Automatic Grassland Degradation Estimation Using Deep Learning
Authors: Xiyu Yan, Yong Jiang, Shuai Chen, Zihao He, Chunmei Li, Shu-Tao Xia, Tao Dai, Shuo Dong, Feng Zheng
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed method achieves satisfactory accuracy in grassland degradation estimation. |
| Researcher Affiliation | Collaboration | 1Dept. of Computer Science and Technology, Tsinghua University 2PCL Research Center of Networks and Communications, Peng Cheng Laboratory 3Baidu, Inc. 4Dept. of Computer Technology and Applications, Qinghai University 5Dept. of Computer Science and Engineering, Southern University of Science and Technology |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | To this end, we build an original Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset), with a large number of grassland images captured from the wild. ... we create a labeled dataset Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset). ... Finally, we randomly divide the dataset into a training set and a test set in the ratio of 2,095:800, which is detailed in Table 2. |
| Dataset Splits | Yes | Finally, we randomly divide the dataset into a training set and a test set in the ratio of 2,095:800, which is detailed in Table 2. ... Table 2: The number of images of each stage in AGDE-Dataset. Train Set Total 2,095 Test Set Total 800 |
| Hardware Specification | Yes | all experiments are conducted on a GTX1080Ti. |
| Software Dependencies | No | The paper does not specify the version numbers for any software dependencies. It only states: "More detailed experimental parameters are specified in Supplementary Materials." |
| Experiment Setup | Yes | Besides, γ in Eq. (5) and Eq. (6) are set to 2. The size of the input images is padded to 256 x 341, which is the largest size of images in the dataset. |