Πώς να Προβλέψετε την Απόδοση Καλλιεργειών με Χρήση Τεχνητής Νοημοσύνης
Ανακαλύψτε πώς η τεχνητή νοημοσύνη μεταμορφώνει τη γεωργία με ακριβείς προβλέψεις απόδοσης καλλιεργειών χρησιμοποιώντας δορυφορικές εικόνες, αισθητήρες IoT, δεδομένα κλίματος και μοντέλα μηχανικής μάθησης. Μάθετε για τα καλύτερα παγκόσμια εργαλεία AI—NASA Harvest, Microsoft FarmBeats, EOSDA—που υποστηρίζουν αγρότες και αγροτικές επιχειρήσεις παγκοσμίως.
Η τεχνητή νοημοσύνη φέρνει επανάσταση στη γεωργία επιτρέποντας πολύ πιο ακριβείς προβλέψεις απόδοσης. Τα σημερινά μοντέλα AI μπορούν να επεξεργαστούν τεράστια σύνολα δεδομένων – πολύ περισσότερα από όσα μπορεί ένας άνθρωπος – για να προβλέψουν τις συγκομιδές.
Οι εφαρμογές AI έχουν σχεδιαστεί να επεξεργάζονται πολύ περισσότερα δεδομένα από έναν άνθρωπο και στη συνέχεια να αναλύουν αυτά τα δεδομένα για να κάνουν πιο ακριβείς προβλέψεις.
— Reuters
Οι ακριβείς προβλέψεις απόδοσης είναι ζωτικής σημασίας για την επισιτιστική ασφάλεια και τον σχεδιασμό, ειδικά καθώς η κλιματική αλλαγή απειλεί τις καλλιέργειες. Μελέτες αναφέρουν έως και 24% μείωση στην απόδοση καλαμποκιού έως το 2030 σε σενάρια υψηλής θέρμανσης. Τα σύγχρονα συστήματα AI παρακολουθούν συνεχώς τα χωράφια: μπορούν να εντοπίσουν στρες ή παράσιτα εβδομάδες νωρίτερα, να χαρτογραφήσουν προβληματικές περιοχές και ακόμη να προτείνουν πότε και πού να ποτίσουν ή να λιπάνουν.
Πηγές Δεδομένων για Μοντέλα AI Καλλιεργειών
Τα μοντέλα απόδοσης καλλιεργειών AI βασίζονται σε πολλαπλές ροές δεδομένων για να δημιουργήσουν ολοκληρωμένη πληροφόρηση πεδίου:
Δορυφορικές & Αεροφωτογραφίες
Δεδομένα Καιρού & Κλίματος
Αισθητήρες Εδάφους & Υπογείων
Ιστορικά Αρχεία Απόδοσης
Μοντέλα Μηχανικής Μάθησης για Πρόβλεψη Απόδοσης
Μόλις συλλεχθούν τα δεδομένα, οι αλγόριθμοι μηχανικής μάθησης εκπαιδεύονται για να προβλέπουν αποδόσεις. Έχουν δοκιμαστεί πολλοί τύποι μοντέλων, ο καθένας με ξεχωριστά πλεονεκτήματα:
Σύνολα Βασισμένα σε Δέντρα
Οι μέθοδοι Random Forest και Gradient Boosting διαχειρίζονται εξαιρετικά καλά μικτά δεδομένα.
- Υπερέχουν σε πολλές μελέτες
- Διαχειρίζονται μη γραμμικές σχέσεις
- Είναι ανθεκτικά σε ακραίες τιμές
Νευρωνικά Δίκτυα
Τα ANN, τα συνελικτικά δίκτυα και τα επαναλαμβανόμενα LSTM ξεχωρίζουν με μεγάλα σύνολα δεδομένων.
- Αιχμαλωτίζουν σύνθετα μοτίβα
- Κλιμακώνονται με τον όγκο δεδομένων
- Επιτρέπουν μεταφορά μάθησης
Υβριδικές Προσεγγίσεις
Ο συνδυασμός βαθιάς μάθησης με μεταφορά μάθησης αυξάνει την ακρίβεια σε περιοχές με περιορισμένα δεδομένα.
- Αξιοποιούν προεκπαιδευμένα μοντέλα
- Προσαρμόζονται σε τοπικές συνθήκες
- Αξιοποιούν στο έπακρο περιορισμένα δεδομένα
Οι αλγόριθμοι μηχανικής μάθησης έχουν αποδειχθεί αποτελεσματικοί για την πρόβλεψη απόδοσης σε πολλές μελέτες.
