Reference health
Deep Learning Based Weed Detection and Target Spraying System At Seedling Stage of Cotton Field
23/23 checkable references clean · checked 2026-08-29
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
12 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 23 checked references that resolve
resolves10.1016/j.compag.2019.105091
Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure
resolves10.1016/j.compind.2018.02.005
Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems
resolves10.1016/j.compag.2017.12.032
Evaluation of support vector machine and artificial neural networks in weed detection using shape features
resolves10.1016/j.biosystemseng.2018.05.013
Factors influencing the use of deep learning for plant disease recognition
resolves10.1016/j.compag.2018.09.021
AgroAVNET for crops and weeds classification: A step forward in automatic farming
resolves10.1016/j.compag.2022.107091
Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems
resolves10.1016/j.compag.2019.104963
Unsupervised deep learning and semi-automatic data labeling in weed discrimination
resolves10.1016/j.biosystemseng.2017.11.003
On-line crop/weed discrimination through the Mahalanobis distance from images in maize fields
resolves10.1016/j.compag.2022.107388
Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields
resolves10.1016/j.compag.2018.02.023
A rapidly deployable classification system using visual data for the application of precision weed management
resolves10.1016/j.compag.2019.104973
Deep learning-based visual recognition of rumex for robotic precision farming
resolves10.1016/j.cropro.2016.08.008
Weed management in cotton (Gossypium hirsutum L.) through weed-crop competition: A review
resolves10.1016/j.biosystemseng.2019.05.002
Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN
resolves10.1016/j.biosystemseng.2020.02.002
Real-time weed-crop classification and localisation technique for robotic weed control in lettuce
resolves10.1109/TPAMI.2016.2577031
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
resolves10.3390/s20247262
Application-Specific Evaluation of a Weed-Detection Algorithm for Plant-Specific Spraying
resolves10.3389/fpls.2022.850666
Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms
resolves10.1016/j.tplants.2018.07.004
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
resolves10.1016/j.compag.2019.02.005
A review on weed detection using ground-based machine vision and image processing techniques
resolves10.1016/j.compag.2022.107194
A deep learning approach incorporating YOLO v5 and attention mechanisms for field real-time detection of the invasive weed Solanum rostratum Dunal seedlings
resolves10.1002/ps.5349
Detection of broadleaf weeds growing in turfgrass with convolutional neural networks
The 12 references without a DOI — listed, not checked
no DOI — not checkedref8
no DOI — not checkedref9
no DOI — not checkedref12
no DOI — not checkedref14
no DOI — not checkedImpact of climate change on cotton growth and yields in Xinjiang
no DOI — not checkedMachine vision retrofit system for mechanicalweed control in precision agriculture applications
no DOI — not checkedSustainable weed control in the agro-ecosystems
no DOI — not checked2022a. Weed detection by faster RCNN model: an enhanced anchor box approach
no DOI — not checkedJustDeepIt: software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis
no DOI — not checkedNoise-tolerant RGB-D feature fusion network for outdoor fruit detection
no DOI — not checkedWeed Detection in Perennial Ryegrass With Deep Learning Convolutional Neural Network
no DOI — not checkedPyramid feature attention network for saliency detection
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-08-29 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2139/ssrn.4482157"><img src="https://citestamp.com/citestamped/10.2139/ssrn.4482157/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2139/ssrn.4482157)