AI for Satellite-Based Crop Classification and Yield Forecasting

Optimizing Crop Monitoring

Use Case Description

Anna Keller, Lead Data Scientist at a major agricultural monitoring business, is tasked with improving the accuracy and scalability of satellite-based crop classification. Reliable field-level land use detection is critical for optimizing yield forecasts and managing insurance risks.

The company currently achieves 90% overall classification accuracy using an in-house model trained on Sentinel-2 data. However, misclassifications in mixed-use areas (e.g. crop–forest boundaries) lead to poor yield estimation and operational inefficiencies. The goal is to introduce a third-party AI assistant that can classify parcels more accurately, especially across diverse soil and climate zones in Europe.

Key requirements:

• Model must run within the company’s secure cloud
• Must reach ≥94% classification accuracy on held-out tiles for 9 classes (8 crop types + non-crop, evenly balanced)
• Must be robust across time of year, cloud conditions, and land heterogeneity

The company owns a high-quality internal dataset: ~30.000 geo-tagged Sentinel-2 image tiles labeled with crop types. Rather than building a new model from scratch, Anna decides to evaluate three commercial AI vendors offering pretrained satellite vision models.

Step 1: Vendor Model Metrics

Each vendor submits a technical proposal for a plug-in satellite image classifier:

Anna and her team prioritize overall classification accuracy since even a 1% error can misclassify thousands of hectares, directly impacting crop input planning and insurance rates.

Step 2: Secure Evaluation and Fine-Tuning

Using tracebloc, Anna sets up a sandbox environment in the company's private cloud. Raw satellite imagery remains on internal infrastructure. Vendors are invited to fine-tune their models securely without direct data access.

Each vendor receives:

• 25.000 annotated tiles for training
• 5.000 held-out tiles for testing
• Target metrics: overall accuracy, inference cost

After an initial baseline run, vendors fine-tune their models using tracebloc’s secure API and submit new versions for evaluation. Real-world performance varies significantly compared to initial vendor claims.

Observed Results After Testing

Surprise outcome: Vendor B’s model, while initially weaker than A and C, responds better to fine-tuning and performs best overall on the company’s dataset.

Step 3: Business Case

Assumptions:

• Annual coverage: 1,5 million km² farmland
• Misclassification cost: €20 per km² (input misallocation, yield loss, regulatory errors)
• Internal upkeep, €300.000 per year for maintenance and labeling 
• AI license priced per km² per year: weekly cadence, fully automated
• Target: reduce error rate from 10% (internal baseline) to below 6%

Updated Business Case: Satellite-Based Crop Classification

Conclusion: With updated per-km² pricing, vendor B becomes even more attractive, delivering the lowest total cost and best accuracy-to-cost ratio. 
Without tracebloc, Anna might have defaulted to Vendor C, which looked cheapest on paper. That choice would have burned an extra €1,14 million compared to Vendor B after fine tuning.

Step 4: Vendor Selection and Strategy

After secure fine-tuning, Vendor B delivers the best balance of classification accuracy and cost. The chosen strategy combines:

• Fully automated classification with vendor B’s model
• Real-time API integration with the internal platform

The company proceeds with a 6-month production pilot.

Disclaimer:

The persona, figures, performance metrics, and financial assumptions in this case study are fictional and simplified to reflect realistic industry logic. This case is designed to illustrate AI benchmarking in agribusiness and does not reflect actual vendor performance or contractual outcomes.