— Έρευνα AI στη Γεωργία
Παγκόσμιες Εφαρμογές AI για Απόδοση Καλλιεργειών
Η πρόβλεψη απόδοσης με βάση την AI εφαρμόζεται πλέον παγκοσμίως σε όλες τις κύριες καλλιέργειες. Ακολουθούν βασικές πραγματικές εφαρμογές:
Κένυα – Πρόβλεψη Απόδοσης Καλαμποκιού
Ερευνητές συνδύασαν μοντέλο προσομοίωσης ανάπτυξης καλλιέργειας με τηλεπισκόπηση χρησιμοποιώντας δορυφορικά δεδομένα WaPOR του FAO για να προβλέψουν αποδόσεις καλαμποκιού. Η υβριδική προσέγγιση βελτίωσε την ακρίβεια σε σχέση με τη χρήση μόνο του μοντέλου, υποστηρίζοντας εκτιμήσεις απόδοσης σε περιοχές με περιορισμένα δεδομένα.
Ηνωμένες Πολιτείες – Χαρτογράφηση Παραγωγής Σιταριού
Ομάδες εκπαίδευσαν βαθιά δίκτυα LSTM σε πολυετή δεδομένα και δορυφορικούς δείκτες για να χαρτογραφήσουν την παραγωγή σιταριού ανά κομητεία, επιτρέποντας ακριβείς περιφερειακές προβλέψεις.
Ευρώπη – Παρακολούθηση Πολλαπλών Καλλιεργειών
Έργα όπως η πρωτοβουλία UPSCALE χρησιμοποιούν δεδομένα από drones και δορυφόρους για κριθάρι, σιτάρι, πατάτες και τριφύλλι για τον υπολογισμό δεικτών επιφάνειας φύλλων και χλωροφύλλης – κρίσιμες εισροές για τη βελτίωση των μοντέλων απόδοσης.
Εμπορικές Πλατφόρμες & Εργαλεία
Διάφορες πλατφόρμες AI ενσωματώνουν πλέον αυτές τις μεθόδους για πραγματικούς αγρότες παγκοσμίως:
SIMA (Αργεντινή)
Microsoft Azure FarmBeats
EOSDA Analytics
Υποστήριξη Πολλαπλών Καλλιεργειών
Εργαλεία και Πλατφόρμες που Υποστηρίζουν την Πρόβλεψη Απόδοσης
A growing ecosystem of AI tools supports yield forecasting. Notable examples include:
EOSDA Crop Monitoring
| Developer | EOS Data Analytics (EOSDA) |
| Supported Platforms |
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| Language Support | Global coverage with English as primary language; additional languages available by region |
| Pricing Model | Paid platform with tiered plans (Essential, Professional, Enterprise) and optional add-ons including yield estimation |
Overview
EOSDA Crop Monitoring is a precision agriculture platform that leverages satellite imagery, weather data, and machine learning to monitor crop health, predict yields, and enable data-driven farming decisions. Designed for farmers, agronomists, cooperatives, and agribusinesses, it provides remote field assessment, resource planning, and crop performance forecasting at both field and regional scales.
How It Works
The platform uses remote sensing data from satellites (Sentinel-2, PlanetScope, and others) combined with advanced AI models to deliver predictive insights. The yield-prediction module employs two complementary approaches:
- Statistical Model: Machine learning-based predictions trained on historical yield and environmental data
- Biophysical Model: Phenology-driven forecasting using leaf area index assimilation
Data refreshes every 14 days to continuously refine predictions, achieving up to 95% accuracy under optimal conditions. This dual-model approach supports field-level decision making, risk assessment, and long-term agricultural planning.
Key Features
Statistical and biophysical approaches for accurate yield forecasting
Up to 3-month yield predictions with 14-day model recalibration cycles
Satellite-based indices including NDVI, MSAVI, RECI, NDMI, and more
14-day hyperlocal forecasting and comprehensive historical weather data
Variable Rate Application maps combining satellite and machinery data
Field activity logs, scouting tasks, and multi-user team management
Full API access for agritech integration and custom applications
Export maps in TIFF, SHP, and other formats for external analysis
Access the Platform
Getting Started
Sign up for EOSDA Crop Monitoring and select your subscription tier (Essential, Professional, or Enterprise).
