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atmosphericcorrection

6s模型大气校正python版本，支持GF1、2,Landsat-8,Sentinel-2等影像

crop_row_detection

A simple crop-row detection algorithm using the opencv libraries for use in agricultural robotics

learn_frcnn

learn frcnn（train, predict, eval）

ls8-oli-mapping

Mapping of rice growth phases and bare land using Landsat-8 OLI and machine learning algorithms

ml_utils

pcl-notes

pcl learning notes

pytorch_lanenet

pytorch_yolo3

pytorch_yolo4

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.

slam_paper

learning SLAM,curse,paper and others

visual-multi-crop-row-navigation

Release code for crop row following