Crop Growth Time-Lapse Images
Buy and sell crop growth time-lapse images data. Daily or weekly field photos showing crop development stages. Crop phenology AI predicts harvest dates from growth stage image sequences.
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Find Me This Data →Overview
What Is Crop Growth Time-Lapse Images?
Crop growth time-lapse images are sequential daily or weekly photographs of agricultural fields that capture crop development stages from planting through maturity. These datasets document appearance changes over time—lighting, plant size, color, and phenological markers—enabling AI models to monitor growth trajectories and predict key farming outcomes. Time-lapse image sequences are particularly valuable because modeling appearance changes across a temporal series delivers far greater impact for growers and breeders compared to static snapshots. Applications range from greenhouse precision agriculture to large-scale field monitoring, with imagery collected using hand-held cameras, smartphones, satellites, or fixed monitoring stations at consistent viewpoints.
Market Data
52 imaging sessions over 108 days
TLC Dataset Temporal Coverage
Source: arXiv
8688 × 5792 pixels per frame
TLC Dataset Image Resolution
Source: arXiv
Images across multiple growth periods for same crop variety
CropDeep Dataset Specialty
Source: MDPI
Who Uses This Data
What AI models do with it.do with it.
Precision Agriculture Management
Monitoring crop health and growth status in greenhouses to make data-driven decisions for PA management and improve operational outcomes.
Crop Phenology & Harvest Prediction
Using image sequences to identify growth stages and predict harvest dates and fruit quality, such as predicting fruit rot in cranberry crops.
Crop Classification & Identification
Training deep learning models to classify and detect crop species and varieties from multi-angle imagery for continental-scale agricultural monitoring.
Plant Phenotyping & Breeding
Analyzing appearance changes over time to support crop breeding programs and trait prediction research.
What Can You Earn?
What it's worth.worth.
Small Dataset (Single Field, <100 images)
Varies
Pricing depends on image resolution, temporal span, and annotation depth required.
Medium Dataset (Multi-Field, 500–5,000 images)
Varies
Factors include growth period coverage and consistency of viewpoint.
Large Annotated Dataset (10,000+ images with phenology labels)
Varies
Higher value with multi-crop varieties, treatment controls, and calibration data.
What Buyers Expect
What makes it valuable.valuable.
Consistent Viewpoint & Framing
Images captured from approximately the same viewpoint and frame boundaries across all sessions to enable temporal comparison and model training.
Photometric Calibration
Mitigation of lighting variations between sessions using reference color checkers or similar standardized calibration methods.
High Resolution & Detail
Sufficient pixel density (e.g., 8688×5792 or equivalent) to capture growth details and enable deep learning model training.
Temporal Frequency & Span
Regular intervals (daily, weekly) over extended periods (8+ weeks) covering key phenological stages for the target crop.
Metadata & Annotations
Timestamps, crop variety, treatment information, growth stage labels, and environmental conditions where available.
Companies Active Here
Who's buying.buying.
Satellite-based crop type mapping and continental-scale agricultural classification using AI and multi-temporal Earth observation data.
Training vision foundation models and deep learning systems for plant phenotyping, yield prediction, and trait analysis from image sequences.
Using CropDeep datasets and similar resources for monitoring crop health, growth, and decision-making in controlled environments.
FAQ
Common questions.questions.
What makes time-lapse crop images more valuable than single photos?
Modeling appearance changes over a time series delivers far greater impact for growers and breeders than static images. Sequential frames reveal growth trajectories, phenological development, and disease progression patterns that enable predictive AI models and better harvest timing decisions.
What resolution should I aim for when collecting crop time-lapse images?
High-resolution imagery (e.g., 8688×5792 pixels) enables better detail capture for deep learning models and phenotyping applications. Resolution must be sufficient to distinguish growth stage changes and crop health indicators across sessions.
How should I handle lighting differences across imaging sessions?
Use photometric calibration techniques such as photographing a Macbeth Color Checker reference card at the beginning of each session. This standardizes color and brightness across sessions, enabling accurate temporal comparisons.
What imaging interval is optimal for tracking crop growth?
Daily or weekly intervals over extended periods (8+ weeks) are ideal for capturing phenological transitions and growth stages. The specific interval depends on the crop type and target growth stages being monitored.
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