Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network.

ABSTRACT: Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong'an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic model. The evaluation metrics were selected as the root mean square error (RMSE) and the coefficient of determination R². Through comparison, the R² of the improved model was foun

SUBMITTER: Ma L 

PROVIDER: S-EPMC9610480 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature