Satellite intelligence
Use Google Earth Engine workflows to explore vegetation, moisture and environmental indicators across a selected field and date range.
Sentinel-2Sentinel-1Climate data
HYBRID AGRICULTURAL INTELLIGENCE
See the field.
Understand what it needs.
One connected workflow for satellite-based crop screening, drone imagery and practical geospatial insight — online and offline.
01 / SATELLITE02 / DRONE03 / FIELD INSIGHT
THE AGRI-EYE APPROACH 01 — PLATFORM
From pixels to field decisions.
AGRI-EYE TRIO K is a hybrid agricultural monitoring platform designed to bring satellite data, drone imagery and GIS tools into one workflow. Review field conditions, compare indicators and identify areas that may deserve closer inspection.
It is a decision-support and screening tool. Satellite indicators and risk proxies help guide field verification; they do not replace laboratory testing, agronomic assessment or confirmed disease diagnosis.
About the project ↗Drone & local imagery
Work with supported local raster imagery, inspect spatial variation and prepare candidate target zones for field follow-up.
GeoTIFFGIS layersVRA export
Turn insight into action
Explore map layers, KPIs and statistics, then export reports and geospatial files to support a documented field workflow.
MapsReportsTarget zones
BUILT FOR FIELD WORK 02 — CAPABILITIES
One workspace.
Multiple layers of insight.
Bring mapping, crop screening and export tools together in a practical desktop workflow.
GIS field mapping
Draw or edit an area of interest, explore basemaps and review analysis layers with map context.
Vegetation & moisture indicators
Review supported indices such as NDVI, NDRE, NDMI, NDWI and EVI2 where input data allows.
Crop-specific screening
Crop workflows surface candidate patterns and relative proxies for further investigation — not definitive diagnosis.
Drone target zones
Export candidate zones and geospatial packages to help plan high-resolution inspection and follow-up.
Reports & GIS exports
Create PDF summaries and export supported formats including GeoJSON, KML/KMZ and Shapefile.
Hybrid workflow
Use online satellite analysis or supported offline drone/raster workflows, depending on the task and available data.
PROJECT POSITIONING 03 — OVERVIEW
BUILT AROUND A HYBRID WORKFLOW
A focused view of the AGRI-EYE approach.
An overview of how AGRI-EYE TRIO K brings crop-oriented screening, offline drone imagery, GIS and reporting into one workflow. This graphic is a high-level comparison, not a claim that any platform is best for every task.
Capabilities vary by product version, available datasets and operating context. Results are screening outputs and should be verified in the field.
Project capability overview · select the image to enlarge
EXPLORE THE CROP MODULES 03 — CROP INFO
Every crop has
its own story.
Choose a card to learn what its screening workflow is designed to explore.
CROP / 01CEREAL
Wheat
Reviews vegetation vigor and canopy development through supported indices. Moisture-related indicators and spatial variation can help flag areas for field scouting; seasonal growth patterns may support relative production-potential assessment when data is suitable.
NDVI / NDREWater-stress proxyScout zones
CROP / 02CEREAL
Corn / Maize
Explores crop vigor, canopy variation and moisture-related patterns across the selected field. The map can help prioritize scouting for uneven growth or possible stress, while any yield-related interpretation remains an estimate rather than a measured harvest forecast.
Growth indicesMoisture proxyField variability
CROP / 03TUBER CROP
Potato
Maps vegetation condition and within-field differences that may relate to crop development or water stress. Candidate zones can guide ground checks for uneven canopy, wet areas or other visible symptoms; satellite data alone cannot confirm a specific potato disease.
NDVI / NDREWater proxyTarget zones
CROP / 04ORCHARD
Olive
Separates vegetation-growth indicators from moisture-related indicators and screens for patterns that may warrant orchard inspection. Peacock spot and erosion outputs are candidate screenings only; mixed tree canopy, grass, soil and shadow can affect satellite pixels.
Growth indexWater-stress proxyPeacock spot candidate
CROP / 05LEGUME
Peanut / Groundnut
Explores canopy growth, moisture patterns and candidate areas for follow-up. Late leaf spot and waterlogging screening, plus an aflatoxin-risk proxy, can help prioritize inspection but cannot confirm infection or measure aflatoxin content.
Growth indicesWaterlogging candidateRisk proxy
MODULE / 06FIELD ASSESSMENT
Soil Analysis
Provides a dedicated soil-oriented analysis workflow using the supported online engine. Outputs depend on the available datasets and should be interpreted as screening information, not a substitute for laboratory soil sampling and testing.
Online engineSpatial contextSoil follow-up
ENGINE / 07MULTI-CROP
Universal Engine
A general workflow for supported online or offline datasets when a specialized crop module is not the right fit. It brings common mapping and indicator tools into a flexible analysis path, with results governed by input data quality.
Online / OfflineRaster layersGIS exports
Swipe horizontally or use the arrows to explore all modules.
OBSERVE · COMPARE · VERIFYBetter visibility.
Explore application screenshots ↗
Better visibility.
More informed field checks.
Explore application screenshots ↗PROJECT CREATOR 05 — FOUNDER
Khaled Albakour · Project creator
FROM EARTH ENGINE SCRIPTS TO A HYBRID PLATFORM
Built with curiosity. Shaped by field problems.
Khaled Albakour
Geospatial Specialist & Web Developer
AGRI-EYE TRIO K grew from crop-analysis experiments on Google Earth Engine into a hybrid desktop platform connecting satellite screening, drone-derived imagery and GIS outputs.
I developed this project to connect my practical agricultural experience with my growing skills in GIS, remote sensing, software development and data analysis.
The work also highlighted the value of formal academic training in geospatial technologies, agricultural data science and software engineering—especially for validating remote-sensing models against field observations. Where validation is not established, outputs are presented as indicators, relative proxies or screening candidates, not as confirmed diagnoses.
LET'S CONNECT 04 — CONTACT
Built around the
real field.
For project information, professional collaboration or a technical discussion about AGRI-EYE TRIO K, get in touch.