Abstract
In recent years, edge computing has become an essential technology for real-time application development by moving processing and storage capabilities close to end devices, thereby reducing latency, improving response time and ensuring secure data exchange. In this work, we focus on a Smart Agriculture application that aims to protect crops from ungulate attacks, and therefore to significantly reduce production losses, through the creation of virtual fences that take advantage of computer vision and ultrasound emission. Starting with an innovative device capable of generating ultrasound to drive away ungulates and thus protect crops from their attack, this work provides a comprehensive description of the design, development and assessment of an intelligent animal repulsion system that allows to detect and recognize the ungulates as well as generate ultrasonic signals tailored to each species of the ungulate. Taking into account the constraints coming from the rural environment in terms of energy supply and network connectivity, the proposed system is based on IoT platforms that provide a satisfactory compromise between performance, cost and energy consumption. More specifically, in this work, we deployed and evaluated various edge computing devices (Raspberry Pi, with or without a neural compute stick, and NVIDIA Jetson Nano) running real-time object detector (YOLO and Tiny-YOLO) with custom-trained models to identify the most suitable animal recognition HW/SW platform to be integrated with the ultrasound generator. Experimental results show the feasibility of the intelligent animal repelling system through the deployment of the animal detectors on power efficient edge computing devices without compromising the mean average precision and also satisfying real-time requirements. In addition, for each HW/SW platform, the experimental study provides a cost/performance analysis, as well as measurements of the average and peak CPU temperature. Best practices are also discussed and lastly, this article discusses how the combined technology used can help farmers and agronomists in their decision making and management process.
Highlights
I N the Agriculture 4.0 era, cutting-edge technologies such as the Internet of Things (IoT), Big Data, Blockchain, Edge/Cloud computing, and Artificial Intelligence (AI) are increasingly used to enable innovative applications that have the potential to revolutionize our daily lives [1]–[3]
The main contributions of this article are: 1) We provide a thorough, complete description of the design, deployment and assessment of an intelligent smart agriculture repelling and monitoring IoT system based on embedded edge AI, to detect and recognize the ungulates, as well as generate ultrasonic signals tailored to each species of the ungulate
EXPERIMENTAL RESULTS AND DISCUSSION firstly some technical details of the training process of the neural network models used by YOLOv3 and Tiny-YOLOv3 are presented, experimental evaluations are discussed for both the models and their deployment on the selected embedded platforms
Summary
I N the Agriculture 4.0 era, cutting-edge technologies such as the Internet of Things (IoT), Big Data, Blockchain, Edge/Cloud computing, and Artificial Intelligence (AI) are increasingly used to enable innovative applications that have the potential to revolutionize our daily lives [1]–[3]. The automation of agricultural production has enabled the continuous monitoring of crop growth and the intelligent management of weeds [5] This helps to provide accurate and efficient solutions to support agricultural activities compared to the traditional methods, which are performed manually, with processes that are time consuming, tedious, increase production costs, and are prone to errors. Due to the nature of our problem and its environs, device portability plays a major role in realizing a real-time animal detection. This is challenging because of the additional constraints in terms of memory footprint and power consumption, which generally conflict with latency and accuracy requirements. This paper will promote the use of embedded devices with low form factor
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