AI Powered Agronomic Platform

Monitoreo Computacional de Cultivos para la Agricultura Digital del Futuro

agragent works with any crop and any location worldwide. No installation required.

Launch Platform
0 yrs
Climate History
0
Spectral Indices
0
Scientific Sources
Sentinel-2 · real capture Cabernet · Oakville, Napa Valley · 27 Jul 2026
NDMI — canopy moisture over a Cabernet vineyard in Napa Valley
NDVI — canopy vigour over the same vineyard
NDVI NDMI © Esri · Sentinel-2
NDVIMore vigour in the south blocks
NDMIDrier canopy in the northern third
10 mEvery 5 days, anywhere on Earth
NDVI0.32 … 0.58
NDMI−0.08 … 0.13
Colours stretched to the field's range · drag to compare
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How it works

From field to insight in minutes

Upload your field boundaries, connect to satellite and climate data sources, and receive AI-powered recommendations.

1
Define Your Field
Draw or upload your polygon on the interactive map.
2
Fetch Live Data
Sentinel-2 imagery and 80 years of climate data load automatically.
3
Analyze & Visualize
NDVI, climate KPIs, yield predictions and genomic data in one view.
4
Ask the AI Agent
Get instant agronomic recommendations with INIA bibliographic support.
Field Polygon: Valle del Aconcagua
-32.8421, -70.6234
12.4 ha
Vitis vinifera
Fetching real-time data…
Sentinel-2 SR Harmonized
NDVI 0.72
Open-Meteo Archive
ET₀ 4.2mm
Biblioteca INIA Chile
447 docs
Yield estimate (heuristic)
15 vars
Field Dashboard: Live KPIs
NDVI
0.72
Dense vegetation
GDD
1,240
Base 10°C, Sep–Mar
ET₀
4.2 mm
FAO Penman-Monteith
Yield Estimate
18.4 t/ha
Indicative range
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Monthly ET₀ (mm)
agragent AI: Agronomic Advisor
¿Qué dice INIA sobre riego en uva de mesa Aconcagua?
Buscando en biblioteca INIA…
Según Ferreyra & Selles (INIA, 2001), el riego de alta frecuencia en uva de mesa requiere 4–5 mm/día en floración. Tu ET₀ actual de 4.2 mm/día está dentro del rango, el sistema de goteo es adecuado para tu suelo franco.
Ver publicación INIA →
Consulta sobre tu cultivo…
Data Sources

Powered by open, scientific data

agragent AI reasons over every source
0papers in OpenAlex
0INIA documents
0spectral indices
0years of climate history
See source details
Desliza horizontalmente para ver toda la tabla →
Source Type Access Usage Volume
Sentinel-2 SR Harmonized
 Satellite imagery (10m) Google Earth Engine Vegetation indices, RGB composites 30 índices · 10m · revisita 5 días
Open-Meteo Archive API
 Historical weather Free Temperature, precipitation, humidity, radiation, ET₀ 7 variables · hasta 80 años históricos
WGISD (Embrapa)
 Crop images (300+) Public dataset Object detection annotations 300+ imágenes · 5 variedades de uva
Biblioteca Digital INIA Chile
 Agricultural publications (7.5K+) Open access Agronomic bibliography, full-text search 7.508 documentos · 5.034 con texto completo
RNA-seq (Altimiras et al. 2024)
 Genomic DEG data Published 3,603 DEGs across 8 phenological stages 68 muestras · 1.336M reads · 87 GB
OpenAlex
 Academic literature (250M+) Open access Peer-reviewed papers, citations, abstracts 250M+ papers · 200+ countries · all disciplines
Spectral library

Every index, on any field on Earth

Real Sentinel-2 renders, computed on demand for any crop, anywhere, every 5 days.

Plant diseases

Disease risk from published epidemiological models

Each model is a peer-reviewed rule coded exactly as published, run on your field's hourly weather: the last days plus a 7-day forecast. High risk lights up the field on the map and points to the Sentinel-2 patches worth scouting.

Priority zones heat map over a vineyard, Sentinel-2
Where to scout first Real app capture · Sentinel-2, last 45 days · opacity follows the estimated risk
Scout laterScout first
All models, day by dayReal model output · demo vineyard, Santa Cruz (Chile) · 16 Sep → 22 Oct 2026
lowmoderatehighforecast
Hourly conditionsExample · last 72 h and next 72 h · blue = estimated leaf wetness (RH ≥ 90 %)

Photo diagnosis

Snap a tomato or grapevine leaf and get the three most likely causes. Trained on PlantVillage (Kaggle) plus PlantDoc field photos, and evaluated separately on field photos, which is what matters in the field.

Weather-based risk, not a diagnosis · always confirm in the field

Who uses agragent

Built for every agricultural professional

Researchers

Access genomic DEG data, yield models with full feature importance, published RNA-seq datasets, and peer-reviewed bibliographic search across 7,500+ INIA publications.

RNA-seqDEG AnalysisPublications

Agronomists

Upload soil or foliar reports and get instant recommendations. Calculate irrigation schedules and fertilization plans. Query the AI agent with real-time field context.

Soil AnalysisIrrigationAI Assistant

Farm Managers

Draw your field polygon and instantly see NDVI, climate KPIs, frost alerts, yield predictions, and phenological stage calendar. No technical expertise needed.

NDVI MapsYield ForecastAlerts
Research

Peer-reviewed publications

agragent is grounded in published scientific research in precision & digital agriculture.

2 Publications
5 Institutions
2024–26 Active Research
2024
Cover of Transcriptome Data Analysis Applied to Grapevine Growth Stage Identification, Agronomy 2024

Transcriptome Data Analysis Applied to Grapevine Growth Stage Identification

Agronomy · MDPI · Q1

Francisco Altimiras, Leonardo Pavéz, Alireza Pourreza, Osvaldo Yañez, Lisdelys González-Rodríguez, José García, Claudio Galaz, Andrés Leiva-Araos, Héctor Allende-Cid

Agronomy, 14(3), 613, 2024

DOI PDF
2025
Cover of A Computational Framework for Crop Yield Estimation, EPIA 2024 / Springer LNCS

A Computational Framework for Crop Yield Estimation and Phenological Monitoring

EPIA · Springer LNCS

Francisco Altimiras, Sofía Callejas, Rayner de Ruyt, Natalia Vidal, Astrid Reyes, Mia Elbo, Luis Martí, Nayat Sánchez-Pi

Progress in Artificial Intelligence, EPIA 2024. LNCS vol. 15400, pp. 161–174, Springer, 2025

DOI Springer
Research Institutions
PUCV UC Davis Fraunhofer IAIS Lamarr Institute UDLA

Start monitoring your crops today

agragent works with any crop and any location worldwide. No installation required.

Launch Platform
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