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Fenômica: A Computer Vision System for High-Throughput Phenotyping

Fenômica: A Computer Vision System for High-Throughput Phenotyping
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Author(s): Marcos Roberto dos Santos (Universidade de Passo Fundo, Rio Grande do Sul, Brazil), Guilherme Afonso Madalozzo (Universidade de Passo Fundo, Rio Grande do Sul, Brazil), José Maurício Cunha Fernandes (Universidade de Passo Fundo, Rio Grande do Sul, Brazil) and Rafael Rieder (Universidade de Passo Fundo, Rio Grande do Sul, Brazil)
Copyright: 2020
Volume: 11
Issue: 1
Pages: 22
Source title: International Journal of Agricultural and Environmental Information Systems (IJAEIS)
Editor(s)-in-Chief: Petraq Papajorgji (Canadian Institute of Technology, Tirana, Albania) and François Pinet (Irstea/Cemagref - Clermont Ferrand, France)
DOI: 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.

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