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Canopy Parameters in Coffee Orchards Obtained by a Mobile Terrestrial Laser Scanner
1F. Hoffmann Silva Karp, 2A. Feritas Colaço, 1R. Gonçalves Trevisan, 1J. P. Molin
1. Biosystems Engineering Department, University of São Paulo. Av. Pádua Dias 11, 13418-900, Piracicaba, São Paulo, Brazil
2. CSIRO, Agriculture and Food, Waite Campus, Locked Bag 2, Glen Osmond, SA 5064, Australia

The application of mobile terrestrial laser scanner (MTLS) has been studied for different tree crops such as citrus, apple, olive, pears and others. Such sensing system is capable of accurately estimating relevant canopy parameters such as volume and can be used for site-specific applications and for high throughput plant phenotyping. Coffee is an important tree crop for Brazil and could benefit from MTLS applications. Therefore, the purpose of this research was to define a field protocol for MTLS data collection in commercial coffee orchards and evaluate the spatial variability of canopy parameters. A LiDAR sensor and a RTK-GNSS was used for data acquisition. Two coffee orchards were scanned by a MTLS to test the proposed protocol. The data obtained were processed to generate 3D point clouds of the orchards. Canopy volume and height maps were generated for one of the fields. A minimum distance between the sensor and the canopy of 1m was determined based on the sensor scanning properties. A metal structure was constructed and attached to the three-point hitch of the tractor creating an offset between the sensor and the tractor. Such a design allowed the sensor to be at least 1 m from the canopy. The point clouds showed that for both fields the sensor was able to scan the entire coffee plants. The coefficients of variation of volume and height were 6.5% and 15.7%. The canopy volume and height maps showed that there was spatial variability in the field. Furthermore, according to the geostatiscal analysis, the spatial dependence was limited to short distances. Consequently, the use of sensors such as LiDAR should be preferred over sampling methods for a good representation of the orchard spatial variability. 

Keyword: 3D modeling, LiDAR, Site-specific management