Current Trends and Technologies in Pest Control Using Robotics

The integration of robotics in pest control has emerged as a transformative approach in modern agriculture, offering precise, efficient, and environmentally friendly solutions. This review explores the current trends and technologies in robotic pest control, highlighting advancements in autonomous systems, AI-driven solutions, and multi-robot fleets. The discussion is supported by insights from recent research papers, providing a comprehensive overview of the field.

Autonomous Weeding and Pest Management Robots

Autonomous robots are increasingly being deployed for precision weed management and pest control. These systems combine computer vision, machine learning, and mechanical systems to identify and eliminate pests or weeds. For instance, autonomous weeding robots use deep learning models to detect weeds and employ mechanical tools for removal, significantly reducing the need for herbicides(Zhang et al. 2022).These robots not only save labor but also minimize environmental pollution by avoiding chemical use. Similarly, laser-based robotic systems have been developed to target specific pests, such as aphids, with high precision. These robots use deep learning algorithms to locate pests and neutralize them with laser beams, achieving a 90% mortality rate in controlled experiments (Lacotte et al. 2022). Such systems are particularly valuable for managing pests that are difficult to control with traditional methods.

Drones in Precision Pest Management

Unmanned aerial vehicles (UAVs), or drones, have become a key technology in precision pest management. Drones equipped with advanced imaging technologies can detect changes in plant reflectance caused by pest infestations, enabling early detection and targeted intervention(Filho et al. 2020). These systems can generate digital maps of pest hotspots, guiding actuation drones to apply pesticides or release natural enemies precisely where needed. Research has also demonstrated the effectiveness of drones in variable-rate application of pesticides and fertilizers. By optimizing the application rate and reducing drift, drones can enhance the efficiency of pest control while minimizing environmental impact(Subramanian et al. 2021). This approach aligns with the goals of sustainable agriculture, where reducing chemical inputs is a priority.

AI and Machine Learning in Pest Control

Artificial intelligence (AI) and machine learning are driving advancements in pest control technologies. Deep learning models, particularly convolutional neural networks (CNNs), are being used to classify pests, identify infestations, and guide robotic systems. For example, CNN-based systems have been employed to recognize Pyralidae pests, such as corn borers and rice leaf rollers, with an accuracy of 94.3% in natural farm settings(Liu et al. 2019). AI tools are also being integrated into UAVs for pest monitoring and control. By analyzing images captured by drones, these systems can detect pest outbreaks early and recommend targeted interventions. This approach not only improves the accuracy of pest control but also reduces the need for blanket chemical applications(Mignoni et al. 2021).

Multi-Robot Fleets for Comprehensive Pest Control

The use of fleets of ground and aerial robots is emerging as a promising strategy for large-scale pest control. These systems combine heterogeneous robots equipped with advanced sensors and end-effectors to cover a wide range of agricultural scenarios. For instance, ground robots can perform mechanical weeding and pest detection, while aerial robots provide overhead monitoring and targeted spraying(Gonzalez-de-Santos et al. 2017). Such multi-robot systems are particularly effective in reducing pesticide use and improving crop quality. By coordinating their actions, these robots can address pest infestations more efficiently than single-robot systems, making them a valuable tool for modern agriculture.

Non-Chemical Pest Control Methods

There is a growing emphasis on non-chemical pest control methods to reduce the environmental and health impacts of pesticides. Robotic systems are playing a key role in this shift by offering alternatives such as mechanical weeding, laser-based pest neutralization, and precision spraying. For example, a spraying robot based on binocular machine vision has been developed to accurately identify and target pests, achieving a recognition rate of 92.5% for tomatoes (Wang and Wang 2017).This system not only reduces pesticide waste but also minimizes the risk of over-application, making it a sustainable solution for farmers.

Future Trends in Robotic Pest Control

The future of robotic pest control is expected to be shaped by several emerging trends. One key area of development is the integration of synthetic biology and biotechnology into pest management. Advances in these fields are enabling the creation of pest-resistant crops and novel biological control agents, which can be deployed alongside robotic systems for enhanced effectiveness(Mateos Fernández et al. 2022). Another promising trend is the use of multi-disciplinary research collaborations to improve the design and functionality of robotic systems. By combining insights from agronomy, ecology, and engineering, researchers can develop more robust and adaptable robots capable of operating in diverse agricultural environments(Filho et al. 2020).

Conclusion

The integration of robotics, AI, and advanced sensors has revolutionized pest control in agriculture, offering precise, efficient, and environmentally friendly solutions. From autonomous weeding robots to drone-based monitoring systems, these technologies are reducing the reliance on chemical pesticides while improving crop yields. As research continues to advance, the future of pest control is expected to be shaped by innovations in synthetic biology, multi-robot fleets, and sustainable practices. These developments will play a critical role in addressing the challenges of modern agriculture while preserving the environment for future generations.

Reference

Filho, Fernando H.Iost, Wieke B. Heldens, Zhaodan Kong, and Elvira S. De Lange. 2020. “Drones: Innovative Technology for Use in Precision Pest Management.” Journal of Economic Entomology 113(1): 1–25. doi:10.1093/jee/toz268.

Gonzalez-de-Santos, Pablo, Angela Ribeiro, Cesar Fernandez-Quintanilla, Francisca Lopez-Granados, Michael Brandstoetter, Slobodanka Tomic, Stefania Pedrazzi, et al. 2017. 18 Precision Agriculture Fleets of Robots for Environmentally-Safe Pest Control in Agriculture. doi:10.1007/s11119-016-9476-3.

Lacotte, Virginie, Toan NGuyen, Javier Diaz Sempere, Vivien Novales, Vincent Dufour, Richard Moreau, Minh Tu Pham, et al. 2022. “Pesticide-Free Robotic Control of Aphids as Crop Pests.” AgriEngineering 4(4): 903–21. doi:10.3390/agriengineering4040058.

Liu, Boyi, Zhuhua Hu, Yaochi Zhao, Yong Bai, and Yu Wang. 2019. “Recognition of Pyralidae Insects Using Intelligent Monitoring Autonomous Robot Vehicle in Natural Farm Scene.” http://arxiv.org/abs/1903.10827.

Mateos Fernández, Rubén, Marko Petek, Iryna Gerasymenko, Mojca Juteršek, Špela Baebler, Kalyani Kallam, Elena Moreno Giménez, et al. 2022. “Insect Pest Management in the Age of Synthetic Biology.” Plant Biotechnology Journal 20(1): 25–36. doi:10.1111/pbi.13685.

Mignoni, Maria Eloisa, Emiliano Soares Monteiro, Cesar Zagonel, and Rafael Kunst. 2021. “ARTIFICIAL INTELLIGENCE AND ITS TOOLS IN PEST CONTROL FOR AGRICULTURAL PRODUCTION: A REVIEW.” RECIMA 21 - Revista Científica Multidisciplinar 2: e28624–e28624.

Subramanian, K. S., S. Pazhanivelan, G. Srinivasan, R. Santhi, and N. Sathiah. 2021. “Drones in Insect Pest Management.” Frontiers in Agronomy 3(December). doi:10.3389/fagro.2021.640885.

Wang, Zhongrui, and Zhongcheng Wang. 2017. “An Agricultural Spraying Robot Based on the Machine Vision.” Applied Science and Innovative Research 1(2): 80. doi:10.22158/asir.v1n2p80.

Zhang, Wen, Zhonghua Miao, Nan Li, Chuangxin He, and Teng Sun. 2022. “Review of Current Robotic Approaches for Precision Weed Management.” Current Robotics Reports 3(3): 139–51. doi:10.1007/s43154-022-00086-5.