Draw field boundaries directly on the map interface or upload existing field boundary files to begin monitoring.
View vegetation indices, water stress, crop classification, and growth stages based on BBCH phenological scales to plan field operations.
Activate the yield-prediction add-on and provide sowing dates, crop varieties, and historical yield data to calibrate models for accurate forecasts.
Export maps in TIFF or SHP formats, generate VRA zone maps, or integrate with your systems via the developer API.
Technical Specifications
| Supported Crops | Over 100 crop types in yield-prediction model |
| Prediction Accuracy | Up to ~95% under optimal data conditions |
| Forecast Horizon | Up to 3 months ahead |
| Data Update Frequency | Every 14 days for model recalibration |
| Satellite Data Sources | Sentinel-2 (10 m resolution), PlanetScope (3 m resolution), and others |
| Vegetation Indices | NDVI, MSAVI, RECI, NDMI, and additional indices |
| Weather Forecasting | 14-day hyperlocal forecasts with historical analytics |
| Export Formats | TIFF, SHP, and other standard GIS formats |
| API Access | Available for satellite imagery, vegetation indices, weather data, and field zoning |
| Infrastructure | Cloud-based platform requiring internet connection |
Important Considerations
- Accuracy depends on data quality, including historical yield records, soil data, and phenological inputs
- Forecast horizon limited to approximately 3 months, making it less suitable for very long-term predictions
- Requires internet access; offline functionality is limited due to cloud-based architecture
- Biophysical model calibration requires user input of sowing dates, crop varieties, and other phenological parameters
- Not suitable for offline or disconnected agricultural operations
Frequently Asked Questions
EOSDA Crop Monitoring supports yield prediction for over 100 crop types, covering most major agricultural commodities and regional crops.
Forecast accuracy can reach up to approximately 95% under optimal conditions, depending on data quality, historical yield records, and proper model calibration.
Model inputs are updated every 14 days, allowing continuous recalibration and refinement of yield predictions throughout the growing season.
Yes. EOSDA provides a comprehensive API that enables integration with custom applications and agritech platforms, offering access to satellite imagery, vegetation indices, weather data, field zoning, and more.
For the statistical model, historical yield data improves accuracy but is not always required. For the biophysical model, you must supply crop variety, sowing dates, and other phenological inputs to maximize forecast precision.
Taranis Ag Intelligence
| Developer | Taranis Inc. |
| Platform | Web-based platform with aerial data capture via drone, plane, and satellite |
| Global Coverage | Operates worldwide with customers in the United States, Europe, Brazil, and beyond |
| Pricing Model | Paid subscription-based service; no public free plan available |
Overview
Taranis Ag Intelligence is a precision-agriculture platform that combines ultra-high-resolution aerial imagery with generative AI to deliver leaf-level crop analysis. The system detects early signs of pests, diseases, nutrient deficiencies, and weed pressure, enabling growers and agronomists to respond proactively. By integrating the Ag Assistant generative AI engine with rich imagery data, Taranis supports yield projection and data-driven decision-making for optimized input use and improved productivity.
How It Works
Taranis deploys a fleet of low-flying aircraft (drones and planes) to capture sub-millimeter resolution images—approximately 0.3 mm per pixel—across crop fields. The AI platform analyzes hundreds of millions of data points to recognize crop stressors including insects, diseases, weeds, and nutritional issues. The Ag Assistant generative AI engine synthesizes this leaf-level data with weather patterns, agronomic research, and crop protection information to generate precise, field-specific insights and recommendations. Recent enhancements include advanced yield-projection algorithms that forecast future crop performance based on detected field health risks.
Key Features
Leaf-level analysis from drone and plane captures at 0.3 mm per pixel resolution
Identifies pests, diseases, nutrient deficiencies, weed pressure, and stand counts automatically
Generative AI that delivers tailored agronomic recommendations and scouting reports
Advanced algorithms forecast crop performance based on leaf-level AI insights
Year-round data capture and full-service monitoring for large-scale operations
Access Taranis
Getting Started
Register with Taranis through their website and select the appropriate service plan for your operation.
Provide field maps or coordinate with Taranis to schedule aerial data capture for your fields.
Taranis flies your fields at scheduled intervals using drones or planes to capture high-resolution imagery.
Imagery is processed using AI algorithms to detect threats and generate actionable insights.
Access generated agronomic reports through Ag Assistant, including recommendations and yield forecasts.
