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Regression applied to measure normalized difference vegetation index in soybean images with visible color spaces collected by smartphones2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC50364.2021.9459862
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An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data ManagementPlant Functional Genomics10.1007/978-1-0716-3778-4_1

Fenômica: A Computer Vision System for High-Throughput Phenotyping

Marcos Roberto dos Santos, Guilherme Afonso Madalozzo, José Maurício Cunha Fernandes, Rafael Rieder

Source Title: International Journal of Agricultural and Environmental Information Systems (IJAEIS)11(1)

ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781799806936|DOI: 10.4018/IJAEIS.2020010101

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MLA

Roberto dos Santos, Marcos, et al. "Fenômica: A Computer Vision System for High-Throughput Phenotyping." IJAEIS vol.11, no.1 2020: pp.1-22. http://doi.org/10.4018/IJAEIS.2020010101

APA

Roberto dos Santos, M., Madalozzo, G. A., Fernandes, J. M., & Rieder, R. (2020). Fenômica: A Computer Vision System for High-Throughput Phenotyping. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 11(1), 1-22. http://doi.org/10.4018/IJAEIS.2020010101

Chicago

Roberto dos Santos, Marcos, et al. "Fenômica: A Computer Vision System for High-Throughput Phenotyping," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 11, no.1: 1-22. http://doi.org/10.4018/IJAEIS.2020010101

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Abstract

Computer vision and image processing procedures could obtain crop data frequently and precisely, such as vegetation indexes, and correlating them with other variables, like biomass and crop yield. This work presents the development of a computer vision system for high-throughput phenotyping, considering three solutions: an image capture software linked to a low-cost appliance; an image-processing program for feature extraction; and a web application for results' presentation. As a case study, we used normalized difference vegetation index (NDVI) data from a wheat crop experiment of the Brazilian Agricultural Research Corporation. Regression analysis showed that NDVI explains 98.9, 92.8, and 88.2% of the variability found in the biomass values for crop plots with 82, 150, and 200 kg of N ha1 fertilizer applications, respectively. As a result, NDVI generated by our system presented a strong correlation with the biomass, showing a way to specify a new yield prediction model from the beginning of the crop.