AgTech

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Key challenges

AgTech organizations are using AI to improve productivity, adapt to environmental variability, and optimize resource use. Key challenges include:

Field variability

Agricultural data is often unstructured and inconsistent across soil types, crops, and weather conditions.

Data collection complexity

Gathering reliable sensor, drone, and satellite data at scale is labor-intensive.

Labeling costs

Annotating field data for plant health, disease, or yield estimation is time-consuming and domain-specific.

Environmental risk

AI systems must operate reliably in unpredictable, high-variance settings.

Key trends

AI is transforming agriculture by enhancing precision, sustainability, and automation:

Crop monitoring

Computer vision models detect pests, disease, and plant health issues in real time.

Smart irrigation

AI optimizes water usage based on soil, weather, and crop data.

Yield prediction

Predictive models estimate crop performance and guide supply chain planning.

Autonomous equipment

Robotics and AI enable autonomous tractors, harvesters, and drones.

Succeed with CloudFactory

CloudFactory helps AgTech companies overcome messy data, environmental complexity, and workflow inefficiencies:

AI Consulting

We work with AgTech teams to prioritize AI use cases that improve yield, efficiency, and decision-making.

Data Engine

We clean and annotate aerial, sensor, and imagery data to train models for field analysis and automation.

Training Engine

We fine-tune models to adapt to specific crops, geographies, and conditions.

Inference Engine

We monitor AI performance and flag errors or anomalies in real-time operations.

AI Engine

We help bring AgTech AI from pilot to production—scaling with precision, trust, and ROI.

Client Story: A Global AgTech Company

CloudFactory's Data Engine helps an AgTech leader deliver optimized crop yields through faster, more accurate ML models.

Tired of Data Prep? How to Scale Precision Agriculture Insights with High-Quality Data

Discover safer, faster, lower-cost, and innovative ways of working. We also share what we've learned from working with leading precision agtech companies, including scalable data prep and labeling.

Nancy Agosta

25 April 2025

Client Story: Tractor Zoom

With CloudFactory data enrichment, agtech company Tractor Zoom has grown from advertising $2B to $5B in assets annually.

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Navigating the field: Overcoming AI challenges in AgTech

Three challenges facing AgTech companies when building AI-driven agriculture solutions for farmers using innovative technologies.

25 April 2024

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AI-powered labeling: The key to better agtech machine learning models

Vladimir Liashenko

25 January 2024

Client Story: Hummingbird Technologies

Learn how CloudFactory helped Hummingbird Technologies develop crop analytics for farmers around the world.

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Client Story: Agriculture Company

Agriculture Chemicals manufacturer needed high-quality, complex annotations to develop a computer vision service for optimizing crop yield across 9 crop varieties. Seasonality, weather factors, and annotation complexity led to failed attempts with crowdsourcing and BPOs, requiring costly internal manual corrections by PhD experts.

One of our key challenges was tagging all the data we captured and making sense of all it so we could build our models. As mentioned previously, it is highly domain specific knowledge. We had been doing the work in-house but it is very, very time-consuming. The question was how could we teach annotators how to do this without them being agronomists. What we discovered is that you need to iterate and have a continuous exchange of communication, which is something we can do with the CloudFactory team.

Francois Lemarchand

Senior Data Scientist, Hummingbird Technologies

Growing AgTech AI. Precision, Resilience, and Yield.