Citation :

José Fabián Huamaní Zegarra, German Alberto Echaiz Espinoza, Daniel Domingo Yanyachi Aco Cardenas, Andrés Ortiz Salazar, "Rail-Guided IoT Imaging System for Leaf-Level Monitoring in Small-Scale Citrus Plots," International Journal of Electronics and Communication Engineering, vol. 13, no. 10, pp. 1-15, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I10P101

Abstract

This paper presents a rail-guided IoT imaging system designed to overcome the difficulty of obtaining consistent, leaf-level citrus images for long-term agricultural monitoring by combining deterministic electromechanical positioning, embedded control, edge computing, and cloud-based data management. It incorporates a cable-suspended linear actuation mechanism together with two inclined cameras, motion control based on ESP32 microcontrollers, a Raspberry Pi 5 for processing, and AWS S3 for cloud services to automate the processes of image acquisition, synchronization, storage, and remote visualization. By limiting image capture to a fixed and repeatable path, the platform reduces the variation in viewpoint that is typically found with aerial and ground-mobile robots, thus allowing the systematic production of consistent image datasets for use in plant phenotyping and pest monitoring. The system was implemented and tested in a citrus orchard under real-world operating conditions. Experimental results demonstrate the system's autonomous and stable operation, reliable image acquisition, continuous synchronization with the cloud and repeatable monitoring over long periods of time with a low-cost hardware design suitable for research-scale setups. The platform provides a reproducible embedded imaging setup, which improves the consistency of agricultural image acquisition and enables the development and evaluation of computer vision algorithms for precision agriculture.

Keywords

Citrus crops, Rail-guided imaging, IoT monitoring system, Leaf-level imaging, Precision agriculture, Instance segmentation.

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