Integrate insights into farm management decisions, including input application, scouting schedules, and crop protection strategies.
Important Considerations
- Requires physical aerial flights (drones or planes), which may limit regional access or increase operational costs
- Handles high data volumes; sub-millimeter imagery requires robust infrastructure and technical expertise
- Data privacy and security must be carefully managed with high-resolution field imagery
- Optimized for advisors, agronomy retailers, and larger operations; smaller farms may have limited direct access
- Yield projections are AI-based and may vary depending on imagery quality and data inputs
- Some AI-generated recommendations may require manual review by agronomists before implementation
- Consistent aerial access may not be feasible in all regions or weather conditions
Frequently Asked Questions
Taranis uses AI-powered yield projection algorithms integrated into Ag Assistant, combining leaf-level imaging data with agronomic information, weather patterns, and field-stress indicators to forecast future crop performance.
Taranis aerial imagery achieves approximately 0.3 mm per pixel resolution, enabling extremely detailed, leaf-level crop analysis and early detection of stressors.
The platform is optimized for advisors, agronomy retailers, and larger operations. While smaller farms may access Taranis through partnerships or cooperative arrangements, direct access depends on the service plan and operational scale.
Ag Assistant is a generative AI engine that processes field imagery, agronomic data, research findings, and weather information to produce tailored agronomy reports and field-specific recommendations.
Yes. By analyzing high-resolution leaf-level images, Taranis detects early signs of pest infestation, disease, nutrient deficiency, and weed pressure, enabling proactive interventions before significant crop damage occurs.
Climate FieldView (Bayer)
| Developer | Bayer (The Climate Corporation) |
| Supported Platforms |
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| Availability | 20+ countries including U.S., Brazil, Canada, Europe, South Africa, Australia, and Turkey |
| Pricing Model | Basic (free) with limited features; paid tiers include Prime, Plus, and Premium for advanced analytics |
Overview
Climate FieldView by Bayer is an AI-driven digital farming platform that unifies agronomic, machine, weather, and satellite data into one intelligent system. By processing billions of data points and 250+ high-definition data layers, it helps farmers gain actionable field insights, predict crop yield, optimize inputs, and make data-driven decisions to maximize return on investment.
How It Works
Climate FieldView aggregates data from tractors, planters, combines, sensors, weather stations, and satellite imagery into a centralized cloud-based platform. Its machine learning models analyze this multilayer data to generate yield forecasts, assess crop health, and provide agronomic recommendations. By integrating with external systems via APIs (such as CLAAS Telematics) and syncing machine data through FieldView Drive, the platform delivers comprehensive farm visibility and predictive insights for planting, crop protection, and harvesting decisions.
Key Features
Machine learning models use historical data, weather patterns, and satellite imagery to predict crop yield with precision.
Satellite-based maps show crop stress, biomass, and field conditions in near real-time for early intervention.
Connects with tractors, combines, and equipment to automatically sync agronomic and yield data.
Scout fields, generate post-harvest yield analysis reports, and export data in PDF or CSV formats.
Supports third-party integrations (CLAAS API, Combyne) and links with grain management platforms.
Access field data and insights from any device via the web platform or iOS mobile app.
Download or Access
Getting Started
Create an account on the Climate FieldView website and select either the free Basic plan or a paid tier (Prime, Plus, Premium) based on your needs.
Insert the FieldView Drive hardware into your machine's diagnostic port to begin streaming machine data to your account.
Import historical data using the Data Inbox or automatically sync via connected machinery, APIs, or weather stations.
Use the web or mobile app to view satellite maps, identify stress zones, and monitor crop conditions throughout the season.
After harvest, use Yield Analysis and Field Region Reports tools to evaluate performance and receive AI-driven predictions for next season.
Export comprehensive reports as PDFs or CSVs to share with agronomists, advisors, or business partners.
Important Considerations
- Fully leveraging the platform typically requires compatible hardware (FieldView Drive) and machine connectivity
- Yield prediction accuracy depends on the quality and completeness of input data (machine data, satellite imagery, weather)
- Some advanced integrations and features may not be available in all regions
- Managing and interpreting large data volumes requires digital literacy and time investment from farmers
Frequently Asked Questions
Climate FieldView uses advanced machine learning algorithms to analyze historical field data, real-time weather patterns, satellite imagery, and machine-generated agronomic data. This multilayer analysis generates accurate yield forecasts to help you plan and optimize your farming operations.
