Federated Edge Learning for Distributed Weed Detection in Precision Agriculture Using Multimodal Sensor Fusion
Year: 2025
Type: conference-paper
Abstract: In this work, our goal is to develop a privacy-preserving distributed weed detection and management system. The proposed work integrates FEL (Federated Learning) and deep learning with multi-modal sensor fusion to enhance the model’s performance while minimising data transfer, latency, and energy consumption. In this study, we used multimodal sensors, such as LiDAR (Light Detection and Ranging), RGB (Red–Green–Blue) cameras, multispectral imaging devices, and soil moisture sensors placed in controlled agricultural plots. Deep learning models, such as Convolutional Neural Networks (CNNs), LSTM–CNN hybrids, and Vision Transformers, were trained using standardized parameters. A proposed Federated CNN (FedCNN) was deployed across multiple edge devices, each locally trained on sensor data without exchanging raw data, using FedAvg and FedProx algorithms. The experimental work revealed that the model FedCNN performed well in comparison to other models and achieved the highest accuracy of 94.1%, precision of 94.3%, recall of 93.9%, F1-score of 94.1%, and AUC of 94.1% during hybrid fusion strategies. We compared the centralized and federated learning performance.
Authors: Dasaradha Arangi, Neelamadhab Padhy
Institution: GIET University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
OpenAlex ID: W4417194339