# A Survey of Deep Learning Techniques for Weed Detection from Images

arXiv: https://arxiv.org/abs/2103.01415 · HTML: https://mlsanity.com/paper/2103.01415/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 174 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [End-to-end people detection in crowded scenes](https://mlsanity.com/paper/1506.04878/) (2015)
- [DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning](https://mlsanity.com/paper/1810.05726/) (2018)
- [Deep Residual Learning in the JPEG Transform Domain](https://mlsanity.com/paper/1812.11690/) (2018)
- [ThunderNet: Towards Real-time Generic Object Detection](https://mlsanity.com/paper/1903.11752/) (2019)
- [weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming](https://mlsanity.com/paper/1709.03329/) (2017)
- [WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming](https://mlsanity.com/paper/1808.00100/) (2018)
- [A Public Image Database for Benchmark of Plant Seedling Classification Algorithms](https://mlsanity.com/paper/1711.05458/) (2017)
- [Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming](https://mlsanity.com/paper/1806.03412/) (2018)
- [Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection](https://mlsanity.com/paper/1612.03019/) (2016)
- [Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming](https://mlsanity.com/paper/1806.03413/) (2018)
- [Deep Learning with unsupervised data labeling for weeds detection on UAV images](https://mlsanity.com/paper/1805.12395/) (2018)
- [Deep Convolutional Neural Network for Plant Seedlings Classification](https://mlsanity.com/paper/1811.08404/) (2018)
- [A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management](https://mlsanity.com/paper/1801.08613/) (2018)

## Built on by (most cited first, 23 of 23)

- [Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems](https://mlsanity.com/paper/2110.04960/) (2021)
- [Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images](https://mlsanity.com/paper/2109.02716/) (2021)
- [Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges](https://mlsanity.com/paper/2308.06668/) (2023)
- [Improved Crop and Weed Detection with Diverse Data Ensemble Learning](https://mlsanity.com/paper/2310.01055/) (2023)
- [WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification](https://mlsanity.com/paper/2505.18930/) (2025)
- [WeedSense: Multi-Task Learning for Weed Segmentation, Height Estimation, and Growth Stage Classification](https://mlsanity.com/paper/2508.14486/) (2025)
- [CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture](https://mlsanity.com/paper/2305.10084/) (2023)
- [Panoptic One-Click Segmentation: Applied to Agricultural Data](https://mlsanity.com/paper/2303.08689/) (2023)
- [Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities](https://mlsanity.com/paper/2305.01899/) (2023)
- [Multi-growth stage plant recognition: a case study of Palmer amaranth (Amaranthus palmeri) in cotton (Gossypium hirsutum)](https://mlsanity.com/paper/2307.15816/) (2023)
- [WeedScout: Real-Time Autonomous blackgrass Classification and Mapping using dedicated hardware](https://mlsanity.com/paper/2405.07349/) (2024)
- [Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction](https://mlsanity.com/paper/2501.07185/) (2025)
- [Weed Recognition using Deep Learning Techniques on Class-imbalanced Imagery](https://mlsanity.com/paper/2112.07819/) (2021)
- [Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images](https://mlsanity.com/paper/2208.04611/) (2022)
- [Investigating image-based fallow weed detection performance on Raphanus sativus and Avena sativa at speeds up to 30 km h-1](https://mlsanity.com/paper/2305.10311/) (2023)
- [From Seedling to Harvest: The GrowingSoy Dataset for Weed Detection in Soy Crops via Instance Segmentation](https://mlsanity.com/paper/2406.00313/) (2024)
- [Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity](https://mlsanity.com/paper/2508.19511/) (2025)
- [Deep semi-supervised approach based on consistency regularization and similarity learning for weeds classification](https://mlsanity.com/paper/2510.10573/) (2025)
- [A Hybrid CNN-ViT-GNN Framework with GAN-Based Augmentation for Intelligent Weed Detection in Precision Agriculture](https://mlsanity.com/paper/2511.15535/) (2025)
- [Design of a six wheel suspension and a three-axis linear actuation mechanism for a laser weeding robot](https://mlsanity.com/paper/2512.10319/) (2025)
- [Self-supervised training for high-resolution close-range multispectral remote sensing imagery](https://mlsanity.com/paper/2607.11366/) (2026)
- [Evaluating Contextual Bias in CNN Image Classification: Evidence from Agricultural Benchmark Datasets](https://mlsanity.com/paper/2609.14654/) (2026)
- [A Multi-Dataset Benchmark of YOLO-Based Weed Detection in Precision Agriculture](https://mlsanity.com/paper/2609.33991/) (2026)

## Abstract (arXiv)

The rapid advances in Deep Learning (DL) techniques have enabled rapid detection, localisation, and recognition of objects from images or videos. DL techniques are now being used in many applications related to agriculture and farming. Automatic detection and classification of weeds can play an important role in weed management and so contribute to higher yields. Weed detection in crops from imagery is inherently a challenging problem because both weeds and crops have similar colours ('green-on-green'), and their shapes and texture can be very similar at the growth phase. Also, a crop in one setting can be considered a weed in another. In addition to their detection, the recognition of specific weed species is essential so that targeted controlling mechanisms (e.g. appropriate herbicides and correct doses) can be applied. In this paper, we review existing deep learning-based weed detection and classification techniques. We cover the detailed literature on four main procedures, i.e., data acquisition, dataset preparation, DL techniques employed for detection, location and classification of weeds in crops, and evaluation metrics approaches. We found that most studies applied supervised learning techniques, they achieved high classification accuracy by fine-tuning pre-trained models on any plant dataset, and past experiments have already achieved high accuracy when a large amount of labelled data is available.