Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Daniel Queirós da Silva, André Silva Aguiar, Filipe Neves dos Santos, Armando Jorge Sousa, Danilo Rabino, Marcella Biddoccu, Giorgia Bagagiolo and Marco Delmastro
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Daniel Queirós da Silva: INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal
André Silva Aguiar: INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal
Filipe Neves dos Santos: INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal
Armando Jorge Sousa: INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal
Danilo Rabino: Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), 10135 Turin, Italy
Marcella Biddoccu: Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), 10135 Turin, Italy
Giorgia Bagagiolo: Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), 10135 Turin, Italy
Marco Delmastro: Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), 10135 Turin, Italy

Agriculture, 2021, vol. 11, issue 3, 1-19

Abstract: Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

Keywords: leaf area index; multi-view stereo; optical sensing; photogrammetry; precision agriculture; structure from motion (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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