Advancing agriculture with machine learning: a new frontier in weed management

Highlights

● Machine learning offers innovative and sustainable weed management approaches.

● Herbicide use and environmental impact can be reduced through machine learning.

● Machine learning models can classify weed species and optimize herbicide usage.

● Real-time monitoring of invasive species is possible with machine learning.

Abstract

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental risks. Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed management. Machine learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate predictions. Machine learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.

Graphical abstract

Keywords

Weed management / herbicides / machine learning / agricultural practices / environmental impact

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Compliance with ethics guidelines

Mohammad Mehdizadeh, Duraid K. A. Al-Taey, Anahita Omidi, Aljanabi Hadi Yasir Abbood, Shavan Askar, Soxibjon Topildiyev, Harikumar Pallathadka, and Renas Rajab Asaad declare that they have no conflicts of interest or financial conflicts to disclose. This article does not contain any studies with human or animal subjects performed by any of the authors.

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The Author(s) 2024. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)