54 skills found · Page 1 of 2
bmizerany / Roundupeliminate bugs and weeds from shell scripts
AlexOlsen / DeepWeedsA Multiclass Weed Species Image Dataset for Deep Learning
cropandweed / Cropandweed Dataset[WACV 2023] Information and scripts for the CropAndWeed Dataset
in-the-weeds-hannah-stulberg / Substack ArticlesLearn AI fundamentals, tips, tricks and workflows from In the Weeds articles, with an AI instructor trained to teach from the source material.
awangenh / Weed MappingWeed Mapping in Aerial Images through Identification and Segmentation of Crop Rows and Weeds using Convolutional Neural Networks
TinySpriteScripts / Tss WeedshopComplete Rewrite of DrB1ackBeard's burgershot job. run your own weedshop where you collect, dry, grind weed to make different strains of joints and sell them to players. other features included
InternScience / WeedStemDetection[AAAI-2025] Towards Efficient and Intelligent Laser Weeding: Method and Dataset for Weed Stem Detection
kapost / WeedsA real-world, universal React/Redux boilerplate from Kapost
GFZ / Weedsgalore[WACV 2025] WeedsGalore dataset and code for segmentation of weeds in maize fields.
PRBonn / WeedsAreWeirdNo description available
Galaxyy2 / Gx WeedshopThis is a free weed shop script used with a legion weed shop mlo
sharmaroshan / Weed DetectionThis Problem is based on a Image Data set consisting of different types of weeds, to detect them in crops and fields. I have used Deep Learning Model called CNN(Convolutional Neural Networks) with Dropout, Batch Normalization, ReduceLearning rate on plateau, Early stoppig rounds, and Transposd Convolutional Neural Networks.
Mulham91 / Multi Spectral Image Synthesis For Crop Weed Segmentation In Precision FarmingIn this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared information, generating four channel multi-spectral synthetic images.
kabbas570 / CED Net Crops And Weeds Segmentation For Smart Farming Using A Small Cascaded Encoder Decoder ArchiSegmentation of crops and weeds for smart farming based on the small cascaded encoder-decoder architecture
josemenber / Image Based Crop Anomaly DetectionA Convolutional Neural Network approach for image-based anomaly detection in smart agriculture
WUR-ABE / Rl Drone Object SearchUAV-based path planning for efficient localization of non-uniformly distributed weeds using prior knowledge: A reinforcement-learning approach
DongChen06 / CottonWeedsDeep Transfer Learning for Weed Classification
vbookshelf / Weed DetectorAi powered web app to detect weeds by analyzing crop and weed seedlings.
nikhilroxtomar / Multiclass Segmentation In PyTorchThis repository contains PyTorch implementations for multiclass image segmentation using the U-Net architecture. It focuses on segmenting multiclass weeds in agricultural images, demonstrating the effectiveness of deep learning models in precision agriculture.
Russ76 / HerbieLawn robot for spraying weeds
