Remote sensing applications for crop ﬁeld detection can be used for yield estimation. Many supervised and unsupervised classiﬁcation techniques and segmentation procedures have been studied in this context. However, most work has focused on large scale monoculture crops, as often seen in western countries. In Ecuador, passion fruit is mainly grown on smallholder farms, which differ greatly from large scale agricultural plots in both spatial characteristics and cropping systems. Crop ﬁeld detection for this form of agriculture is less widely studied and poses challenges to the conventional crop detection methods. Therefore, the question arises how existing segmentation and classiﬁcation techniques can be adapted for detecting smallholder passion fruit farms in Ecuador.
In a previous MGI thesis research by Thomas Oosterhuis, a method was developed to detect smallholder passion fruit farms in Ecuador, using red edge UAV imagery. A Fourier transform was used to compute textural features based on the spatial periodicity of the passion fruit rows. These features were used for a supervised pixel-wise classiﬁcation with a classiﬁcation tree. The aim of the current thesis research is to further develop and test methodologies. The exact research content will be defined in cooperation with our contact in Ecuador.
Objectives (to be elaborated)
- Crop detection
- Field delineation
- Identify gaps in passion fruit plantations
- Accuracy assessment
- Crop age/health assessment
- To be elaborated ...
- Bégué, A., Arvor, D., Bellon, B., Betbeder, J., de Abelleyra, D., Ferraz, R. P., Lebourgeois, V., Lelong, C., Simões, M., and Verón, S. R. (2018). Remote sensing and cropping practices: A review. Remote Sensing, 10(1).
- Brus, J., Pechanec, V., and Machar, I. (2018). Depiction of uncertainty in the visually interpreted land cover data. Ecological Informatics, 47:10–13.
- Delenne, C., Durrieu, S., Rabatel, G., and Deshayes, M. (2010). From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data. Computers and Electronics in Agriculture, 70(1):78–83.
- Thesis Thomas Oosterhuis.
- Strong analytical skills (including statistical analysis)
- Scripting skills
Theme(s): Sensing & measuring, Modelling & visualisation