Yes, the Basic plan is completely free and includes essential features such as data storage, field visualization, and data upload capabilities. Paid tiers (Prime, Plus, Premium) unlock advanced analytics, predictive modeling, and premium support.
Absolutely. You can connect your equipment using FieldView Drive hardware or through API integrations (such as CLAAS Telematics). This allows automatic syncing of field work data, yield information, and machine diagnostics directly to your FieldView account.
Climate FieldView is available in over 20 countries worldwide, including the United States, Brazil, Canada, European nations, South Africa, Australia, and Turkey. Availability and feature sets may vary by region.
After harvest, use the Field Region Reports and Yield Analysis features to review field performance data. You can export detailed reports showing yield distribution, input impact analysis, and AI-generated recommendations for optimizing next season's strategy.
AGRIVISION AI
| Developer | AgriVision AI Tech (Nutriyo Agro Foods Pvt Ltd) |
| Supported Platforms |
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| Language Support | Multiple regional languages with voice support; optimized for Indian farmers |
| Pricing Model | Freemium / Paid model; core advisory and monitoring features are part of commercial offering |
Overview
AgriVision AI is an intelligent agritech platform that leverages artificial intelligence, computer vision, and voice technology to deliver real-time crop insights, yield predictions, and pest/disease advisory. Designed specifically for farmers and farmer-producer organizations (FPOs), it combines image-based diagnostics with environmental data and predictive analytics to enhance crop productivity and support better farming decisions.
How It Works
AgriVision AI democratizes access to AI-driven agronomic intelligence through a simple mobile interface. Farmers capture images of their crops, which machine learning models analyze to detect diseases, pests, and nutrient deficiencies. These insights are enhanced with predictive yield models powered by IoT sensors, environmental monitoring, and farmer inputs. The platform features voice-based advisory in local languages, making it accessible to farmers with limited literacy. FPOs and cooperatives gain access to data dashboards for tracking aggregated farm performance and crop health.
Key Features
Detects diseases, pests, and nutrient stress using mobile camera images for accurate crop health assessment.
Uses advanced AI models to forecast crop yield based on environmental data, images, and farmer inputs.
Sends instant notifications for weather updates, pest outbreaks, and disease risks to keep farmers informed.
Provides guidance in multiple regional languages with voice input and output, even in offline mode.
Aggregated insights and decision support tools for farmer producer organizations and cooperatives.
Works without internet connection; syncs data when connectivity is restored for uninterrupted access.
Download or Access
Getting Started
Sign up for AgriVision AI through their website or mobile application using your phone number or email.
Input your farm information, crop type, and sowing dates to establish your farming profile.
Use your phone camera to photograph plant leaves and upload them to the app for AI-based analysis.
Get personalized pest, disease, and nutrient treatment recommendations via text or voice in your local language.
Stay updated with weather alerts and pest/disease risk notifications through the app's alert system.
Use the yield prediction feature to estimate future crop production and plan accordingly.
Farmer producer organizations can access the web dashboard to view aggregated farm data and collective insights.
Important Considerations
Frequently Asked Questions
AgriVision AI uses advanced machine learning models that combine image analysis of your crops, environmental sensor data (weather, soil conditions), and farmer inputs to generate accurate yield forecasts.
Yes, AgriVision AI supports offline operation. You can use core features without internet; however, advisory updates and data synchronization require periodic connectivity.
The platform supports voice input and guidance in multiple regional languages, making it accessible to farmers across different linguistic regions in India.
Absolutely. AgriVision AI is specifically designed for small farmers and FPOs, featuring a simple mobile interface, localized language support, and affordable pricing options.
Yes, the app sends real-time alerts for pest risks, disease outbreaks, and adverse weather conditions to help you take preventive action quickly.
CropX
| Developer | CropX Technologies, Inc. |
| Supported Platforms |
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| Global Availability | Active in 70+ countries worldwide |
| Pricing Model | Paid subscription — requires hardware investment (sensors) plus ongoing platform fees |
Overview
CropX is an AI-powered precision agriculture platform that combines soil sensor data, machine learning, weather intelligence, and satellite imagery to optimize irrigation, fertilizer application, and crop management. By integrating real-time field data with predictive analytics, CropX helps farmers maximize yields, reduce input waste, and improve resource efficiency at scale.
