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Vriksh Vani
Architecture & Concept Document

Nature Intelligence: A Conceptual Foundation.

Our technical vision for decoding plant biophysics through Vapor Pressure Deficit (VPD) modeling, non-invasive thermal imaging, atmospheric gas sensing, and on-device TinyML inference — shared openly with the community.

Design Doc

Status

2026

Published

ESP32-S3

Architecture

CC BY 4.0

License

Author: Subhash Koli · Vriksh Vani Nature Intelligence

Executive Summary

Traditional domestic plant care relies heavily on periodic visual inspection and intrusive soil moisture probes. This document outlines a non-invasive biometric architecture that combines non-contact thermal imaging (FLIR Lepton) and environmental gas sensing (BME688) to monitor leaf transpiration dynamics and atmospheric stress.

We describe the Nature Translation Engine (NTE™) framework — an embedded TinyML model designed to run on the ESP32-S3 microcontroller, translating multivariate biophysical states into natural human expressions while maintaining complete privacy through local volatile SRAM execution.

Outline of Topics

1.The Limits of Reactive Visual Plant CareSection 1
2.Vapour Pressure Deficit (VPD) & Leaf TranspirationSection 2
3.Non-Invasive LWIR Thermal Sensing PrinciplesSection 3
4.Atmospheric Gas Resistance & VOC Stress SignaturesSection 4
5.The NTE™ Framework: Mapping Biometrics to Natural LanguageSection 5
6.Edge AI Inference on Low-Power Microcontrollers (ESP32-S3)Section 6
7.Volatile SRAM Privacy ArchitectureSection 7
8.Open Research Questions & Prototype RoadmapSection 8

Download Technical Overview

Read our conceptual framework on non-invasive plant biometrics, sensor selection, and edge TinyML translation.

Open Access · CC BY 4.0