外文摘要：Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybrid deep learning model that coordinates the outcomes of a classification model and a regression model in deep learning via the shared layers to predict the rice crop yield. Three statistical analyses on the features, including Pearson correlation coefficients (PCC), Shapley additive explanations (SHAP), and recursive feature elimination with cross-validation (RFECV), are proposed to select the most relevant ones for the predictive goal to reduce the model training time. The data preprocessing normalizes the features of the collected data into specific ranges of values and then reformats them into a three-dimensional matrix. As a result, the root-mean-square error (RMSE) of the proposed model in rice yield prediction has achieved 344.56 and an R-squared of 0.64. The overall performance of the proposed model is better than the other deep learning models, such as the multi-parametric deep neural networks (MDNNs) (i.e., RMSE = 370.80, R-squared = 0.59) and the artificial neural networks (ANNs) (i.e., RMSE = 550.03, R-squared = 0.09). The proposed model has demonstrated significant improvement in the predictive results of distinguishing high yield from low yield with 90% accuracy and 94% F1 score.
外文关键词：remote sensing;deep learning;Convolutional Neural Network;crop yield prediction;multi-task learning
作者：Chang, Che-Hao;Lin, Jason;Chang, Jia-Wei;Huang, Yu-Shun;Lai, Ming-Hsin;Chang, Yen-Jen
作者单位：Agr Res Inst;Natl Chung Hsing Univ
期刊名称：AGRICULTURE-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14（4）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台