Tobacco yield estimation via multi-source data fusion and recurrent neural networks - Zhang, Mingzheng; Zhang, Baoyuan; Zhao, Chunjiang; Chen, Liping; Kuai, Yan; Wang, Cong; Jiang, Shuwen; Chen, Dong; Zhu, Qingzhen; Wang, Zhiyong; Gu, Xiaohe; Chen, Tian’en (International journal of applied earth observation and geoinformation, 2025)
In China, tobacco production must strictly follow the yield plan set by the higher authorities. In this context, accurate and stable yield estimation is meaningful for effective production management. In this paper, we adopted a multi-source data fusion strategy to develop the yield estimation models for tobacco. The data used include unmanned aerial vehicle (UAV)-borne hyperspectral features (HF), biophysical parameters (BPP) collected in the field, and biochemical parameters (BCP) measured in the laboratory. Since the crop state at different growth stages both affect the final yield, we employed two typical recurrent neural network (RNN) algorithms, long short-term memory (LSTM) and gated recurrent unit (GRU), for modeling. The random forest (RF) algorithm was selected as the baseline scheme. In addition, we designed a one-dimensional convolutional autoencoder (AEC1D) to unify the input dimensions of raw data from different years. It was found that yield estimation performance from multi-source data was more accurate than using any single feature. The GRU model with the HF+BCP+BPP combination achieved the highest estimation accuracy, with an Rv2 of 0.705. The overall performance of LSTM and GRU models was also better than that of RF. We also quantified the contribution of each feature to the model, with HF, BPP, and BCP accounting for approximately 45%, 32%, and 23%, respectively. This study demonstrated the benefits of multi-source data fusion and RNN algorithms in estimating tobacco yields, which can be used to assist in site-specific crop management. •Construct tobacco yield estimation models using multi-source data integration strategy and RNN algorithms.•Design a one-dimensional autoencoder to unify the temporal dimensions of raw data from different years.•The GRU model achieved promising results, with an R2 of 0.802 on the training set and 0.705 on the test set