Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow
Auat Cheein, Fernando A.
MetadataShow full item record
Yield monitoring and geometric characterization of crops provide information about orchard variability and vigor, enabling the farmer to make faster and better decisions in tasks such as irrigation, fertilization, pruning, among others. When using LiDAR technology for fruit detection, fruit occlusions
are likely to occur leading to an underestimation of the yield. This work is focused on reducing the fruit occlusions for LiDAR-based approaches, tackling the problem from two different approaches: applying forced air flow by means of an air-assisted sprayer, and using multi-view sensing. These approaches are evaluated in fruit detection, yield prediction and geometric crop characterization. Experimental tests were carried out in a commercial Fuji apple (Malus domestica Borkh. cv. Fuji) orchard. The system was able to detect and localize more than 80% of the visible fruits, predict the yield with a root mean square error lower than 6% and characterize canopy height, width, cross-section area and leaf area. The forced air flow and multi-view approaches helped to reduce the number of fruit occlusions, locating 6.7% and 6.5% more fruits, respectively. Therefore, the proposed system can potentially monitor the yield and characterize the geometry in apple trees. Additionally, combining trials with and without forced air flow and multi-view sensing presented significant advantages for fruit detection as they helped to reduce the number of fruit occlusions.
Is part ofComputers and Electronics in Agriculture, 2020, vol. 168, article number 105121
The following license files are associated with this item:
Showing items related by title, author, creator and subject.
LFuji-air dataset: annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions Gené Mola, Jordi; Gregorio López, Eduard; Auat Cheein, Fernando A.; Guevara, Javier; Llorens Calveras, Jordi; Sanz Cortiella, Ricardo; Escolà i Agustí, Alexandre; Rosell Polo, Joan Ramon (Elsevier, 2020)This article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth. A mobile terrestrial laser scanner (MTLS) comprised of a LiDAR ...
Gené Mola, Jordi; Gregorio López, Eduard; Guevara, Javier; Auat Cheein, Fernando A.; Sanz Cortiella, Ricardo; Escolà i Agustí, Alexandre; Llorens Calveras, Jordi; Morros Rubió, Josep Ramon; Ruiz Hidalgo, Javier; Vilaplana Besler, Verónica; Rosell Polo, Joan Ramon (Academic Press. Published by Elsevier, 2019-09-21)The development of reliable fruit detection and localization systems provides an opportunity to improve the crop value and management by limiting fruit spoilage and optimised harvesting practices. Most proposed systems for ...
Sanz Cortiella, Ricardo; Llorens Calveras, Jordi; Escolà i Agustí, Alexandre; Arnó Satorra, Jaume; Ribes Dasi, Manuel; Masip Vilalta, Joan; Camp, Ferran; Gràcia Aguilà, Felipe José; Solanelles Batlle, Francesc; Planas de Martí, Santiago; Pallejà Cabrè, Tomàs; Palacín Roca, Jordi; Gregorio López, Eduard; Del Moral Martínez, Ignacio; Rosell Polo, Joan Ramon (Molecular Diversity Preservation International (MDPI), 2011)In this work, a LIDAR-based 3D Dynamic Measurement System is presented and evaluated for the geometric characterization of tree crops. Using this measurement system, trees were scanned from two opposing sides to obtain two ...