ReviewSensors (Basel, Switzerland)2021
Review of Weed Detection Methods Based on Computer Vision.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
32 citing papers in PubMed.
- Deep learning and IoT-based framework for sesame plant identification and weed detection.Scientific reports · 2026Article
- Maize Detection and Row Extraction Using Maize-YOLO and IPM-Clustering Method for Autonomous Agricultural Navigation.Sensors (Basel, Switzerland) · 2026Article
- A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation.Scientific data · 2026Article
- Weed Discrimination at the Seedling Stage in Dryland Fields Under Maize-Soybean Rotation.Plants (Basel, Switzerland) · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Deep learning classification of INSV-associated weeds in Monterey county using a curated RGB image dataset.Scientific reports · 2025Article
- A Novel Lightweight Dairy Cattle Body Condition Scoring Model for Edge Devices Based on Tail Features and Attention Mechanisms.Veterinary sciences · 2025Article
- An Indian annotated weed dataset for computer vision tasks in precision farming.Data in brief · 2025Article
- WeedSwin hierarchical vision transformer with SAM-2 for multi-stage weed detection and classification.Scientific reports · 2025Article
- A method for phenotyping lettuce volume and structure from 3D images.Plant methods · 2025Article
- A Review of CNN Applications in Smart Agriculture Using Multimodal Data.Sensors (Basel, Switzerland) · 2025Review
- A lightweight weed detection model for cotton fields based on an improved YOLOv8n.Scientific reports · 2025Article
- PD-YOLO: a novel weed detection method based on multi-scale feature fusion.Frontiers in plant science · 2025Article
- Effect of Depth Band Replacement on Red, Green and Blue Image for Deep Learning Weed Detection.Sensors (Basel, Switzerland) · 2024Article
- Efficient Optimized YOLOv8 Model with Extended Vision.Sensors (Basel, Switzerland) · 2024Article
- Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing.Sensors (Basel, Switzerland) · 2024Article
- The analysis of art design under improved convolutional neural network based on the Internet of Things technology.Scientific reports · 2024Article
- Twindemic Threats of Weeds Coinfected with Tomato Yellow Leaf Curl Virus and Tomato Spotted Wilt Virus as Viral Reservoirs in Tomato Greenhouses.The plant pathology journal · 2024Article
- Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning-A Comparison between High-End and Low-Cost Multispectral Sensors.Sensors (Basel, Switzerland) · 2024Article
- Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.