Exploring Effective Weed Management through UAV Application

Author: Monika Raghuwanshi, Namrata Jain, K.K. Agrawal and Mrinali Gajbhiye

Download PDF

Abstract

Weeds, which are plants considered undesirable, can significantly reduce agricultural yields by competing for essential resources such as water, nutrients, light, space, and carbon dioxide. Effective weed management is essential to meet the increasing demands of food production. The integration of drones, artificial intelligence, and a variety of sensors, including hyperspectral, multi-spectral, and RGB (red-green-blue), holds the exciting potential to enhance weed management outcomes. The transformational impact of Unmanned Aerial Vehicles (UAVs) on agricultural weed management is undeniable. This comprehensive review delves into various aspects, encompassing types of UAVs, emerging trends, payload options, sensing technologies, weed distribution mapping, spectral analysis, and image processing. The utilization of UAVs offers a range of benefits, including heightened efficiency, cost-effectiveness, and reduced environmental footprint. While challenges persist, real-world case studies underscore the successful integration of UAVs into weed management strategies. As a pivotal advancement in precision agriculture, UAVs have the capacity to revolutionize weed management, ushering in an era of sustainable and precisely targeted interventions.

Keywords

Weed management, Unmanned Aerial Vehicle, RGB, Hyper spectral, Multi-spectral

Conclusion

Agriculture plays a crucial role in upholding the economy, serving as a foundational element that influences long-term economic growth and structural shifts. Amidst this backdrop, farmers confront a range of uncertainties, including issues related to achievable crop yields, the consequences of climate change, the presence of pests and weeds, soil degradation, and other intricate challenges. Nevertheless, the rise of advanced technologies spanning production, information sharing, transportation, and more, has distinctly introduced new patterns in the agricultural domain. This evolution is notably demonstrated by the rapid acceptance of artificial intelligence (AI) in conjunction with the advancement of state-of-the-art computing technologies. Within the sphere of agricultural management, AI, encompassing tools such as drones and remote sensing unmanned aerial vehicles (UAVs), has emerged as a potent, accurate, cost-effective, and sustainable remedy. Its importance lies in ensuring the continued viability of the agricultural sector in efficiently meeting the demands and supply dynamics of food production. A key aspect explored in this study revolves around the strategic utilization of unmanned aerial vehicles (UAVs) and machine learning algorithms to enhance the sustainability of weed management practices. This is accomplished by precisely identifying clusters of weeds within cultivated fields. The incorporation of UAVs holds potential for advancing strategies in integrated weed management (IWM). By identifying weed patches, these technologies can alleviate the pressure on herbicide-resistant weeds, thus reducing the spread of herbicides into the environment. The application of AI in agriculture also offers the benefit of addressing labor shortages and minimizing human intervention in tasks such as the application of chemical herbicides. For example, the use of drone-based fertilizer sprayers streamlines this process, optimizing both efficiency and accuracy. In summary, this paper envisions that the ongoing advancement of AI technology will significantly transform the agricultural sector. This transformation will serve as a pivotal approach in reshaping the industry for all stakeholders involved, aligning with the fundamental principles of agricultural precision—employing the right strategies, in the right locations, at the right times, and in suitable quantities.

References

-

How to cite this article

Monika Raghuwanshi, Namrata Jain, K.K. Agrawal and Mrinali Gajbhiye (2023). Exploring Effective Weed Management through UAV Application. Biological Forum – An International Journal, 15(9): 743-752.