Image-based Weed Detection
Image-Based Weed Detection is a computational methodology within agricultural image analysis that identifies and classifies weed species, including poisonous varieties, in digital imagery through pattern recognition and machine learning techniques, distinguishing itself from other detection approaches by relying exclusively on visual data inputs.
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31.8K
Citations
1.7K
Authors
560
Institutions
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2017–2026
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Victor Partel, Sri Charan Kakarla, Yiannis Ampatzidis · Computers and Electronics in Agriculture · 2019 · 416 citations · Full text
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Nanjing Forestry University
Nanjing, China
13 h-index
37 publications
2.3K citations
China Agricultural University
Beijing, China
13 h-index
56 publications
2.6K citations
University of Florida
Gainesville, United States
11 h-index
26 publications
3.5K citations
Texas A&M University
College Station, United States
11 h-index
25 publications
1.2K citations
Jilin Agricultural University
Changchun, China
10 h-index
64 publications
2.2K citations
Jiangsu University
Zhenjiang, China
10 h-index
31 publications
1.9K citations
Ministry of Agriculture and Rural Affairs
Beijing, China
9 h-index
26 publications
2.2K citations
Florida Department of Citrus
Bartow, United States
9 h-index
9 publications
709 citations
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64 publications · 7.4K citations
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