503664367's starred repositories
Nurtient-Deficiency-Detection
Project to evaluate different CNN Architectures for N,P,K deficiency detection of Maize Plant
Maize-plant-point-cloud-dataset
Point cloud dataset
CropDiseaseDiag
Maize disease detection using Deep Convolutional Neural Network.
Deep-Learning-with-Unmanned-Aerial-Vehicle-Imagery-in-the-Detection-of-Tassels-in-Maize
Course project of CSE499(Senior Design) where we tried to detect tassels from UAV imagery using different deep learning techniques.
Maize-Leaf-Disease_CNN
Identifying disease in images of maize leaves using CNNs
Maize-phenotyping-methods-based-on-point-clouds
There are maize phenotyping methods including organ segmentation, leaf traits extraction, etc.
Maize-DapSeq-Machine-Learning
Code and data sets for the machine learning performed for the Maize DapSeq project as a collaboration between the Nemhauser, Seelig and Galavotti labs
maize_early_yield_prediction
Repository with the code for predicting the yield of crop varieties using deep learning and early phenotype data.
Maize_Phenotype_Map
Image Data Processing for Maize Phenotype Map
Plant_Counting
IntegrateNet: A Deep learning Network for maize Stand Counting from UAV Imagery by Integrating Density and Local Count Maps.
Corn_seedlings-Weeds_Database
SHIHEZI University(Xinjiang, China): Database of corn seedlings and weeds
cornSeedlingPlants
A graph-based skeletonization algorithm for point cloud of corn plants
CropIdentification
Crop Identification application using flutter, that uses machine learning to find crop in the given image.
e16-4yp-Identification-of-Weeds-in-broadcasted-Paddy-fields-using-multispectral-UAV-images
Develop a model to Identify paddy crops and weeds by images taken from UAV (unmanned aerial vehicle) and develop a desktop application as user interface
CIS---Crop-Identification-System
Repository for Ingenious Hackathon
Crop-Segmentation-Yield-Estimation
The project was designed to develop a deep learning based convolutional neural network(CNN) model using satellite imageries of farmlands for the identification of crop types from images through a semantic segmentation task.
Crop-Classification-Software
A transfer learning approach for the identification of Paddy, Wheat, Sugarcane and Cotton crop fields.
Weed-Mapping
Weed Mapping in Aerial Images through Identification and Segmentation of Crop Rows and Weeds using Convolutional Neural Networks
CropManager
基于安卓的农作物PH识别推荐施肥系统（结合wifi模块）
BallTracking
这是开源软件基础的大作业：基于OpenCV的物体位置识别程序，以识别小球为例
Image-recognition-of-agricultural-diseases
使用现代卷积神经网络架构（例如ResNet，DenseNet）对38类植物病害进行识别，并生成一个简单的UI操作界面
SKlearn_Reg
利用欧空局数据和软件SNAP生成全椒LAI（植被叶面积指数）栅格数据，再同GEE（Google earth engine）下载的对应时空的栅格数据进行回归模型构建，利用模型推算其他时间GEE栅格影像的LAI数据
SOTANet-better
这是一个基于Pytorch实现的轻量化目标检测网络。
Reproduct-Object-Detection-Papers
复现目标检测所有经典论文当作练习