Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area

Smart agriculture and wildlife monitoring are one of the recent trends of Internet of Things (IoT) applications, which are evolving in providing sustainable solutions from producers. This article details the design, development and assessment of a wildlife monitoring application for IoT animal repelling devices that is able to cover large areas, thanks to the low power wide area networks (LPWAN), which bridge the gap between cellular technologies and short range wireless technologies. LoRa, the global de-facto LPWAN, continues to attract attention given its open specification and ready availability of off-the-shelf hardware, with claims of several kilometers of range in harsh challenging environments. At first, this article presents a survey of the LPWAN for smart agriculture applications. We proceed to evaluate the performance of LoRa transmission technology operating in the 433 MHz and 868 MHz bands, aimed at wildlife monitoring in a forest vegetation area. To characterize the communication link, we mainly use the signal-to-noise ratio (SNR), received signal strength indicator (RSSI) and packet delivery ratio (PDR). Findings from this study show that achievable performance can greatly vary between the 433 MHz and 868 MHz bands, and prompt caution is required when taking numbers at face value, as this can have implications for IoT applications. In addition, our results show that the link reaches up to 860 m in the highly dense forest vegetation environment, while in the not so dense forest vegetation environment, it reaches up to 2050 m

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

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

Novel Proportionate Scrutiny On Crop Protection From Creatures By Deep Learning

The main objective of this paper is to protect the crop from animal attacks. The conventional techniques have the same kind of security applied to all the types of animals detected based on a Passive IR sensor, and only single-stage protection is applied. The images were captured and identified with the help of machine learning and deep learning techniques. The project was designed with a rectangular farm area. On each side of the entrance, the device was installed to capture the image for processing to identify the animals, based on the animal identification, different levels of security were applied, and that will produce different sounds with different Db levels and variety of dazzling light. This work provides a comprehensive description of the design, development, and assessment of an intelligent animal repelling system that allows for to detection and recognition of the animals. The enhancement is done by different levels of protection and different types of protection based on the classified animals. In initial level protection, making the noise and lightning from the opposite side send the animal out of the farm. If the animals are still on the farm, initiating the next stage that the image will send to the owner. The accuracy of all the methods discussed will be compared based on the complexity of the technique, implementation cost, reciprocating time, and accuracy of animal detection. In recent years, edge computing has become an essential technology for real-time application development by moving processing and storage capabilities close to ending devices, thereby reducing latency, improving response time, and ensuring secure data exchange

PERFORMANCE ANALYSIS OF UNDERGROUND-TO-ABOVEGROUND COMMUNICATION IN AGRICULTURAL IOT NETWORKS

This thesis investigates the potential of LoRa, a low-power, wide-area networking technology, for establishing reliable communication between underground sensors and aboveground infrastructure. We comprehensively analyze LoRa\u27s performance in both single-hop and multi-hop configurations, considering the impact of diverse environmental factors such as soil composition, moisture content, underground transmission distance, and path loss on signal propagation. We delve into the crucial role of the spreading factor (SF) within the LoRa communication system, analyzing its influence on network performance. Furthermore, we develop a comprehensive mathematical model for bit error rate (BER) under various channel conditions, including additive white Gaussian noise (AWGN) and Rayleigh fading, encompassing multi-hop networks with decode-and-forward relays. Through simulations employing realistic Rayleigh fading scenarios, we validate the accuracy of our theoretical models. Our key findings highlight the importance of optimizing network parameters, particularly the SF, to achieve superior bit error rate performance, ultimately enhancing the overall network reliability. We demonstrate that multi-hop LoRa networks offer a significant advantage over single-hop configurations, especially in challenging underground environments. This extended reach via multi-hop makes LoRa a compelling technology for large-scale, reliable communication networks in diverse agricultural applications

The Gnu Frontier: deploying machine learning and open-source electronics for the study of ungulate movement in the Anthropocene

The Anthropocene epoch has ushered in unprecedented and irreversible changes in many biomes, resulting in the disruption of ecological functions and processes. These changes are largely driven by the increased human footprint on a planetary scale and global warming. Consequently, various impacts have been documented, including the extinction of flora and fauna, modification of ecosystems into more homogeneous covers (e.g., farmlands), increase in human-dominated landscapes, disruptions of animal migrations, species range shifts, invasions leading to the extermination of native species, and encroachment of protected areas. These widespread ecosystem changes have become a primary concern for researchers and policymakers who must maintain a delicate balance between the persistence of species and their habitats and the promotion of sustainable development. Furthermore, the rate at which these changes are occurring outpaces the evolutionary response of many species. Consequently, gaining insight into how species respond to various ecological disruptions, both within and outside protected areas, is imperative. However, a thorough understanding of animal behaviour and their responses to rapid ecosystem changes remains challenging due to the lack of robust tools for collecting fine-grained data. To address this methodological gap, I first use camera trap data to demonstrate how migratory species in the Serengeti ecosystem are spatially distributed in relation to human activities occurring in the immediate landscapes adjoining protected areas. The results reveal that the species tend to avoid areas transitioning into human-dominated landscapes as opposed to those bordering buffer zones. The results hold significant conservation value and illuminate population-level responses to anthropogenic disturbances. However, camera trap data does not provide individual-level behavioural insights. Consequently, it remains unclear which additional factors and social cues animals may be observing when traversing across habitats with varying threat levels and how these factors influence their behaviour. Camera traps and telemetry tools such as GPS alone cannot provide data required to answer such questions; therefore, a different set of tools is necessary. Leveraging the capabilities of open-source electronics, I present a low-cost system for automated and repeated observation of collared animals. This system consists of a GPS collar, a long range network (LoRa) radio transmitter, and a commercially available low-flying unmanned aerial vehicle (UAV), taking advantage of its built-in capacity to track a stream of GPS points. The system was tested on a small group of ponies and demonstrated its efficacy and performance by collecting data on focal individual as well as information about its nearest neighbours. Furthermore, automated tracking system collects data in bursts of approximately 20 minutes, aligning with the flight time capacity of a fully charged battery. As such, obtaining behavioural data for longer periods is difficult which necessitates a different approach. Given the rapid ecological changes, it is crucial to understand animal behaviour and its perception of the immediate surroundings. For instance, where do animals spend more time being vigilant as opposed to engaging in other restorative activities like resting? Such areas could be regarded as risky from an animal’s perspective. In this study, I developed a near real-time animal behaviour classifier using low-cost open-source electronics, a low-power long-range wide-area network (LoRaWAN) for connectivity, and edge machine learning. The custom-designed animal tracking system records behavioural data, preprocesses it, and classifies it into four classes: grazing, lying, standing, and walking. The predicted behaviour classes are transmitted to the end-user via servers in near real-time. The tracking tool was tested on Serengeti wildebeest and demonstrated its performance by sending both behavioural classes alongside positional data of the collared animal. In this study, I have demonstrated the utility of existing remote tracking tools as well as their limitations in addressing evolving ecological questions in relation to animal behaviour and response to ecosystem perturbations. The methodological approaches presented here have the potential to greatly enhance our understanding of animal ecology. Importantly, the application of novel technologies will empower scientists to enhance existing tools, generate complementary data streams, improve data resolution and quantity, and enrich their overall capabilities to study complex questions. For instance, it improves our ability to collect behavioural and positional data, monitor focal individuals, and track nearest neighbours, and potentially opens up other avenues for scientific applications. The application of open-source electronics creates an opportunity for other researchers to customise the tools as an alternative to commercial devices to address specific questions and potentially result in other valuable innovations

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools