> For the complete documentation index, see [llms.txt](https://decentragri.gitbook.io/decentragri.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://decentragri.gitbook.io/decentragri.com/decentragri-mobile-application/scan-history-and-agronomic-intelligence.md).

# Scan History & Agronomic Intelligence

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.

<div><figure><img src="/files/lQsxRRG80ubiLMz6AgW6" alt="" width="352"><figcaption></figcaption></figure> <figure><img src="/files/0kK9yTUtMv9lXqAfY3le" alt="" width="352"><figcaption></figcaption></figure></div>

#### 📅 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).

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### 🎯 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.