Artificial Intelligence and Agronomy: An Introductory Reflection on Reducing Herbicide Dependence in Weed Management

Abstract

The crop production sector faces the critical challenge of effectively managing weeds while reducing herbicide dependence, which aligns with environmental and economic sustainability. This chapter explores the shift toward site-specific weed management (SSWM), accelerated by artificial intelligence (AI) and digital technologies. Also, it addresses the often-neglected complexities of weed-seed bank germination. We propose an integrated approach, combining AI-enhanced weed detection, cover crop strategies to limit weed seedling emergence, cost-effective spot spraying, and the application of large language models to enrich decision-making under an integrated weed management (IWM) scheme. This helps ensure varied management tactics and weed resistance prevention. We present findings from our Chilean case study, which provide insights into real-world challenges and successes, and highlight the study’s limitations, such as the specific agroecological conditions and limited sample size, which may affect the generalizability of the results to other contexts. We draw comparisons with global AI-driven weed management advancements. This chapter underscores the potential of such integrated strategies to lower herbicide reliance and contribute to sustainable, technologically advanced weed control, fostering environmental stewardship and economic viability in the face of climate change.