Decentragri
  • Introduction
  • PROJECT DETAILS
    • Problem, Solution & Our Vision
    • Architecture Overview
    • Use Cases
    • Technical Stack
    • Conclusion
  • Decentragri Mobile Application
    • Login and Registration
    • Main Dashboard
    • Scan History & Agronomic Intelligence
    • Blockchain Staking Integration
    • Upcoming Features: Livestock & Animal Integration
  • White-labeled Agri-Supply Marketplace
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  1. Decentragri Mobile Application

Scan History & Agronomic Intelligence

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Last updated 1 month ago

The Scan History module provides users with a chronological log of all soil sensor submissions and their corresponding evaluations. This functionality ensures traceability, supports data-driven decisions, and allows farmers to track the effects of interventions over time.

đź“… Historical Scan Tracking

Each scan entry displays:

  • Overall Result (e.g., “Good,” “Needs Attention,” “No evaluation”)

  • Crop Type

  • Timestamp (with date and precise time)

By tapping on any entry, users can view a detailed breakdown of that scan’s data and receive AI-generated recommendations tailored to the specific crop and conditions.

🔍 Detailed Scan Insights

In the Scan Details view, the app displays the submitted environmental parameters:

  • Fertility (µS/cm)

  • Moisture (%)

  • pH Level

  • Temperature (°C)

  • Sunlight (Lux)

  • Humidity (%)

These values are processed by DecentrAgri’s AI agents, which generate agronomic feedback and suggestions. For example:

  • Moisture levels that are slightly high will trigger recommendations to adjust irrigation.

  • High humidity prompts ventilation advice to mitigate disease risk.

  • Adequate but suboptimal sunlight is flagged with a suggestion for light optimization.

Each scan ends with an Overall Evaluation, summarizing whether the conditions are good, need attention, or couldn’t be evaluated (e.g., due to incomplete data).


🎯 Purpose and Impact

This feature not only empowers farmers with contextual feedback but also supports long-term farm management. By logging and reviewing trends, users can:

  • Detect recurring issues (e.g., persistent overwatering)

  • Monitor the effectiveness of corrective measures

  • Improve yield outcomes using science-backed advice

As data accumulates, the app can evolve to include visual graphs, predictive alerts, and benchmarking against community or regional data.