A Review on IoT-Based Agricultural Robots: Seed Sowing, Fertilizer Spraying, and Grass Cutting Automation for Precision Farming

Abstract

The imperative for global food security, driven by a projected population of $9.8$ billion by 2050, conflicts with escalating labor costs and inherent inefficiencies in traditional agricultural practices. This review critically assesses the role of IoT-based agricultural robotics in advancing Smart Agriculture (SA) and Precision Farming (PA). We synthesize the state-of-the-art across three specialized domains—precision seed sowing, Variable Rate Technology (VRT) chemical spraying, and autonomous mechanical weed control—to establish the necessity and design principles of multifunctional robotic platforms, exemplified by systems like Krushi Yantra. Thematic analysis reveals that, for resource-constrained smallholders, the economy of scope provided by multi-tasking robots offers a superior solution over the economy of scale offered by specialized machinery. However, widespread adoption is limited by high acquisition costs, the rural connectivity gap (hindering real-time autonomy), and technical trade-offs required for cost-effective sensor fusion. Future research directions must prioritize hybrid 5G/WiFi6 communication infrastructures, sustainable hardware (solar power, lightweight chassis), and the integration of enhanced generalized AI to ensure robust, equitable, and environmentally conscious autonomy in modern agriculture.

Introduction

Global agriculture faces growing pressure from rising food demand, labor shortages, and increasing production costs. Traditional farming methods rely heavily on manual labor and uniform input application, leading to inefficiencies, resource wastage, and environmental degradation. Labor scarcity—driven by rising wages, regulatory changes, and demographic shifts—has made automation and robotics economically attractive despite high initial costs.

Smart Agriculture and Precision Agriculture address these challenges by shifting from uniform field management to site-specific, data-driven practices. Agriculture 4.0 integrates IoT, AI, machine learning, and robotics to improve efficiency, sustainability, and productivity. IoT-enabled sensors and autonomous platforms allow real-time monitoring, precise irrigation, fertilization, and pest control.

This review focuses on IoT-based agricultural robots for seed sowing, fertilizer spraying, and grass cutting/weeding, emphasizing the importance of multifunctional robots for small and mid-sized farmers. Unlike large farms that benefit from scale, smaller farms gain economic advantage through scope—machines that perform multiple tasks. Multifunctional platforms such as the proposed Krushi Yantra (A.G.R.I. Yantra) combine operations like ploughing, seed sowing, fertilizing, and weeding into a single system, reducing labor dependence and capital constraints.

Technically, smart agri-robots consist of a mobile platform, actuators, vision systems, control units, and IoT-based communication. Precision farming relies on sensor fusion using GNSS, optical, electrochemical, and soil sensors. However, poor rural connectivity remains a major bottleneck, limiting real-time data transmission, cloud analytics, and advanced autonomy.

Specialized robotic applications demonstrate high efficiency in individual tasks: precision seed sowing improves planting accuracy, VRT-based spraying reduces chemical use through AI-driven target detection, and robotic weed control minimizes pesticide dependence using computer vision. Integrating these capabilities into a single low-cost platform presents trade-offs between affordability and advanced precision.

Krushi Yantra is designed as a cost-effective, multifunctional robot for resource-constrained farmers, using shared hardware, modular attachments, and IoT-based control. Its value lies not in outperforming specialized machines, but in maximizing asset utilization and reducing overall labor costs across multiple farm operations.

Despite its promise, challenges remain, including limited processing power, reduced autonomy, lack of standardization, infrastructure gaps, and concerns around affordability, data ownership, sustainability, soil compaction, and e-waste. Future research must focus on improved rural connectivity (5G, edge computing), robust and energy-efficient AI, modular lightweight designs, renewable energy integration, and environmentally sustainable deployment.

Conclusion

The rigorous review of IoT-based agricultural robotics confirms the existence of highly capable, specialized systems for precision seed sowing, VRT chemical spraying, and autonomous weed control. However, the overarching conclusion is that the pathway to widespread global adoption, particularly among resource-constrained success of this model is critically reliant upon the use of hybrid COTS hardware and robust, if simplified, software interfaces. Multifunctional IoT-based agricultural robots offer transformative potential by bridging the technological chasm between high-end research

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Copyright

Copyright © 2026 Anamika G. Ingole, Dr. A. S. Telang, Anurag R. Totare, Kartika B. Bhokse, Dhanashri D. Lajurkar, Anshul S. Pote, Shweta Jawanjal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.