The Evolution of Agricultural Robotics: A Comprehensive Review and Future Challenges

Abstract

Agricultural robotics is constantly evolving in an effort to address the problems caused by urbanisation, population increase, high cost of high-quality goods, environmental preservation, and shortage of skilled workers. The primary current applications of agricultural robotic systems are reviewed in this study, which include their use in land preparation prior to planting, sowing, planting, plant treatment, harvesting, yield calculation, and phenotyping. The criteria used to evaluate all robots include their locomotion system, intended use, whether they had sensors, robotic arm, or computer vision algorithm, level of development and the nation or continent to which they belong. Four key areas that require further research to advance the state of the art in smart agriculture were identified after evaluating all similar characteristics, exposing research trends, common pitfalls, and characteristics that impede commercial development. The findings of this review indicate that investment in agricultural robotic systems enables the achievement of short-term goals (harvest monitoring) and long-term goals (yield estimation).

Introduction

Global Context and Agricultural Challenges

• The UN identifies global population growth—expected to rise from 7.6 billion to 9.8 billion by 2050—as a shared concern for all 193 member nations.

• Half of this growth will occur in just nine countries, including India and Nigeria.

• Increasing urbanization (68% urban by 2050) and a shift in consumer preference for pesticide-free, high-quality food is pressuring farmers to double food production.

• Despite a slight increase in arable land (from 9.6% in 1991 to 10.7% in 2022), smaller growing spaces and a shortage of skilled labor call for innovative farming methods.

Socioeconomic Issues

• The COVID-19 pandemic exposed vulnerabilities in agriculture, especially in developing nations dependent on small-scale producers.

• Wealthier countries face different challenges, such as youth migration to cities and low profitability in farming.

• These conditions highlight the need for automation and technological solutions.

Precision Agriculture (PA)

• PA aims to optimize farming decisions per unit of land and time using technology to enhance efficiency, productivity, and sustainability.

• Technologies include:

  • Robotics

  • Artificial Intelligence (AI)

  • Internet of Things (IoT)

• Applications span monitoring, soil management, pest control, and more.

• The PA market is projected to grow from $3.67 billion (2016) to $7.29 billion (2025).

Robotics in Agriculture

• Robots are transforming various farming stages:

  • Land Preparation: Robots like Casar, Greenbot, and UAVs like AGRAS MG-1P perform tasks such as fertilizing, ploughing, and spraying.

  • Navigation systems often use RTK GNSS for high-precision location tracking.

  • Obstacle detection via ultrasonic sensors, radar, and cameras ensure safe operation.

  • Research robots like AgBot use machine learning for weed detection.

Sowing and Planting Robots

• Traditional heavy tractors compact soil, negatively affecting crop growth.

• Robotic alternatives include:

  • Lumai-5 (China): Compact and sensor-rich for wheat fields.

  • Di-Wheel (Australia): Lightweight, modular, and uses smartphone sensors.

  • Sowing Robot 1 (Pakistan): Efficient corn planter, 5x faster than traditional methods.

  • Indian prototype: Uses tracked drives to minimize soil damage while carrying heavy loads.

• Cost remains a major barrier to adoption, especially for small producers.

Conclusion

In order to determine the actual needs for changes, it is necessary to first understand the major existing works in the field of smart agriculture, highlighting their benefits, drawbacks, and typical faults before proposing new technical and scientific advancements. 37% of agricultural robotic systems are 4WD, 64.52% lack a robotic arm, 22.06% are used for weeding tasks, 32.23% use RGB cameras, 35.48% do not include/report computer vision algorithms, 80.65% are in the research stage, 16.67% are designed by Australian companies/researchers, and 41.94% are developed by countries on the European continent, according to a systematic review of agricultural robotic systems used in the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation, and phenotyping. Simple and effective computer vision algorithms, parallelism, the swarm of robots, the limited use of the off-the-shelf concept, and multipurpose platforms that suitably adjust to the crop type under study were the primary features noted. Four primary areas have been suggested for further research in order to enhance the current agricultural robotic systems: sensors, computer vision algorithms, locomotion systems, and Internet of Things-based smart agriculture. This study examined numerous agricultural robotic systems. Given that the average harvest success rate increased by 22.98% and the average harvesting robot cycle decreased by 42.78% between 2014 and 2021. Thus, it is anticipated that as the aforementioned areas improve, agricultural robotic systems will continue to advance in terms of efficiency and robustness. Therefore, it is thought that this work was able to correlate the benefits of investing in technologies that serve as instruments for changing nature in addition to demonstrating the noteworthy advancements in the field of mobile robots.

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