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Weed mapping using UAV imagery and AI techniques: current trends and challenges.

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Title: Weed mapping using UAV imagery and AI techniques: current trends and challenges.
Authors: Tosin MC; Crop Science Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil., Merotto Júnior A; Crop Science Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil., Sulzbach E; Crop Science Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil., Scheeren I; Engineering School, Federal University of Rio Grande do Sul, Porto Alegre, Brazil., Bagavathiannan M; Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA., Markus C; Crop Science Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
Source: Pest management science [Pest Manag Sci] 2025 Dec; Vol. 81 (12), pp. 7625-7638. Date of Electronic Publication: 2025 Aug 20.
Publication Type: Journal Article; Review; Systematic Review
Language: English
Journal Info: Publisher: Published for SCI by Wiley Country of Publication: England NLM ID: 100898744 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1526-4998 (Electronic) Linking ISSN: 1526498X NLM ISO Abbreviation: Pest Manag Sci Subsets: MEDLINE
Imprint Name(s): Original Publication: West Sussex, UK : Published for SCI by Wiley, c2000-
MeSH Terms: Plant Weeds*/classification , Weed Control*/methods , Unmanned Aerial Devices* , Remote Sensing Technology*, Machine Learning ; Deep Learning
Abstract: Despite achieving accuracy rates of over 90% in recognizing weeds in crop fields using images captured by unmanned aerial vehicles (UAVs), challenges remain with embedded systems that perform automatic weed identification in real-time. The primary objective of this review is to analyze the latest academic research on the application of machine learning/deep learning (DL) techniques for weed recognition, highlight the methodology employed, and identify the challenges encountered. A systematic review was conducted, and the retrieved papers were organized according to the strategy adopted in the proposed method. Then, for each niche, the studies were described and compared in terms of the methodology and the type of issues addressed. This review specifically covers research associated with weed mapping from images captured using UAVs, providing an in-depth and detailed analysis of them, presenting their advantages and limitations. Regarding classical methodologies, numerous works have focused on the proposition and analysis of features aimed at extracting information related to spectral reflectance, texture, geometry, and other spatial patterns, with the goal of improving the classifier's discrimination capacity. Here, a tendency was observed with respect to non-visible spectral channels. In contrast, DL methods stand out for their ability to extract multi-scale features directly from images, leading to promising results in distinguishing between weed species or types. This review outlines the current landscape of UAV imagery-based weed mapping systems, offering valuable insights for researchers and guiding future efforts toward real-time weed mapping and the development of intelligent systems for site-specific herbicide applications. © 2025 Society of Chemical Industry.
(© 2025 Society of Chemical Industry.)
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Grant Information: Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul
Contributed Indexing: Keywords: artificial intelligence; computer vision; image processing; machine learning; neural networks; precision agriculture; site‐specific weed management
Entry Date(s): Date Created: 20250821 Date Completed: 20251115 Latest Revision: 20251121
Update Code: 20251121
DOI: 10.1002/ps.70151
PMID: 40836577
Database: MEDLINE
Description
Abstract:Despite achieving accuracy rates of over 90% in recognizing weeds in crop fields using images captured by unmanned aerial vehicles (UAVs), challenges remain with embedded systems that perform automatic weed identification in real-time. The primary objective of this review is to analyze the latest academic research on the application of machine learning/deep learning (DL) techniques for weed recognition, highlight the methodology employed, and identify the challenges encountered. A systematic review was conducted, and the retrieved papers were organized according to the strategy adopted in the proposed method. Then, for each niche, the studies were described and compared in terms of the methodology and the type of issues addressed. This review specifically covers research associated with weed mapping from images captured using UAVs, providing an in-depth and detailed analysis of them, presenting their advantages and limitations. Regarding classical methodologies, numerous works have focused on the proposition and analysis of features aimed at extracting information related to spectral reflectance, texture, geometry, and other spatial patterns, with the goal of improving the classifier's discrimination capacity. Here, a tendency was observed with respect to non-visible spectral channels. In contrast, DL methods stand out for their ability to extract multi-scale features directly from images, leading to promising results in distinguishing between weed species or types. This review outlines the current landscape of UAV imagery-based weed mapping systems, offering valuable insights for researchers and guiding future efforts toward real-time weed mapping and the development of intelligent systems for site-specific herbicide applications. © 2025 Society of Chemical Industry.<br /> (© 2025 Society of Chemical Industry.)
ISSN:1526-4998
DOI:10.1002/ps.70151