Weed Detection Using Deep Learning: A Systematic Literature Review

Nafeesa Yousuf Murad, Tariq Mahmood, Abdur Rahim Mohammad Forkan, Ahsan Morshed, Prem Prakash Jayaraman, Muhammad Shoaib Siddiqui

Sensors 2023 review cited by 106

importance 7/10 partially solved Smart Agriculture and AI agriculture / crop protection stage: research

Weeds are harmful agricultural pests that raise production costs and damage the global agricultural economy, motivating early detection technology for farmers.

Weed-driven crop losses impose significant costs on the global agricultural economy.

A systematic literature review of deep learning weed-detection research, filtering 52 application papers and 8 survey papers for analysis.

Review found rapid growth in DL weed-detection research since 2015; 52 application papers and 8 surveys analyzed; pooled results yielded 34 unique items (abstract truncated).

Maps the state of the art in AI weed detection rather than presenting a new solution.

Venue Sensors
Published 2023
Work type review
Authors Nafeesa Yousuf Murad, Tariq Mahmood, Abdur Rahim Mohammad Forkan, Ahsan Morshed, Prem Prakash Jayaraman, Muhammad Shoaib Siddiqui
Citations 106 (OpenAlex, at import time)
References 98
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.3390/s23073670
Open access gold
Keywords weed detection, deep learning, systematic review, agriculture, image analysis
7 / 10

significant — thousands to millions affected

Abstract states weeds are harmful agricultural pests raising production costs with significant impact on the global agricultural economy, motivating early detection for farmers; broad industry-level problem, addressed here via literature review.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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