How It Works
CropX deploys a network of soil probes that continuously measure moisture, temperature, and electrical conductivity at multiple depths. This real-time sensor data feeds into the CropX cloud platform, where AI algorithms combine it with local weather patterns, topography, satellite imagery, and farm machinery data to generate actionable agronomic insights. The system uses validated crop models to forecast plant stress, predict disease risk, and calculate water-use efficiency.
A documented field trial demonstrated a 22% yield increase using CropX-driven irrigation by preventing water stress and precisely matching soil water demands.
Key Features
In-field probes monitor moisture, temperature, and electrical conductivity at multiple depths for continuous field insights.
Machine learning models integrate soil, weather, satellite, and machinery data to guide irrigation and fertilization decisions.
Create prescription maps for seeding, fertilizer, and irrigation tailored to field variability and soil conditions.
Optimize irrigation scripts based on soil moisture zones to maximize water efficiency and crop performance.
Import farm machinery data via ISO-XML, CSV, SHP, and TIFF formats for comprehensive field analysis.
Track water savings, nitrogen leaching, and input usage to support efficient and sustainable farming practices.
Download or Access
Getting Started
Deploy CropX probes in your field at designated depths (typically 20 cm and 46 cm) to begin collecting real-time soil data.
Set up data transmission via 4G, Bluetooth, or satellite connectivity to ensure continuous sensor data flow to the cloud platform.
Use the CropX app or web dashboard to define field boundaries and connect additional data sources like weather stations and topography maps.
Upload yield maps, machinery records, and prescription files in ISO-XML, CSV, SHP, or TIFF formats for comprehensive field analysis.
Use the VRA tool to create variable-rate application maps for seeding, fertilizer, and irrigation customized to your field's specific conditions.
Export VRI scripts to your irrigation controller or pivot system, or manually adjust operations based on CropX recommendations.
Track real-time sensor data, satellite vegetation indices, and predictive disease risk alerts on the intuitive dashboard.
After harvest, analyze yield data and field reports to evaluate prescription effectiveness and refine strategies for future seasons.
Important Considerations
- Recurring subscription fees required to access full platform analytics and features
- Connectivity dependency: 4G, Bluetooth, or satellite connectivity needed for reliable data transmission
- Learning curve: interpreting AI-driven insights may require technical knowledge or agronomic expertise
- Prescription export compatibility varies by OEM — not all farm machinery brands are fully supported
Frequently Asked Questions
In documented field trials, CropX-driven irrigation achieved a 22% yield increase by preventing water stress and precisely matching soil water demands to crop needs.
CropX deploys capacitance-based soil probes that measure volumetric water content (moisture), soil temperature, and electrical conductivity (EC) at multiple depths for comprehensive soil profiling.
Yes — CropX supports data import from farm equipment via multiple file formats including ISO-XML, CSV, SHP, and TIFF, enabling seamless integration with most modern machinery systems.
VRA (Variable Rate Application) allows farmers to apply inputs at different rates across a field based on soil and crop variability. CropX generates prescription maps for seeding, fertilizer, and irrigation that account for field-specific conditions, optimizing input efficiency and yield potential.
Yes — CropX's Variable Rate Irrigation (VRI) tool optimizes irrigation scripts based on real-time soil moisture data and field zones, significantly reducing water waste while maintaining optimal crop hydration and performance.
OneSoil
Application Information
| Developer | OneSoil (OneSoil Inc.) |
| Supported Platforms |
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| Language Support | Available globally with multi-language web app support across many regions. |
| Pricing Model | Freemium — basic field monitoring is free; advanced tools like VRA mapping and soil sampling require OneSoil Pro subscription. |
General Overview
OneSoil is an AI-driven precision farming platform that helps growers monitor crop health, analyze productivity zones, and predict yields using satellite imagery and machine learning. It enables farmers to make data-backed decisions by integrating NDVI trends, weather forecasts, and yield data. With both free and Pro tiers, OneSoil supports variable-rate application (VRA), crop rotation planning, and yield analysis — helping maximize returns and minimize waste.
How It Works
OneSoil leverages Copernicus Sentinel-1 and Sentinel-2 satellite imagery to generate NDVI (Normalized Difference Vegetation Index) maps and detect crop development stages. It processes historical NDVI data (up to 6 years) to build productivity zones, which represent field sub-areas with consistent yield potential. These zones enable users to apply variable-rate seeding, fertilization, or spraying via customizable prescription maps.
