Speaker
Description
The use of precision agriculture contrasts with the challenge posed by the high cost of commercial technologies, particularly for small-scale producers. For this reason, it is necessary to develop low-cost, accessible solutions that can be applied directly in the productive environment. Within this context, this work presents the development of a low-cost system for acquiring 3D images of beef cattle, enabling morphometric measurements without displacing the animals from their environment. The validation of the system was carried out through statistical processing, comparing manual measurements with automated ones. The planning of data acquisition included control of internal and external factors that could interfere with collection, as well as detailed recording of experimental conditions to ensure reproducibility.
Tests were conducted to identify capture and processing configurations, including variations in camera positioning, acquisition environment, and the software employed. Image capture was performed using two and three Kinect v2 cameras operating simultaneously, as it was necessary to determine how the number of cameras influenced the 3D reconstruction of the animal, both in terms of shape quality and processing time. The first challenge was to evaluate image quality and identify which conditions directly influenced noise and the accuracy of point clouds. The second challenge involved selecting an efficient method for image fusion, since manual alignment is impractical given the volume of data generated. Subsequently, efforts focused on establishing a processing pipeline capable of operating within reduced timeframes, respecting the practical limitations of farm use.
Once the acquisition and fusion of point clouds were stabilized, the final stage concentrated on extracting body measurements with the objective of relating them to the animal’s live weight and carcass weight. Statistical methods were applied to establish correlations between the measurements and weight. Measurements were performed in the animals’ natural environment, avoiding stress and behavioral changes during capture. Furthermore, the entire system was built using open-source software, eliminating additional costs and reinforcing the feasibility of the solution for small and medium-scale producers. The combination of low cost, operation in real farm environments, and absence of direct contact with the animals positions this system as a practical alternative for morphometric measurements, with potential to integrate into management routines, improve production decisions, and expand access to precision agriculture technologies.
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