The adoption of new sensors for crop monitoring is leading to the acquisition of large amounts of data, which usually are not directly usable for agricultural applications. The 3D point cloud maps of fields and parcels, generated from remotely sensed data, are examples of such big data, which require the development of specific algorithms for their processing and interpretation, with the final aim to obtain valuable information about crop status. This manuscript presents an innovative 3D point cloud processing algorithm for vine row detection and localisation within vineyard maps, based on the detection of key points and a density-based clustering approach. Vine row localisation is a crucial phase in the interpretation of the complex and huge 3D point clouds of agri- cultural environments, which makes it possible to move the focus from a macro level (parcel and plot scale) to a micro level (plants, fruits and branches). The algorithm outputs fully describe the spatial location of each vine row within the whole 3D model of the agricultural environment by a set of key points and an interpolating curve. The algorithm is specifically conceived to be robust and: (i) independent of the adopted airborne sensor used to acquire the in-field data (not requiring a model with colour or spectral information); (ii) able to manage vine- yards with any vine row layout or orientation (such as curvilinear) and (iii) not hindered by the occurrence of missing plants. The experimental results, obtained by processing the models of seven case study parcels, proved the algorithm’s reliability and accuracy: the automatic vine row detection was found to be 100% in accordance with the manual one; and the obtained localisation indices showed an average error of 12 cm and standard deviation of 10 cm, which is fully compatible with the considered agricultural applications. In addition, the algorithm outputs can be profitably exploited for enhanced path planning of autonomous agricultural machines adopted for in-field operations.

3D point cloud density-based segmentation for vine rows detection and localisation

A. Biglia
First
;
P. Gay;D. Ricauda Aimonino;L. Comba
Last
2022

Abstract

The adoption of new sensors for crop monitoring is leading to the acquisition of large amounts of data, which usually are not directly usable for agricultural applications. The 3D point cloud maps of fields and parcels, generated from remotely sensed data, are examples of such big data, which require the development of specific algorithms for their processing and interpretation, with the final aim to obtain valuable information about crop status. This manuscript presents an innovative 3D point cloud processing algorithm for vine row detection and localisation within vineyard maps, based on the detection of key points and a density-based clustering approach. Vine row localisation is a crucial phase in the interpretation of the complex and huge 3D point clouds of agri- cultural environments, which makes it possible to move the focus from a macro level (parcel and plot scale) to a micro level (plants, fruits and branches). The algorithm outputs fully describe the spatial location of each vine row within the whole 3D model of the agricultural environment by a set of key points and an interpolating curve. The algorithm is specifically conceived to be robust and: (i) independent of the adopted airborne sensor used to acquire the in-field data (not requiring a model with colour or spectral information); (ii) able to manage vine- yards with any vine row layout or orientation (such as curvilinear) and (iii) not hindered by the occurrence of missing plants. The experimental results, obtained by processing the models of seven case study parcels, proved the algorithm’s reliability and accuracy: the automatic vine row detection was found to be 100% in accordance with the manual one; and the obtained localisation indices showed an average error of 12 cm and standard deviation of 10 cm, which is fully compatible with the considered agricultural applications. In addition, the algorithm outputs can be profitably exploited for enhanced path planning of autonomous agricultural machines adopted for in-field operations.
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https://www.sciencedirect.com/science/article/pii/S0168169922004835
Smart agriculture, Precision viticulture, Remote sensing from UAV, Photogrammetry, 3D data processing
A. Biglia, S. Zaman, P. Gay, D. Ricauda Aimonino, L. Comba
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1869518
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