Pinned Repositories
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.
shimanStone's Repositories
shimanStone/learn_frcnn
learn frcnn（train, predict, eval）
shimanStone/AtmosphericCorrection
6s模型大气校正python版本，支持GF1、2,Landsat-8,Sentinel-2等影像
shimanStone/crop_row_detection
A simple crop-row detection algorithm using the opencv libraries for use in agricultural robotics
shimanStone/LS8-OLI-MAPPING
Mapping of rice growth phases and bare land using Landsat-8 OLI and machine learning algorithms
shimanStone/ml_utils
shimanStone/PCL-Notes
pcl learning notes
shimanStone/pytorch_lanenet
shimanStone/pytorch_yolo3
shimanStone/pytorch_yolo4
shimanStone/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.
shimanStone/slam_paper
learning SLAM,curse,paper and others
shimanStone/visual-multi-crop-row-navigation
Release code for crop row following