Color segmentation to measure the percentage of the affected area in leaves with signs of chlorosis (#1113)
Read ArticleDate of Conference
July 17-19, 2024
Published In
"Sustainable Engineering for a Diverse, Equitable, and Inclusive Future at the Service of Education, Research, and Industry for a Society 5.0."
Location of Conference
Costa Rica
Authors
Cuadrado-Jiménez, Zary Luz
Restrepo-Martinez, Eyleen Carolina
Berrio-Bracamonte, Kevin Antonio
Acevedo-Barrios, Rosa
Chavarro-Mesa, Edisson
Rubiano-Labrador, Carolina
Ariza-Rua, Danilo Lusbin
Patiño-Vanegas, Alberto
Abstract
Observing the signs of deterioration caused by environmental pollutants and some phytopathogens in plants, a computational algorithm programmed in Python language was developed using image processing tools to determine the percentage of damage in leaves with signs of chlorosis. In the present study, the following stages were implemented, i) image collection, using bean (Phaseolus vulgaris) plants exposed to perchlorate, and cowpea (Vigna unguiculata) plants affected by the phytopathogenic fungus Rhizoctonia solani AG-1 IA. ii) Image processing, by implementing the OpenCV-Python package that allowed segmentation and binarization of the images. Finally, the result of the binarization was compared with an approximation of a healthy leaf, and the percentage of affected leaf area compared with the healthy leaf obtained. Meanwhile, the timely detection of diseases in plants and crops is a determining factor for the efficiency of agricultural production, as well as the assessment and presence of chemical substances that affect the environment and human health.