Canopeo

Name: Canopeo
Author: Oklahoma State University

Oklahoma State University

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App Summary

Canopeo is a measurement tool for agricultural professionals and researchers that quantifies the percentage of green canopy cover from photos taken on a mobile device to monitor crop progress and plant phenotype. Based on an algorithm that analyzes color ratios, associated research found a strong linear relationship (R²=0.94) between the app's measurements and the standard light interception method in soybeans and demonstrated 90% pixel classification accuracy compared to standard software. The authors conclude that Canopeo provides a rapid, accurate, and accessible alternative to traditional methods, supporting informed management decisions and research in both field and lab settings.

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Detailed Description

Functionality & Mechanism

Developed at Oklahoma State University, Canopeo quantifies fractional green canopy cover from downward-facing RGB images. The system leverages an algorithm based on color ratios (Red/Green, Blue/Green) and an excess green index to differentiate vegetation from background materials. The interface facilitates image capture or import from a device's photo library. Sessions involve optional slider-based adjustments to refine green pixel classification, with each analysis automatically logged with geographic coordinates and a timestamp for subsequent review and data management.

Evidence & Research Context

A foundational validation study demonstrated the system correctly classified 90% of pixels compared to the SamplePoint software, with an average root mean squared difference (RMSD) of 0.073 across various crops including corn, sorghum, and switchgrass.
In field studies of soybean, canopy cover measurements from Canopeo exhibited a strong linear relationship (R2 = 0.94 for images, R2 = 0.92 for videos) with data from the standard line quantum sensor light interception method.
The tool has been adopted for plant phenotyping in controlled laboratory environments, enabling cost-efficient biomass estimation for model plants (Arabidopsis thaliana) by personnel with limited technical resources.
The system's video analysis capability enables rapid quantification of canopy spatial variability across transects, a function not available in comparable legacy software packages.

Intended Use & Scope

Canopeo is designed for agronomists, researchers, and crop managers as a quantitative field measurement tool. Its primary utility is to monitor crop progress, evaluate turfgrass stands, and assess canopy damage. The system provides a percent cover metric and does not perform causal analysis for changes in vegetation, such as nutrient deficiency or disease identification.

Studies & Publications

3 publications

Peer-reviewed research associated with this app.

Validation Study

Application of Canopeo as a Precision Agriculture Tool in Controlled Environments

Hale et al. (2024) · PLOS ONE

App accurately identified growth differences among plant varieties in controlled settings.

Canopeo app was developed as a simple, accurate, rapid, and free tool to analyze ground cover fraction (GCF) from red-green-blue (RGB) images and videos captured in the field. With increasing interest in tools for plant phenotyping in controlled environments, the usefulness of Canopeo to identify differences in growth among Arabidopsis thaliana mutants in a controlled environment were explored. A simple imaging system was used to compare Arabidopsis mutants based on the FLAVIN-BINDING, KELCH REPEAT, F-BOX-1 (FKF1) mutation, which has been identified with increased biomass accumulation. Two FKF1 lines such as null expression (fkf1-t) and overexpression (FKF1-OE) lines were used along with wild type (Col-0). Canopeo was used to phenotype plants, based on biomass estimations. Under long-day photoperiod, fkf1-t had increased cellulose biosynthesis, and therefore biomass. Resource partitioning favored seedling vigor and delayed onset of senescence. In contrast, FKF1-OE illustrated a determinative growth habit where plant resources are primarily allocated for seed production. This study demonstrates the use of Canopeo for model plants and highlights its potential for phenotyping broadleaved crops in controlled environments. The value of adapting Canopeo for lab use is those with limited experience and resources have access to phenotyping methodology that is simple, accessible, accurate, and cost-efficient in a controlled environment setting.

Validation Study

Soybean Canopy Cover Measured with Canopeo Compared with Light Interception

Shepherd et al. (2018) · Agricultural & Environmental Letters

App measurements of soybean canopy cover closely matched the reference standard method.

Core Ideas The mobile device application Canopeo can be used to measure soybean canopy cover. Canopeo and light interception measurements of canopy cover were linearly related. Canopeo was faster than measuring light interception with the line quantum sensor. Canopy cover is a useful way of monitoring crop productivity, but it can be time‐consuming to measure using light interception. The objective of this research was to evaluate soybean [Glycine max (L.) Merr.] canopy cover measured with Canopeo, a new mobile device application, compared with the light interception method. Data were collected from a soybean planting date by relative maturity study established at three locations. Canopy cover was measured every other week throughout the growing season using pictures and videos analyzed by Canopeo and was compared with light interception measurements using a line quantum sensor. There was a linear relationship between canopy cover measured with pictures (R2 = 0.94) and videos (R2 = 0.92) in Canopeo and light interception. These results suggest Canopeo can be used to take pictures or videos to determine canopy cover as a viable alternative to measuring canopy cover in soybean.

In the Media

Canopeo app provides benefits to urban and rural users

Oklahoma State University's App Center developed Canopeo jointly with plant and soil sciences faculty to quantify green vegetation in plant canopies through smartphone photos. USDA-ARS scientist Corey Moffet praised the app's practicality, stating "It's a quick and very easy app to use" and noting that "there are both iPhone and android versions so almost everyone already has what they need to use it, plus it's free." The app automatically records time, date, and location data while providing accurate percent canopy cover measurements for crops, grassland, and turfgrass.

OkstateRead article

OSU App Evaluates Plants in Real Time

Oklahoma State University developed Canopeo to help researchers evaluate plant health in real time, replacing a tedious process where scientists had to analyze vegetation photos "pixel by pixel" that took "five or 10 minutes for a photo" with "hundreds of photos to process." The app measures the ratio of vegetation to ground by counting green pixels in images to accurately measure green canopy cover. PhD student Andres Patrignani, who developed the initial version, says the app is "particularly useful in the early stages of plant development" for crop consultants and turf management professionals.

PublicradiotulsaRead article

Oklahoma State University releases crop analysis app

Oklahoma State University developed Canopeo to help users quantify green vegetation in plant canopies through smartphone photos, creating the first mobile application developed from concept to release through the OSU App Center. "Green canopy cover is an excellent indication of crop progress, especially early in the growing season," said Tyson Ochsner, Sarkeys Distinguished Professor in Applied Soil Physics with OSU's Division of Agricultural Sciences and Natural Resources. The free app analyzes images to provide accurate percent canopy cover measurements for row crops, grassland, and turfgrass, available for both Apple and Android devices.

OkstateRead article