Development and Testing of a Locally Manufactured Forestation Robot for Enhanced Seeding Efficiency in Bangladesh

DOI

10.2991/978-94-6463-884-4_83How to use a DOI?

Keywords

Machine learning; Locally manufactured; Forestation robot; Automation; Seeding efficiency; Sustainable afforestation

Abstract

Bangladesh’s deforestation and land degradation pose a threat to biodiversity, calling for automation to facilitate effective reforestation. The research introduces Seedobot, a smart, locally manufactured forestation robot to improve seeding efficiency through machine learning. Built with locally sourced materials and developed through CAD modeling, Seedobot combines mechanical, electrical, and AI-powered systems, such as Arduino modules, Raspberry Pi, Bluetooth control, and environmental sensors (moisture, pH, NPK, ultrasonic). These sensors give real-time information, allowing the robot to choose the best tree species and provide accurate forestation. Field trials showed Seedobot’s adaptability to terrain and autonomous soil analysis, drilling, and seeding capability. The gear motor-based system provided precise seed placement, and torque-speed analysis verified efficient performance. Relative to conventional techniques, Seedobot streamlines seeding efficiency, diminishes labor reliance, and improves seed survival rates by means of automation and data-informed decision-making. This study underscores the prospects of locally produced forestation robots for large-scale automated reforestation, promoting sustainable afforestation activities in Bangladesh and beyond.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Avishek Chowdhury
AU  - Atia Ashrafy
AU  - Iltimas Wasek
AU  - Kaifa Samsad Shaan
PY  - 2025
DA  - 2025/11/18
TI  - Development and Testing of a Locally Manufactured Forestation Robot for Enhanced Seeding Efficiency in Bangladesh
BT  - Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)
PB  - Atlantis Press
SP  - 689
EP  - 697
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-884-4_83
DO  - 10.2991/978-94-6463-884-4_83
ID  - Chowdhury2025
ER  -