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Smart tree crop sprayer sensing system utilizing sensor fusion and artificial intelligence
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: 2022 ASABE Annual International Meeting 2200585.(doi:10.13031/aim.202200585)
Authors: Lucas Costa, Yiannis Ampatzidis
Keywords: Artificial Intelligence, Sensor fusion, Smart machine, Variable Rate Applications
This study evaluates the resolution of the lidar sensor between a low-end and high-end sensor on the capabilities to measure tree height and canopy density. Using the higher resolution lidar obtained slightly higher precision on height measurements (94% on low resolution, 95% on high resolution). The field-of-view (FOV) of the camera sensors is evaluated on the classification models capabilities for tree health condition. A convolutional neural network (CNN) was used to perform tree classification with an average accuracy of 84% in classifying the collected imagery into mature, young, dead, and non-tree objects. A larger FOV provided a similar accuracy for the models, but a higher reliability on different grove conditions, as it guarantees that the system can see the whole tree regardless of crop size and grove tree spacing.(Download PDF) (Export to EndNotes)