What does this research mean for the field?

In autonomous weed detection and identification, model accuracy positively correlates with dataset size, and VGGNet achieves the highest accuracy among reviewed deep learning and classical machine learning models. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This review aims to consolidate findings on autonomous weed detection and classification using machine learning and computer vision.

July 19, 2026Discover Applied SciencesOpen Access

Systematic review of autonomous weed detection and identification using deep learning and computer vision

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Authors

BABahga Abdul-HakeemCairo University OEOmar Fayez ElMahdyCairo University SMSherif MehannyCairo University

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Implication

Systematic review finds trends in deep learning for weed detection and classification, suggesting future innovations.

Key Points

This review aims to consolidate findings on autonomous weed detection and classification using machine learning and computer vision.
Systematic review of studies on weed detection and classification using machine learning approaches.
Analysis of results from classical methods like SVM, KNN, ANN, and deep learning models such as VGG16, ResNet, and YOLO.
Evaluation of datasets from various sources including UAV imagery and crop-specific datasets.
VGGNet achieved the highest accuracy among all reviewed models.
A positive correlation was found between dataset size and model accuracy.
The review highlights opportunities for developing autonomous weed management technologies.

Cite This Study

Abdul-Hakeem et al. (2026) studied this question.

synapsesocial.com/papers/6a5c69fb118b92953e3ee2ee https://doi.org/10.1007/s42452-026-09205-9

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Systematic review of autonomous weed detection and identification using deep learning and computer vision

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Key Points

Cite This Study

Systematic review of autonomous weed detection and identification using deep learning and computer vision

Discussion

Member takes

Implication

Key Points

Cite This Study

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