01
Edge-native visual intelligence
Run AI plant health inference on Raspberry Pi 5 with a production baseline and an advanced EfficientFormer pipeline designed for realistic edge deployment.
AI Plant Health
Precision Agriculture
AI Plant Health
Orchestration for the next era of precision agriculture.
DELTA Plant fuses edge AI, leaf disease detection, explainable vision, sensor intelligence, and conversational operations into a single deep-tech platform built for agritech teams, research labs, biotech pilots, and venture-scale deployment narratives.
Public Benchmark
89.33%
Top-1 accuracy on a 600-image, 33-class validation sample.
Top-3
99.00%
High-confidence shortlisting for operator review and rapid triage.
Coverage
33
PlantVillage classes aligned to the public documentation surface.
Explainability
LayerCAM
Heatmap and overlay outputs ready for DELTAPLANO, reports, and field trust.
Mission Control
The bio-digital orchestration surface.
v3.2 live
DELTA stack
Edge ready
Vision core
MobileNetV2 + EfficientFormerV2-S1
Hybrid edge inference designed for Raspberry Pi 5 runtime realism.
Explainer
LayerCAM overlays
Readable attention maps that show where the model focused on the leaf.
Operations
DELTAPLANO + Admin + API
Conversational bot access, local control plane, and integration endpoints.
Signals
7 environmental sensor channels
Temperature, humidity, pressure, light, CO2, pH, and EC enrich the diagnosis loop.
Public benchmark logic
General model
89.33% top-1 | 99.00% top-3
EfficientFormer estimate
92.91% top-1 | 100.00% top-3
Mean edge latency
41.360 ms
Leaf
Signal
Signal
Features
One product surface, multiple deep-tech entry points.
DELTA Plant is not just another plant classifier. It is an AI plant health orchestration layer that combines edge inference, explainability, sensor-aware reasoning, operational workflows, and premium communication surfaces.
02
Explainable diagnosis
LayerCAM overlays reveal where the model focused on the leaf, helping operators and researchers interpret model attention rather than relying on opaque predictions.
03
DELTAPLANO operations
Open a conversational gateway into DELTA Plant through Telegram, enabling quick diagnostics, follow-up questions, and human-readable plant health workflows.
04
Sensor and rule fusion
Blend leaf image analysis with environmental sensor channels, expert rules, and agronomic context to move from recognition to decision support.
05
Research-to-release pipeline
Training, export, benchmarking, dissemination, and manual regeneration are wired into a resumable pipeline that supports disciplined technical storytelling.
06
Investor-grade communication
GitHub Pages, documentation, benchmark framing, and premium design language turn the repository into a coherent deep-tech narrative for funding and partnership conversations.
Technology and research
Built at the intersection of edge AI, plant biology, and robotic orchestration.
DELTA Plant positions AI plant health as a systems problem: not just image classification, but a coordinated runtime that spans sensing, inference, explainability, agronomic reasoning, and deployable human interfaces.
Vision stack
MobileNetV2 transfer learning remains the production baseline while EfficientFormerV2-S1 expands the deep-tech story with TFLite int8 export, float32 fallback, and benchmark pathways.
Explainability layer
LayerCAM produces heatmaps and overlays that show where the model looked, improving interpretability for researchers, growers, and partner organizations.
Decision layer
Expert rules, agronomic recommendations, and a quantum-inspired risk component enrich raw model output with plant-health reasoning.
Operational layer
DELTAPLANO, admin tooling, API endpoints, manuals, and benchmark artifacts expose the system to operators, integrators, and technical stakeholders.
Research map
DELTA Plant system flow
Ready for agritech pilots
1. Observe
Leaf image capture, optional sensor acquisition, and context-aware input gathering.
Input
2. Infer
Hybrid edge vision stack performs plant disease inference and optional ensemble logic.
AI
3. Explain
LayerCAM overlays and textual interpretation make the diagnosis legible to humans.
XAI
4. Orchestrate
Rules, recommendations, conversational flows, and operational interfaces turn insight into action.
Ops
Edge performance
41.360 ms
Mean measured edge inference for the public runtime profile.
Public top-1
89.33%
Public benchmark reference on 600 validation images.
Public top-3
99.00%
High-confidence support for fast operator review.
Demo
From repository to live operational narrative.
The DELTA Plant landing page is designed to make the product legible in under a minute: what it does, why it matters, how it performs, and where stakeholders can validate the technical depth.
Operator journey
Step 1
Capture
Collect a leaf image, sensor values, and context from the field, lab, or greenhouse floor.
Step 2
Diagnose
Run edge inference, apply explainability, and enrich the result with agronomic intelligence.
Step 3
Act
Deliver recommendations, benchmark trust signals, and ongoing DELTAPLANO follow-up.
Launch sequence
python main.py --preflight --enable-api --enable-telegram --daemon
Designed for direct deployment on GitHub Pages for storytelling and Raspberry Pi 5 for runtime execution.
Live access
Open DELTAPLANO
Move straight into the conversational control layer and test the product surface most relevant to operators and partners.
Open Telegram bot
Code and assets
Inspect the repository
Benchmark reports, release notes, model card, manual, and the orchestration codebase are all accessible from the public repository.
Explore GitHub
Use cases and impact
Positioned for venture conversations, field pilots, and research credibility.
DELTA Plant creates a single narrative thread across product design, AI performance, and operational practicality. That makes it legible to both technical evaluators and decision-makers.
Greenhouse AI
Protected crop operations
Support controlled-environment agriculture with explainable edge diagnostics and a lightweight operator-facing workflow.
Field scouting
Portable plant triage
Enable fast field checks, leaf health triage, and recommendation loops without relying on cloud-only inference paths.
Research platforms
Explainable edge experimentation
Give AI researchers and agronomic labs a practical substrate for benchmarking, explainability, and deployment-aware experimentation.
Deep-tech capital
Investor-ready narrative
Present a differentiated combination of agritech, edge AI, robotics orchestration, and polished public-facing communication.
Docs and assets
Validate the story with technical depth.
The landing page is only the front door. The repository also exposes the benchmark source, user manual, release notes, model card, and the code itself so every claim can be audited.
Repository guide
README
Product framing, quick start, benchmark snapshot, and links to the most relevant technical assets.
Model transparency
Model Card
Understand deployment assumptions, benchmark framing, and how DELTA Plant communicates model performance.
Operations manual
User Manual PDF
Review the operational manual for runtime setup, interfaces, telemetry, and explainability details.
Benchmark source
Public Report
Inspect the 600-image benchmark report and the raw comparison assets behind the public metrics surface.
Contact
Open a direct channel with DELTA Plant.
Use this form for partnership inquiries, agritech pilots, research collaborations, technical questions, and project follow-up. Messages sent here are relayed to the DELTA Plant mailbox and handled manually.
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