# weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming

arXiv: https://arxiv.org/abs/1709.03329 · HTML: https://mlsanity.com/paper/1709.03329/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 29 of 29)

- [A Survey of Deep Learning Techniques for Weed Detection from Images](https://mlsanity.com/paper/2103.01415/) (2021)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection](https://mlsanity.com/paper/2106.07770/) (2021)
- [deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques](https://mlsanity.com/paper/2203.09091/) (2022)
- [Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models](https://mlsanity.com/paper/2208.02707/) (2022)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation](https://mlsanity.com/paper/2210.09739/) (2022)
- [Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images](https://mlsanity.com/paper/2109.02716/) (2021)
- [WeedsGalore: A Multispectral and Multitemporal UAV-based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields](https://mlsanity.com/paper/2502.13103/) (2025)
- [Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery](https://mlsanity.com/paper/2104.10249/) (2021)
- [Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review](https://mlsanity.com/paper/2204.04707/) (2022)
- [iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species](https://mlsanity.com/paper/2503.20068/) (2025)
- [Deep Learning Techniques for In-Crop Weed Identification: A Review](https://mlsanity.com/paper/2103.14872/) (2021)
- [Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2108.01884/) (2021)
- [CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture](https://mlsanity.com/paper/2305.10084/) (2023)
- [Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands](https://mlsanity.com/paper/2502.08678/) (2025)
- [Vision Foundation Models in Agriculture: Toward Domain-Specific Adaptation for Weed Herbicide Trials Assessment](https://mlsanity.com/paper/2511.04288/) (2025)
- [Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression](https://mlsanity.com/paper/2112.03205/) (2021)
- [Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming](https://mlsanity.com/paper/2112.13986/) (2021)
- [Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and Leaves](https://mlsanity.com/paper/2310.06582/) (2023)
- [A Review of Cybersecurity Incidents in the Food and Agriculture Sector](https://mlsanity.com/paper/2403.08036/) (2024)
- [Wild Berry image dataset collected in Finnish forests and peatlands using drones](https://mlsanity.com/paper/2405.07550/) (2024)
- [Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming](https://mlsanity.com/paper/2407.14119/) (2024)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2108.06608/) (2021)
- [Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming](https://mlsanity.com/paper/2210.11545/) (2022)
- [Lessons Learned Report: Super-Resolution for Detection Tasks in Engineering Problem-Solving](https://mlsanity.com/paper/2303.00364/) (2023)
- [Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops](https://mlsanity.com/paper/2405.02218/) (2024)
- [RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection](https://mlsanity.com/paper/2410.04983/) (2024)
- [Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves](https://mlsanity.com/paper/2501.00527/) (2024)
- [AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation](https://mlsanity.com/paper/2609.05762/) (2026)

## Abstract (arXiv)

Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable, and accurate weed detection to minimize damage to surrounding plants. In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder-decoder cascaded Convolutional Neural Network (CNN), Segnet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the Normalized Difference Vegetation Index (NDVI) as a distinguishable feature for automatic ground truth generation. We train 6 models with different numbers of input channels and condition (fine-tune) it to achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested for MAV integration. Dataset used in this paper is released to support the community and future work.