological input recommendations.