Tobacco yield estimation via multi-source data fusion and recurrent neural networks Article Swipe

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Mingzheng Zhang , Baoyuan Zhang , Chunjiang Zhao , Liping Chen , Yan Kuai , Cong Wang , Shuwen Jiang , Dong Chen , Qingzhen Zhu , Zhiyong Wang , Xiaohe Gu , Tianen Chen · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1016/j.jag.2025.104925

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.

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article

Language

en

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https://doi.org/10.1016/j.jag.2025.104925

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gold

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55

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https://openalex.org/W4415760850

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https://openalex.org/W4415760850

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DOI

https://doi.org/10.1016/j.jag.2025.104925

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Title

Tobacco yield estimation via multi-source data fusion and recurrent neural networks

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article

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en

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2025

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2025-11-01

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Mingzheng Zhang, Baoyuan Zhang, Chunjiang Zhao, Liping Chen, Yan Kuai, Cong Wang, Shuwen Jiang, Dong Chen, Qingzhen Zhu, Zhiyong Wang, Xiaohe Gu, Tianen Chen

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https://doi.org/10.1016/j.jag.2025.104925

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Yes

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gold

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https://doi.org/10.1016/j.jag.2025.104925

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0

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55

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