The commercial deployment of 5G networks require heterogeneous multi-tier, multiple radio access technologies (RATs) to support vehicle-to-infrastructure (V2I) communication with diversified services. Vehicles may need to cross a number of heterogeneous networks of various sizes before reaching the destination. Due to high-speed travel, vehicles may quickly move in and out of the network coverage areas while performing handover. Fast and efficient selection of an appropriate underlying network is critical for seamless handover performance. In this paper we propose a novel network selection mechanism for improved handover performance in V2I communication over heterogeneous wireless network. The idea is for vehicles to self-evaluate a candidate list of access points (AP) that are located in the vehicle movement direction and select the best underlying candidate network based on key criteria, like, the distance between target candidate and the trajectory of the vehicle movement as well as the vehicle mobility information. Fuzzy logic inference system is used to decide whether a target candidate is suitable for handover. Experimental results show that for a vehicle moving at 30km=h, an AP of 100m radius should be located at less than 30m from the road, while this distance is limited to 15m when the vehicle speed is 60km/h.

In response to the persistent challenges posed by fall armyworm (FAW) outbreaks in Rwanda’s maize production since 2017, this research introduces an innovative strategy integrating fuzzy logic and neural network methodologies, originally developed for hydrometeor identification. The focus is on distinguishing flying adult FAW moths using four polarimetric radar parameters: horizontal reflectivity (DBZHC), correlation coefficient (RHOHV), differential reflectivity (ZDR), and specific differential phase (KDP). Demonstrating a remarkable accuracy with a fraction of echoes correctly identified (FEI) of 98.42% for FAW and 87.02% for other weather phenomena, validated by a Heidke skill score (HSS) of 0.9801, the system proves adept at discerning between weather and nonweather events. A significant strength of the developed method lies in its ability to detect FAW adult moths approximately four weeks earlier than ground-based observations identifying infestation outbreaks, This was evident in the context of FAW infestation in maize fields within the surveyed districts of Nyanza, Huye, and Gisagara in the Southern Province of Rwanda. This positions the weather radar method as a promising early warning system for FAW outbreaks, especially beneficial in less-monitored regions like East Africa. The study underscores the potential application of polarimetric C-band Doppler weather radar, providing valuable insights into the intricate dynamics of agricultural insect pest outbreaks. The method offers practical solutions for timely interventions and enhanced crop management strategies, contributing to more effective pest control and promoting sustainable agriculture practices.

The activities of birds present increasing challenges in agriculture, aviation, and environmental conservation. This has led to economic losses, safety risks, and ecological imbalances. Attempts have been made to address the problem, with traditional deterrent methods proving to be labour-intensive, environmentally unfriendly, and ineffective over time. Advances in artificial intelligence (AI) and the Internet of Things (IoT) present opportunities for enabling automated real-time bird detection and repellence. This study reviews recent developments (2020–2025) in AI-driven bird detection and repellence systems, emphasising the integration of image, audio, and multi-sensor data in IoT and edge-based environments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used, with 267 studies initially identified and screened from key scientific databases. A total of 154 studies met the inclusion criteria and were analysed. The findings show the increasing use of convolutional neural networks (CNNs), YOLO variants, and MobileNet in visual detection, and the growing use of lightweight audio-based models such as BirdNET, MFCC-based CNNs, and TinyML frameworks for microcontroller deployment. Multi-sensor fusion is proposed to improve detection accuracy in diverse environments. Repellence strategies include sound-based deterrents, visual deterrents, predator-mimicking visuals, and adaptive AI-integrated systems. Deployment success depends on edge compatibility, power efficiency, and dataset quality. The limitations of current studies include species-specific detection challenges, data scarcity, environmental changes, and energy constraints. Future research should focus on tiny and lightweight AI models, standardised multi-modal datasets, and intelligent, behaviour-aware deterrence mechanisms suitable for precision agriculture and ecological monitoring.

Crop damage attributed to pest birds is an important problem, particularly in low-income countries. This paper describes a prototype system for pest bird detection using a Conv1D neural network model followed by scaring actions to reduce the presence of pest birds on farms. Acoustic recorders were deployed on farms for data collection, supplemented by acoustic libraries. The sounds of pest bird species were identified and labeled. The labeled data were used in Edge Impulse to train a tinyML Conv1D model to detect birds of interest. The model was deployed on Arduino Nano 33 BLE Sense (nodes) and XIAO (Base station) microcontrollers to detect the pest birds, and based on the detection, scaring sounds were played to deter the birds. The model achieved an accuracy of 96.1% during training and 92.99% during testing. The testing F1 score was 0.94, and the ROC score was 0.99, signifying a good discriminatory ability of the model. The prototype was able to make inferences in 53 ms using only 14.8 k of peak RAM and only 43.8 K of flash memory to store the model. Results from the prototype deployment in the field demonstrated successful detection and triggering actions and SMS messaging notifications. Further development of this novel integrated and sustainable solution will add another tool for dealing with pest birds.