Exploring FarmBot in Agriculture Applications: An AI and IoT-Based Robotic System for Farmers with Disabilities

Agriculture remains inaccessible for over 1.3 billion individuals with disabilities due to physical, sensory, and cognitive challenges, restricting their ability to perform tasks such as planting, irrigation, and weeding. FarmBot, an open-source Internet of Things (IoT) and artificial intelligence (AI)-based robotic system, can address these challenges by automating tasks such as planting and irrigation. Integrating IoT sensors, AI models, and an intuitive mobile interface empowers individuals with disabilities to engage in farming with reduced physical strain. This article examines FarmBot's architecture, highlighting specifically the weed detection system's high precision and recall. To integrate weed detection with high accuracy, a weed detection model has been trained achieving 95% precision, where it also explores the scalability of the platform for diverse applications, highlighting potential advancements such as voice controls and offline functionality, which aim to further enhance its accessibility and sustainability. The article also highlights challenges and future research directions in relevant areas.

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Publication Details

Journal
IEEE Internet of Things Magazine
Published
2025-06-24
DOI
https://doi.org/10.1109/iotm.001.2500009
Citations
2
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
3.50
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article

Exploring FarmBot in Agriculture Applications: An AI and IoT-Based Robotic System for Farmers with Disabilities

Rafia Mumtaz, Aryan Kaushik, Syed Ali Hassan, Zahid Mahmood et al.
2 citations
IEEE Internet of Things Magazine
Smart Agriculture and AI
3.50
article

Exploring FarmBot in Agriculture Applications: An AI and IoT-Based Robotic System for Farmers with Disabilities

Rafia Mumtaz, Aryan Kaushik, Syed Ali Hassan, Zahid Mahmood, Noor Sajjad
article en
2 citations

Abstract

Agriculture remains inaccessible for over 1.3 billion individuals with disabilities due to physical, sensory, and cognitive challenges, restricting their ability to perform tasks such as planting, irrigation, and weeding. FarmBot, an open-source Internet of Things (IoT) and artificial intelligence (AI)-based robotic system, can address these challenges by automating tasks such as planting and irrigation. Integrating IoT sensors, AI models, and an intuitive mobile interface empowers individuals with disabilities to engage in farming with reduced physical strain. This article examines FarmBot's architecture, highlighting specifically the weed detection system's high precision and recall. To integrate weed detection with high accuracy, a weed detection model has been trained achieving 95% precision, where it also explores the scalability of the platform for diverse applications, highlighting potential advancements such as voice controls and offline functionality, which aim to further enhance its accessibility and sustainability. The article also highlights challenges and future research directions in relevant areas.

IEEE Internet of Things MagazineVol. 8(4)
Manchester Metropolitan University (GB), Pakistan Agricultural Research Council (PK), National University of Sciences and Technology (PK)
Zero hunger
Openalex Percentile: Top 8%
Smart Agriculture and AI
3.50
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Exploring FarmBot in Agriculture Applications: An AI and IoT-Based Robotic System for Farmers with Disabilities — Rafia Mumtaz, Aryan Kaushik, et al. · IEEE Internet of Things Magazine (2025) | TGRS Research Map | TGRS