Abstract
Weed control in precision agriculture refers to the design of site-specific control treatments according to weed coverage and it is very useful to minimise costs and environmental risks. The crucial component is to provide precise and timely weed maps via weed monitoring. This paper compares different approaches for weed mapping using imagery from Unmanned Aerial Vehicles in sunflower crops. We explore different alternatives, such as object-based analysis, which is a strategy that is spreading rapidly in the field of remote sensing. The usefulness of these approaches is tested by considering support vector machines, one of the most popular machine learning classifiers. The results show that the object-based analysis is more promising than the pixel-based one and demonstrate that both the features related to vegetation indexes and those related to the shape of the objects are meaningful for the problem.
This work was financed by the Recupera 2020 Project (Spanish MINECO and EU-FEDER Funds). Research of Mr. Torres-Sánchez and Dr. Peña was financed by the FPI and Ramón y Cajal Programs, respectively. Research of Dr. Gutiérrez and Dr. Hervás-Martínez has been subsidized by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P11-TIC-7508 project of the “Junta de Andalucía” (Spain).
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Pérez-Ortiz, M., Gutiérrez, P.A., Peña, J.M., Torres-Sánchez, J., Hervás-Martínez, C., López-Granados, F. (2015). An Experimental Comparison for the Identification of Weeds in Sunflower Crops via Unmanned Aerial Vehicles and Object-Based Analysis. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_22
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DOI: https://doi.org/10.1007/978-3-319-19258-1_22
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