Fenômica: A Computer Vision System for High-Throughput Phenotyping

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, conside...

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Bibliographic Details
Published in:International journal of agricultural and environmental information systems 2020-01, Vol.11 (1), p.1-22
Main Authors: Roberto dos Santos, Marcos, Madalozzo, Guilherme Afonso, Fernandes, José Maurício Cunha, Rieder, Rafael
Format: Article
Language:English
Subjects:
Case studies
Computer peripherals industry
Crop yields
Fertilizers
Image processing
Internet software
Machine vision
Wheat
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Summary: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.
ISSN:1947-3192
1947-3206
DOI:10.4018/IJAEIS.2020010101