Advanced UAV edge computing ML solutions for livestock management
Tagarakis, Aristotelis Christos; Davarakis, Costas; Loos, Alexander; Kateris, Dimitrios; Bochtis, Dionysis
- Publisher
- Zenodo
- Language
- en
Abstract
The role of Unmanned Aerial Systems (UAS) in different application areas in the agricultural sector is increasing rapidly. Following the current trends, the focus of SPADE EU Project (HE 101060778) is to investigate the potential benefits of UAS contributing to multiple field operations and processes and promote sustainable digital services in the sectors of agriculture, forestry, and livestock. Part of the developments within the SPADE ecosystem involves deployment of UAV formations (single UAV, collaborating UAVs, UAV swarms) equipped with edge-computing devices (AI/ML) for direct applications in livestock management such as detecting focused risks. These developments are being evaluated in real field conditions through the dedicated pilot activities. The current work presents the preliminary results of SPADE livestock use cases undergone two trials to evaluate the performance of UAS-enabled edge-computing AI tools, for ML modelling & data assimilation. The scope is to enable the flying systems for real time monitoring of sheep in open field grazing environments. Within this framework, the SPADE Livestock setup utilized state-of-the-art object detectors and edge computing devices. The Models being investigated included Faster R-CNN, YOLO, and SSD as backbone object detection in aerial images captured by the UAS. The acquired dataset was used to train selected Tiny Object Detection algorithms. The trials included flights at varying altitudes for capturing objects at different scales and evaluate the motion blur due to high-speed at low-altitude flights. The analysis proposed the utilization of TPH-YOLOv5 model, an enhanced version of YOLOv5, where an additional prediction head is introduced to detect objects at different scales. Furthermore, the original prediction heads are replaced with Transformer Prediction Heads (TPH), which leverage a self-attention mechanism to enhance object detection capabilities. Moreover, the convolutional block attention model (CBAM) was integrated into the model to identify attention regions in scenarios with dense objects. The results showed that TPH-YOLOv5 exhibits excellent performance when applied to drone-captured scenarios and thus outperformed competing methods. Specifically, on the DET-test-challenge dataset, TPH-YOLOv5 achieved an average precision (AP) of 39.18%, surpassing the previous state-ofthe-art method by 1.81%. Plans for future work include the implementation of further in-situ pilot trials, to evaluate the real-time response of the system.
Full text
PP01-019 | Advanced UAV edge computing ML solutions for livestock management Aristotelis C. Tagarakis1, Costas Davarakis2, Alexander Loos3, Dimitrios Kateris1, Dionysis Bochtis1 1Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology Hellas (CERTH), Volos, Greece, 2Nydor System Technologies, Athens, Greece, 3Fraunhofer Institute for Digital Media Technology (IDMT), Ilmenau, Germany The role of Unmanned Aerial Systems (UAS) in different application areas in the agricultural sector is increasing rapidly. Following the current trends, the focus of SPADE EU Project (HE 101060778) is to investigate the potential benefits of UAS contributing to multiple field operations and processes and promote sustainable digital services in the sectors of agriculture, forestry, and livestock. Part of the developments within the SPADE ecosystem involves deployment of UAV formations (single UAV, collaborating UAVs, UAV swarms) equipped with edge-computing devices (AI/ML) for direct applications in livestock management such as detecting focused risks. These developments are being evaluated in real field conditions through the dedicated pilot activities. The current work presents the preliminary results of SPADE livestock use cases undergone two trials to evaluate the performance of UAS-enabled edge-computing AI tools, for ML modelling & data assimilation. The scope is to enable the flying systems for real time monitoring of sheep in open field grazing environments. Within this framework, the SPADE Livestock setup utilized state-of-the-art object detectors and edge computing devices. The Models being investigated included Faster R-CNN, YOLO, and SSD as backbone object detection in aerial images captured by the UAS. The acquired dataset was used to train selected Tiny Object Detection algorithms. The trials included flights at varying altitudes for capturing objects at different scales and evaluate the motion blur due to high-speed at low-altitude flights. The analysis proposed the utilization of TPH-YOLOv5 model, an enhanced version of YOLOv5, where an additional prediction head is introduced to detect objects at different scales. Furthermore, the original prediction heads are replaced with Transformer Prediction Heads (TPH), which leverage a self-attention mechanism to enhance object detection capabilities. Moreover, the convolutional block attention model (CBAM) was integrated into the model to identify attention regions in scenarios with dense objects. The results showed that TPH-YOLOv5 exhibits excellent performance when applied to drone-captured scenarios and thus outperformed competing methods. Specifically, on the DET-test-challenge dataset, TPH-YOLOv5 achieved an average precision (AP) of 39.18%, surpassing the previous state-ofthe-art method by 1.81%. Plans for future work include the implementation of further in-situ pilot trials, to evaluate the real-time response of the system. Keywords: Digital Agriculture, Unmanned Aerial Systems, Edge-computing, Machine learning, Livestock management Acknowledgement: This work has been supported by the SPADE project, funded by the European Union’s Horizon Europe Research and Innovation programme within HORIZON-CL6-2021GOVERNANCE-01 under Grant Agreement no. 101060778. 274
