Journal
APPLICATIONS IN PLANT SCIENCES
Volume 2, Issue 7, Pages -Publisher
BOTANICAL SOC AMER INC
DOI: 10.3732/apps.1400033
Keywords
Arabidopsis; digital images; leaf area; Python
Categories
Funding
- National Science Foundation [IOS-1358675]
- Direct For Biological Sciences
- Division Of Integrative Organismal Systems [1358675] Funding Source: National Science Foundation
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Premise of the study: Measurement of leaf areas from digital photographs has traditionally required significant user input unless backgrounds are carefully masked. Easy Leaf Area was developed to batch process hundreds of Arabidopsis rosette images in minutes, removing background artifacts and saving results to a spreadsheet-ready CSV file. Methods and Results: Easy Leaf Area uses the color ratios of each pixel to distinguish leaves and calibration areas from their background and compares leaf pixel counts to a red calibration area to eliminate the need for camera distance calculations or manual ruler scale measurement that other software methods typically require. Leaf areas estimated by this software from images taken with a camera phone were more accurate than ImageJ estimates from flatbed scanner images. Conclusions: Easy Leaf Area provides an easy-to-use method for rapid measurement of leaf area and nondestructive estimation of canopy area from digital images.
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