Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas
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2021-12-09
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Universidade Federal de Goiás
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For higher productivity and economic yield in sugarcane field, several imaging techniques
using sugarcane field images have been developed. However, the identification and
measurement of gaps in sugarcane field crop rows are still commonly performed manually on
site to decide to replant the gaps or the entire area. Manual measurement has a high cost of
time and manpower. Based on these factors, this study aimed to create a new technique that
automatically identifies and evaluates the gaps along the crop rows in aerial images of
sugarcane fields obtained by a small remotely piloted aircraft. The images captured using the
remotely piloted aircraft were used to generate the orthomosaics of the crop field area and
classified with the algorithm K-Nearest Neighbors to segment the crop rows. The orientation
of the planting rows in the image was found using the filter gradient Red Green Blue. Then,
the crop rows were mapped using the curve adjustment method and overlap the classified
image to detect and measure the gaps along the segment of the planting line.
The technique developed obtained a maximum error of approximately 3% when compared to
the manual method to evaluate the length of the gaps in the crop rows in an orthomosaic with
an area of 8.05 hectares using the method proposed by Stolf, adapted for digital images. The
proposed approach was able to properly identify the spatial position of automatically
generated line segments over manually created line segments. The proposed method was also
able to achieve statistically similar results when confronted with the technique performed
manually in the image for the mapping of rows and identification of gaps for sugarcane fields
with growth 40 and 80 days after planting. The automatic technique developed had a
significant result in the evaluation of the gaps in the crop rows in the aerial images of
sugarcane fields, thus, its use allows automated inspections with high accuracy
measurements, and besides being able to assist producers in making decisions in the
management of their sugarcane fields.
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ROCHA, B. M. Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas. 2022. 121 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2021.