Towards Autonomous Raised Bed Flower Pollination with IoT and Robotics

Strawberries, a high-value crop with growing demand, face increasing challenges from labour shortages, declining pollinator populations, and the limitations of inconsistent manual pollination. This paper presents an IoT-enabled robotic system designed to automate strawberry pollination in open-field raised-bed environments with minimal human intervention. The system consists of a mobile rover equipped with an ESP32-CAM for image capture and a robotic arm mounted on an Arduino Uno, capable of controlled X, Y, and Z positioning to perform targeted pollination. Images of strawberry beds are transmitted to a locally deployed server, which uses a lightweight detection model to identify flowers. System components communicate asynchronously via HTTP and I2C protocols, and the onboard event-driven architecture enables responsive behaviour while minimizing RAM and power usage, which is an essential requirement for low-cost, field-deployable robotics. The server also manages multi-rover scheduling through a custom priority queue designed for low-end hardware. In controlled lo0ad tests, the scheduler improved average response time by 6.9% and handled 2.4% more requests compared to the default queueing system, while maintaining stability. Preliminary field tests demonstrate successful flower identification and reliable arm positioning under real-world conditions. Although full system yield measurements are ongoing, current results validate the core design’s functional feasibility. Unlike previous systems that focus on greenhouse deployments or simpler navigation approaches, this work emphasizes modularity, affordability, and adaptability for small and medium farms, particularly in resource-constrained agricultural regions such as Sri Lanka. This study presents a promising step toward autonomous and scalable pollination systems that integrate embedded systems, robotics, and IoT for practical use in precision agriculture.

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Published
2025-11-07
DOI
https://doi.org/10.3390/ecsa-12-26572
Primary Topic
Smart Agriculture and AI
Type
article
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article

Towards Autonomous Raised Bed Flower Pollination with IoT and Robotics

Kapila Dissanayaka, Bhagya Nathali Silva, Jagath Wickramarathne, Ruchire Eranga Wijesinghe et al.
Smart Agriculture and AI
article

Towards Autonomous Raised Bed Flower Pollination with IoT and Robotics

Kapila Dissanayaka, Bhagya Nathali Silva, Jagath Wickramarathne, Ruchire Eranga Wijesinghe, M. Alavi, Rusira Thamuditha Karunarathna, Chamath Shanaka Wickrama Arachchi
article en

Abstract

Strawberries, a high-value crop with growing demand, face increasing challenges from labour shortages, declining pollinator populations, and the limitations of inconsistent manual pollination. This paper presents an IoT-enabled robotic system designed to automate strawberry pollination in open-field raised-bed environments with minimal human intervention. The system consists of a mobile rover equipped with an ESP32-CAM for image capture and a robotic arm mounted on an Arduino Uno, capable of controlled X, Y, and Z positioning to perform targeted pollination. Images of strawberry beds are transmitted to a locally deployed server, which uses a lightweight detection model to identify flowers. System components communicate asynchronously via HTTP and I2C protocols, and the onboard event-driven architecture enables responsive behaviour while minimizing RAM and power usage, which is an essential requirement for low-cost, field-deployable robotics. The server also manages multi-rover scheduling through a custom priority queue designed for low-end hardware. In controlled lo0ad tests, the scheduler improved average response time by 6.9% and handled 2.4% more requests compared to the default queueing system, while maintaining stability. Preliminary field tests demonstrate successful flower identification and reliable arm positioning under real-world conditions. Although full system yield measurements are ongoing, current results validate the core design’s functional feasibility. Unlike previous systems that focus on greenhouse deployments or simpler navigation approaches, this work emphasizes modularity, affordability, and adaptability for small and medium farms, particularly in resource-constrained agricultural regions such as Sri Lanka. This study presents a promising step toward autonomous and scalable pollination systems that integrate embedded systems, robotics, and IoT for practical use in precision agriculture.

Sri Lanka Institute of Information Technology (LK)
Openalex Percentile: Top 71%
Smart Agriculture and AI
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