Aprendizado profundo aplicado na pulverização seletiva em tempo real para controle de Ipomoea spp.
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Data
2020-07-21
Autores
Sabóia, Hederson de Souza
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Universidade Federal de Mato Grosso
Resumo
The culture of soybean and cotton have great importance in the Brazilian economic scenario,
both are commodities that move billions of reais per year in exports. The importance is
demonstrated in the increased in planted areas and production year after year, keeping the
country between the world ́s largest producers of crops. The weed management are of
paramount importance, to achieve greater productivity year after year. However, due to the
incorrect use of controls, mainly of herbicides, it has been causing resistance of some biotypes
to the most popular active ingredients. Among the plants that have been representing resistance/
tolerance are those of the genus Ipomoea spp., most popularly known as Morning Glory. These
plants affect soybean and cotton crops throughout their cycle, affecting their productivity. In
this context, the object of this work was to evaluate the implementation of two object detection
algorithms in real time (Faster R-CNN and YOLOv3), and to develop an embedded system for
selective spraying of herbicides. Morning Glory plants in crops soybean and cotton, in the
Cerrado Matogrossense. The project was developed at the Agricultural machinery laboratory
of the Federal University of Mato Grosso, campus of Rondonopolis. The algorithms were
trained to detect three classes (Soybean, Morning Glory and cotton) and evaluated in terms of
precision and recall in the laboratory and field. The laboratory results of the Faster R-CNN
algorithm showed results with an average accuracy of 87.20% and recall 77.20%, while the
YOLOv3 tiny showed 81.16% accuracy and recall 68.00%. In the field tests, Faster R-CNN
showed better results in comparison to YOLOv3 tiny in both modules analyzed, showing weed
control average of 81.70% in cotton and 77.00% in soybean. The YOLOv3 tiny did not present
satisfactory results in the field, presenting results less than 21.00% in the control of Morning
Glory, present in the modules. The spray precision results of the Faster R-CNN demonstrate
that object detection algorithms in real time for the selective control of post-emergent Morning
Glory weeds in soybean and cotton crops.
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SABÓIA, Hederson de Souza. Aprendizado profundo aplicado na pulverização seletiva em tempo real para controle de Ipomoea spp. 2020. 65 f. Dissertação (Mestrado em Engenharia Agrícola) - Universidade Federal de Mato Grosso, Instituto de Ciências Agrárias e Tecnológicas, Rondonópolis, 2020.