After harvest, farmers can upload yield maps from their combine to analyze performance, compare with productivity zones, and evaluate the effectiveness of VRA strategies. OneSoil also offers crop rotation planning and weather forecasts (precipitation, growing degree days) to support agronomic decisions over time.
Key Features
Real-time crop health tracking using Sentinel-2 satellite imagery for accurate development stage detection.
Historical NDVI analysis creates yield-potential zones based on elevation and soil brightness patterns.
Create customizable prescription maps for planting, fertilization, and spraying based on productivity zones.
Import combine yield maps and compare performance against VRA prescriptions and NDVI zones.
Automated planning for future seasons based on comprehensive field history and best practices.
7-day forecasts, accumulated precipitation tracking, and growing degree days for informed decisions.
Download or Access
Getting Started Guide
Create an account via the OneSoil web app or download the mobile app for iOS or Android.
Draw or import field boundaries directly on the interactive map interface.
Allow OneSoil to process satellite data (NDVI, elevation, soil brightness) to generate productivity zones.
Select "Create VRA map," choose zone type (historical or NDVI), set zones and rate values, then export your prescription map.
After harvest, upload yield map files from your combine, match attributes (yield, units, timestamp), and generate yield reports.
Compare yield maps with productivity zones or VRA prescriptions to evaluate performance and ROI.
Use the crop rotation tool to document and forecast crop schedules for upcoming seasons.
Important Notes & Limitations
- Yield prediction accuracy improves with uploaded yield data; without it, forecasts are less precise.
- Satellite imagery depends on cloud cover; NDVI data updates may occasionally be delayed.
- Prescription map export may require compatibility with specific machinery and file formats.
Frequently Asked Questions
Yes. OneSoil analyzes NDVI trends, productivity zones, and uploaded yield data to forecast yields and assess field performance accurately.
OneSoil Pro unlocks advanced precision farming tools including VRA map creation, soil sampling maps, control-strip trials, and detailed yield-zone analysis — features unavailable in the free tier.
In the Pro version, navigate to "Create VRA map," select your prescription type (productivity zones or NDVI), configure your crop and application rates, then export the map to your machinery.
Yes, basic field monitoring features are free. Advanced precision farming tools like VRA map creation and control trials require a Pro subscription.
OneSoil relies on Copernicus Sentinel-1 and Sentinel-2 satellite imagery, processed with AI algorithms to derive NDVI metrics and other precision agriculture insights.
Βασικά Συμπεράσματα
- Η AI συνδυάζει δορυφορικές εικόνες, δεδομένα καιρού, αισθητήρες εδάφους και ιστορικά αρχεία για ολοκληρωμένη ανάλυση καλλιεργειών
- Οι αλγόριθμοι μηχανικής μάθησης – από σύνολα δέντρων έως νευρωνικά δίκτυα – παρέχουν ακριβείς προβλέψεις απόδοσης
- Οι υβριδικές προσεγγίσεις και η μεταφορά μάθησης μεγιστοποιούν την ακρίβεια ακόμα και σε περιοχές με περιορισμένα δεδομένα
- Οι παγκόσμιες εφαρμογές καλύπτουν Κένυα, ΗΠΑ, Ευρώπη και Αργεντινή με αποδεδειγμένα αποτελέσματα
- Οι εμπορικές πλατφόρμες καθιστούν πλέον την πρόβλεψη AI προσιτή σε αγρότες και φορείς χάραξης πολιτικής παγκοσμίως
- Η πρόβλεψη απόδοσης με AI βελτιστοποιεί τη διαχείριση καλλιεργειών και ενισχύει την επισιτιστική ασφάλεια
Συμπέρασμα: Η πρόβλεψη απόδοσης καλλιεργειών με AI γίνεται πρακτική πραγματικότητα σε όλες τις περιοχές και καλλιέργειες. Συνδυάζοντας παγκόσμιες δορυφορικές εικόνες, τοπικούς αισθητήρες και δεδομένα κλίματος με ισχυρούς αλγόριθμους ML, οι αναλυτές μπορούν να προβλέψουν τις συγκομιδές εβδομάδες ή και μήνες πριν τη συγκομιδή. Αυτό δίνει τη δυνατότητα σε αγρότες και κυβερνήσεις να σχεδιάζουν πιο αποδοτικά τη φύτευση και τη διανομή, βοηθώντας τελικά να θρέψουν έναν αυξανόμενο κόσμο με βιώσιμο τρόπο.
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