Information and scripts for the CropAndWeed Dataset

Weed Detection and Laser Elimination on Farmbot Genesis.

Deep Learning-based Early Weed Segmentation using Motion Blurred UAV Images of Sorghum Fields

Lawn robot for spraying weeds

Using YOLOv7 for crop and weed detection

CoFly-WeedDB dataset: 201 aerial RGB images for weed detection.

A curated collection of 45 high-quality RGB image datasets for computer vision in agriculture. Features datasets for weed detection, disease identification, and crop monitoring, focusing on natural field scenes. Part of our GIL 2025 survey paper.

Code repository for sandplain lupin detection paper. Deep learning model for segmentation of morphologically similar weed and crop species

RecyclingRush ♻️: Towards Continuous Floating Invasive Plant Removal Using Unmanned Surface Vehicles and Computer Vision, IEEE Access 2024.

METADATA-FARMER ASSISTANCE WEBAPP | AI & ML

Weed detection method with training datasets

Detecting weed plants in fields using Ai techniques.

This package contains the code used by Kamaro Engineering e.V. for the Task 3 (object detection) on the virtual Field Robot Event 2021 (June 8-10).

This is the Python Code used in Weed Detection In the AgriVision Project.

'CNN_Sorghum_Weed_Classifier' is an artificial intelligence (AI) based software that can differentiate a sorghum sampling image from its associated weeds images.

Final Year Project -> AIT Banglore -> Weed Detections

Detecting weed plants in fields using Ai techniques. This is the Code from which our AgriVision Project Weed Detection code based.

My science project aims to identify weeds from crops during their early seedling phase.

A project to detect weed using classic image processing.

Created image classifiers as well as an access model by using Python to create a process for the processing of image data. This pipeline include: • Pre-processing, feature extraction, train classifiers with extracted features and labels from train, test, and val set. • Evaluate models with extracted features from test and val set with Visualisation