liuze-nwafu

4d_plant_analysis

A new method for the space-time registration of a growing plant based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into its different organs, including its main stem, its branches, etc. Then the organs are further divided into smaller segments that possess simpler geometric structures, for instance, cylinders, rectangular. Those segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP (Iterative Closest Point) algorithm.

AttentionDeepMIL

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

Automated_plant_height_measurement

Automatic-leaf-infection-identifier

Automatic detection of plant diseases

AutoPiIrrigator

Plant Watering System based on raspberry pi zero w, intended to provide an ease of automatic irrigation control at agricultural lands ( in farm lands ,etc. ) or even at your own home ( in gardens , seed plots ,etc. )

awesome-remote-sensing-change-detection

List of datasets, codes, and contests related to remote sensing change detection

canopy_height

plant height extractor science functions

CCSPredictor

coda

Coda: a convolutional denoising algorithm for genome-wide ChIP-seq data

HSm6AP

liuze-nwafu/HSm6AP

liuze-nwafu/4d_plant_analysis

A new method for the space-time registration of a growing plant based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into its different organs, including its main stem, its branches, etc. Then the organs are further divided into smaller segments that possess simpler geometric structures, for instance, cylinders, rectangular. Those segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP (Iterative Closest Point) algorithm.

liuze-nwafu/AttentionDeepMIL

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

liuze-nwafu/Automated_plant_height_measurement

liuze-nwafu/Automatic-leaf-infection-identifier

Automatic detection of plant diseases

liuze-nwafu/AutoPiIrrigator

Plant Watering System based on raspberry pi zero w, intended to provide an ease of automatic irrigation control at agricultural lands ( in farm lands ,etc. ) or even at your own home ( in gardens , seed plots ,etc. )

liuze-nwafu/awesome-remote-sensing-change-detection

List of datasets, codes, and contests related to remote sensing change detection

liuze-nwafu/CCSPredictor

liuze-nwafu/Collaborative-Learning-for-Weakly-Supervised-Object-Detection

liuze-nwafu/Deep-Learning

:computer:深度学习实战：手写数字识别、Discuz验证码识别、垃圾分类、语义分割

liuze-nwafu/DeepLatentMicrobiome

liuze-nwafu/ExplainableForestMapping

Explainable deep learning method for forest mapping using aerial imagery

liuze-nwafu/gcn

Implementation of Graph Convolutional Networks in TensorFlow

liuze-nwafu/graph_peak_caller

ChIP-seq peak caller for reads mapped to a graph-based reference genome

liuze-nwafu/image-processing-from-scratch

This project contains some interesting image processing algorithms that were wrote in python and c++ from scratch.

liuze-nwafu/ImageProcessing-Python

该资源为作者在CSDN的撰写Python图像处理文章的支撑，主要是Python实现图像处理、图像识别、图像分类等算法代码实现，希望该资源对您有所帮助，一起加油。

liuze-nwafu/Landsat-LAI

Employing a data-driven approach to generate Leaf Area Index (LAI) maps from Landsat images over CONUS

liuze-nwafu/leafArea

Project for Research Methodology in Computing

liuze-nwafu/LeafMask

liuze-nwafu/LSSVM

Python implementation of Least Squares Support Vector Machine for classification on CPU (NumPy) and GPU (PyTorch).

liuze-nwafu/m6ABRP

A machine learning-based model for predicting YTHDF2 binding regions in mRNAs via sequence-based properties

liuze-nwafu/ML_gzh

常用机器学习算法的简单手写实现，帮助更好理解算法

liuze-nwafu/Plant-Height-Detection-using-Contour-Operations

A program that uses color segmentation and contour operations to determine the height of plants. This model can be used in greenhouses to identify the height of plants at any point in time.

liuze-nwafu/Remote-Sensing-Image-Classification

深度学习图像分类的入门教程

liuze-nwafu/rnacocktail

liuze-nwafu/TIsigner

Translation Initiation coding region designer

liuze-nwafu/TISIGNER-ReactJS

TISIGNER: Unleash the power of synthetic biology

liuze-nwafu/Unet-pytorch

自然环境以及白背景下单片西瓜叶片分割模型以及利用MIOU（平均交并比）算法进行评估、分割结果展示

liuze-nwafu/Wind-Speed-Forecasting-for-wind-power-generation-plant.-Neural-Network-ML-based-prediction-algo.-

For largescale wind power penetration Wind speed prediction is a basic requirement of wind energy generation. There are many artificial neural network (ANN), ARMA, ARIMA approaches proposed in the recent literature in order to tackle this problem. This paper will use the artificial neural network (ANN) approach to get a prediction of wind speed using historical wind speed data. The historical data used here were gathered from NREL website ,as hourly basis from 80 meter hub height. The measurement location is NREL Flatirons Campus (M2). The readings displayed are derived from instruments mounted on or near a 82 meter (270 foot) meteorological tower located at the western edge of the Flatirons Campus (formerly NWTC) and about 11 km (7 miles) west of Broomfield, and approximately 8 km (5 miles) south of Boulder, Colorado. The tower is located at 39o 54' 38.34" N and 105o 14' 5.28" W (datum WGS84) with its base at an elevation of 1855 meters (6085 feet) above mean sea level. Data from year 2014 to 2018, in total 5 years of data has been used here as dataframe. Here the neural network has been implemented by Tensorflow’s Keras API. The used model is “sequential”. Four dense layer has been used in the optimized model. LSTM(Long- short-term memory) architecture has been used here as neural network architecture. Activation function being used in the dense layers are dropout function. The optimizer being used here is Adam. Here various range of Dropout function has been examined and chosen the best fit for this model. Also this paper examined various kinds of optimization method and used the best fitted one. The model performances were evaluated using the mean squared error using adam optimizer. Various kinds of data analytic techniques has been used here for better visualization and in depth understanding of the dataset and its variables. Since it is mostly a time series data so in the analytic part how the data is being changed with time has been shown. From the result of the predicted dataset it can be state that, this wind speed prediction model works best for all kinds of winds speed besides overfitted/ abnormal wind speeds which is a very rare case scenario.

liuze-nwafu/Zhang2018SCLS

Rice (Oryza Sative) root microbiome time-course analysis. (Zhang2018SCLS)