BIOENERGETIC CULTURES: ESTIMATE OF OIL CONTENT IN MACAW PALM VIA COMPUTER VISION

Costa, Anderson G.; Oliveira, Maria Clara T. M. de; Carvalho, João Célio L. de; Pinto, Francisco de A. de C.; Motoike, Sérgio Y.

doi:10.1590/1809-4430-Eng.Agric.v43nepe20220105/2023

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Scientific Paper, Special Issue: Energy in Agriculture • Eng. agríc. 43 (spe) • 2023 • https://doi.org/10.1590/1809-4430-Eng.Agric.v43nepe20220105/2023 copy

BIOENERGETIC CULTURES: ESTIMATE OF OIL CONTENT IN MACAW PALM VIA COMPUTER VISION

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Macaw palm (Acrocomia aculeata) is a potential raw material for biodiesel production owing to its high oil content. In this study, a model is created that can estimate the oil percentage in Macaw fruit using spectral indices obtained via computer vision. Ninety Macaw fruits are used to perform an experiment. They are categorized into three stages of maturation and counted in terms of weeks after flowering. Images of the fruits are captured using a multispectral camera in the visible spectral region (RGB) and infra-red regions (NIR), which allows spectral indices to be obtained. A model is established using the principal component regression method and used to estimate the oil content based on the colorimetric characteristics of Macaw palm fruits. The selected model is composed of the first two principal components, which indicate an explanatory potency of 91.57% of the variance. Applying this information to the validation data, the model exhibits a determination coefficient of 0.91, mean square error of 10.61%, and standard error of 5.46% in terms of the oil content, which indicates that computer vision is a promising alternative method for estimating oil content.

bioenergetic matrix; biodiesel; digital images principal component analysis

Associação Brasileira de Engenharia Agrícola SBEA - Associação Brasileira de Engenharia Agrícola, Departamento de Engenharia e Ciências Exatas FCAV/UNESP, Prof. Paulo Donato Castellane, km 5, 14884.900 | Jaboticabal - SP, Tel./Fax: +55 16 3209 7619 - Jaboticabal - SP - Brazil
E-mail: revistasbea@sbea.org.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] 1*Corresponding author. Universidade Federal Rural do Rio de Janeiro/Seropédica - RJ, Brasil. E-mail: acosta@ufrrj.br | ORCID ID: https://orcid.org/0000-0003-0594-8514

Index name	Equations
Normalized difference	$N D V I r = \frac{(N I R - R)}{(N I R + R)}$

Green normalized difference	$N D V I g = \frac{(N I R - G)}{(N I R + G)}$

Ratio between red - near infrared	$R N I R = \frac{R}{N I R}$

Ratio between green - near infrared	$G N I R = \frac{G}{N I R}$

Plant pigment ratio	$P P R = \frac{(G - B)}{(G + B)}$

Green-red normalized difference	$N D G R I = \frac{(G - R)}{(G + R)}$

	Mean	Median	Minimum	Maximum	CV (%)	Kurtosis	Skewness
NDVIr	0.09	0.06	0.02	0.25	66.49	0.39	1.36
NDVIg	0.11	0.08	0.03	0.27	64.56	0.27	1.30
RNIR	0.83	0.88	0.60	0.96	11.63	0.20	-1.27
GNIR	0.81	0.87	0.58	0.95	13.11	0.07	-1.19
PPR	0.12	0.11	0.05	0.21	28.25	1.62	0.91
NDGRI	-0.02	-0.01	-0.05	0.03	99.57	-0.07	-0.14

	PC1	PC2	PC3	PC4	PC5	PC6
Variance	4.58	0.92	0.50	1.33E-04	3.37E-04	4.81E-07
EP (%)	76.32	15.26	8.40	0.02	0.0056	8.02E-06
AEP (%)	76.32	91.57	99.97	99.99	~100.00	100.00
Correlation between PCs and original variables
NDVIr	-0.978	0.081	0.194	0.020	0.001	~ 0.00
NDVIg	-0.995	-0.064	0.069	0.015	0.003	~ 0.00
RNIR	0.975	-0.072	-0.208	0.012	0.014	~ 0.00
GNIR	0.993	0.088	-0.075	0.023	-0.011	~ 0.00
PPR	-0.549	0.712	-0.437	0.001	~ 0.00	~ 0.00
NDGRI	0.628	0.620	0.4711	0.001	0.003	~ 0.00

	Coefficient	Std. error	p-value	R²
Constant	39.11	1.11	2.69E-40
PC1	-2.41	0.52	1.81E-05	0.67
PC2	-15.63	1.23	3.06E-18	0.08

	Number of samples	R²	MSE	Standard error
Calibration set data	60	0.75	7.33	2.86
Validation set data	30	0.91	10.61	5.46