Abstract

The integration of multi-sensor data fusion and edge intelligence within scalable microcontroller-based systems offers transformative potential for smart agriculture. This chapter presents a comprehensive exploration of real-time multi-sensor data acquisition, fusion algorithms, and on-device processing techniques tailored for resource-constrained agricultural environments. Emphasis is placed on modular hardware frameworks adaptable to diverse crop conditions, alongside robust fusion strategies including weighted averaging, fuzzy logic, and Kalman filtering to enhance data reliability and accuracy. A comparative analysis of cloud, edge, and hybrid processing architectures elucidates trade-offs in latency, energy consumption, and computational efficiency pertinent to agricultural applications. Communication protocols optimized for lowbandwidth, energy-efficient data encoding, compression, and transmission are also examined to address challenges inherent to remote field deployments. Performance evaluation metrics and realworld deployment scenarios underscore the practical feasibility and scalability of these systems. The findings provide critical insights for researchers and practitioners aiming to advance autonomous, precise, and sustainable agricultural practices through embedded intelligence. 

Introduction

The increasing demand for sustainable and efficient agricultural practices has catalyzed the integration of advanced technologies such as the Internet of Things (IoT), edge computing, and multi-sensor data fusion into modern farming systems [1]. Smart agriculture leverages these innovations to optimize resource usage, enhance crop yields, and reduce environmental impact [2]. Central to this technological evolution is the deployment of microcontroller-based sensor networks that enable continuous monitoring of diverse agro-environmental parameters including soil moisture, temperature, humidity, and pest activity [3]. These distributed sensors generate voluminous data that require timely and accurate processing to support informed decision-making [4]. Traditional centralized cloud-based processing often struggles with latency, connectivity, and energy challenges, especially in remote farming locations. Hence, embedding intelligence at the network edge, close to the data sources, is emerging as a promising approach to address these constraints [5]. Edge intelligence facilitates real-time data processing and analysis directly on microcontroller platforms, thereby reducing dependence on cloud connectivity and minimizing communication overhead [6]. This localized processing enables swift responses to environmental changes, such as triggering irrigation systems or deploying pest control measures, which are critical for precision farming [7]. Multi-sensor data fusion plays a vital role in synthesizing heterogeneous sensor outputs to derive more reliable and comprehensive insights about the agricultural ecosystem [8]. Fusion algorithms such as weighted averaging, fuzzy logic, and Kalman filtering improve data quality by mitigating noise, handling missing values, and accommodating sensor uncertainties [9]. The combination of edge AI with sophisticated fusion techniques empowers smart agricultural systems to operate autonomously and efficiently under resource constraints [10].