Agtech • Agentic AI • CV

[REDACTED] Agtech Group

Agentic Crop Intelligence Platform

Client:GCC-Backed Agtech Investment Group
Timeline:12 months (2023-2024)
Team:AI Engineering, IoT, Mobile & Data Science
LangGraphYOLOv8Satellite AIIoTIn-MemoryMulti-Agent AI
System Architecture

The Challenge

A large-scale agtech group operating thousands of hectares across Pakistan and the UAE was losing 20-30% of potential yield annually. Issues included sub-optimal irrigation, late-stage pest detection, and purely reactive fertilizer application. Human agronomists could not monitor vast fields at the resolution required for precision farming.

  • High yield loss due to delayed detection of pests and disease
  • Inefficient water usage in arid regions with high cost of irrigation
  • Manual monitoring of thousand-hectare field zones was unscalable
  • Lack of predictive insights for fertilizer and harvest scheduling

The Solution

Developed a multi-modal "Crop Intelligence" platform combining satellite computer vision, ground-level IoT sensor streams, and a multi-agent AI engine for autonomous decision support.

  • Multi-Modal Monitoring combining Sentinel-2 satellite data and IoT sensors
  • Agentic Decision Engine (LangGraph) for autonomous irrigation/fertilizer actions
  • Drone Vision Pipeline (YOLOv8) for high-resolution pest and disease detection
  • Real-time IoT Ingestion via MQTT and InfluxDB time-series storage
  • Localized Farmer App (Urdu/Arabic) for offline-first field recommendations

Lexer System's Approach

1

Satellite & IoT Fusion

Fused Sentinel-2 NDVI analytics with soil moisture and temperature sensor data to build a high-fidelity "digital twin" of every field zone.

2

Agentic Orchestration

Implemented a LangGraph architecture where Monitoring, Prediction, and Action agents collaborate to generate precise operational recommendations.

3

Custom Pest Detection

Fine-tuned YOLOv8 on local crop disease datasets (cotton bollworm, wheat rust) to provide field-level diagnosis via drone imagery.

4

Urdu/Arabic Localization

Designed a mobile interface specifically for farm workers, delivering AI-driven actions in local languages with intuitive iconography.

Results & Impact

25%
Yield Increase

In the first harvest season post-deployment

-35%
Water Usage

Reduction through AI-optimized irrigation

12 Days Earlier
Pest Detection

Compared to traditional manual field inspections

89%
Autonomy

Routine decisions handled by agents without human input

Technical Highlights

Multi-Agent AI

LangGraph-based agents that can reason about complex agricultural trade-offs (e.g., yield vs. resource cost).

Edge Computer Vision

High-speed object detection for pest identification, deployable on mobile devices or drone-mounted processors.

Time-Series Intelligence

Predictive modeling on InfluxDB data to forecast water stress and nutrient requirements 14 days ahead.

Lessons Learned

  • Data fusion between satellite and IoT is more accurate than either source individually
  • Agentic AI provides the "reasoning" layer that traditional predictive models lack in operational settings
  • Offline capability is non-negotiable for agtech deployments in remote regions

Next Steps

  • Integrate automated harvester and tractor fleet orchestration
  • Implement carbon credit verification using historical satellite health data
  • Expand to indoor hydroponic and vertical farming system optimizations

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