justinamichael Goto Github PK

bibsearch

cnn_sorghum_weed_classifier

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

covid-19-deep-learning-cnn-model

Here, I created my own deep learning(CNN) model for early detection of COVID-19 from chest x-ray images. If we were to answer the question that why we need a deep learning model for early detection of COVID-19 from chest x-ray images, we can say the followings, doctors have seen that even if the test kits desined for diagnosis results in negative, the real results are positive for some patients when they review the chest X-ray images. For now the public dataset contains less amount of data which you can see in the dataset2 folder. We get this dataset from open-source https://github.com/ieee8023/covid-chestxray-dataset, but for sure it is not enough to train a proper deep learning model. But just to show that how easy it is to create an AI for the early detection of these kind of viruses. Just keep in mind that this cannot be used for diagnosis without training many more images in high-resolution and professinal medical tests. There you go! Let's work together to fight against COVID-19. As a tool, I used Keras with Tensorflow background, and the model can be improved by addig more convolution and pooling layers, and increasing the number of feature detectors'. Don't forget to upvote. Best Regards.

covidaid

COVID-19 Detection Using Chest X-Ray

crop_and_weed_detection

we made the crop and weed detection model using YOLOV3 on agricultural image data.

data-science-complete-tutorial

For extensive instructor led learning

dataset

Crop/Weed Field Image Dataset

deep-learning-keras-tf-tutorial

Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.

deep_learning_ai_lessons

Deep learning tutorials

deepface

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

harvestify

A machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.

hierbanetv1-a-novel-cnn-architecture

HierbaNetV1 is a novel convolutional neural network (CNN) architecture with a cutting-edge feature extraction technique

html

image-based-crop-anomaly-detection

A Convolutional Neural Network approach for image-based anomaly detection in smart agriculture

keras-applications

Reference implementations of popular deep learning models.

labelimg

🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images

machine-learning-using-python

Open Machine learning Projects

machine_learning_ai_lessons

Basics of Machine learning end to end input data

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

opencv_extra

OpenCV extra data

pybonirob

Set of tools to access bonirob datasets in Python

real-time-crop-recognition

code for

rice-crop-insects-and-weed-detection-using-faster-r-cnn

As the increase in the world population the demand of the rice is also increases. In order to increase the growth of rice in the rice crop it is necessary to detect the weed and insects in the rice crop to minimize the growth of weed and insects so that the growth of the rice can be increased.Insect and Weed detection is the important factor to be analyzed. Unmanned Air Vehicle (UAV) is used for data acquisition of rice crop in different phases and states so that high quality of RGB images can be captured. In which we have taken 15 different types of rice crop insects species images and different phases of weed images to train the model. The proposed method facilitates the extraction of weed and insects into the rice crop field using deep learning concept faster region-based convolutional neural networks(Faster R-CNNs) it is implemented using Python3 with the help of Tensorflow API. The result shows that Faster R-CNN method is the state of arts method for detection and classification of weed and insects with good accuracy rate.

sorghumweeddataset_classification

‘SorghumWeedDataset_Classification’ is a crop-weed research dataset with 4312 data samples, which can be used for multiclass image classification.

sorghumweeddataset_segmentation

‘SorghumWeedDataset_Segmentation’ is created to address real-time weed challenges precisely and encourage weed research using computer vision applications.

spark

Apache Spark - A unified analytics engine for large-scale data processing

srmjan22

time-series-r

This is a collection of R scripts from my Time Series Analysis course.

weed_detection

files