Multi-Resolution-Based Deep Learning Approach for Rice Field Monitoring

In India, agribusiness is directly dependent on the precise monitoring of paddy areas to take considerable supportive actions toward food security. For this, satellite-based data is considered one of the effective solutions. The goal of this study is to design an intelligent framework to determine the crop area by using satellite data that is easily available. In this article, a Multi-resolution Deep Neural Network (MR-DNN) is proposed to determine rice fields by performing multi-streaming classification. The task of prediction is performed on Landsat 8 satellite images with high spatial resolution. The prediction performance of the proposed model is justified by comparing the calculated outcomes from a few selected methods. The proposed model has achieved the highest prediction performance in terms of the F1 score with the accuracy of 95.40% and 95.12% for Punjab and West-Bengal dataset as compared to the selected models, such as DeepLabV3+, Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Light-Gradient Boosting Method (LGBM), eXtreme Gradient Boosting (XGBoost), Spectral, and Threshold. In this manner, the empirical evaluation defines the prediction performance of the proposed model over the visual interpretation of the maps as well as seasonal impacts.