An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China

Tian, Hailin; Wang, Pengxin; Tansey, Kevin; Zhang, Jingqi; Zhang, Shuyu; Li, Hongmei

doi:10.1016/j.agrformet.2021.108629

Cited by 105 publications

(52 citation statements)

References 46 publications

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“…In Table 9, the comparison results performed on the wheat yield estimation data set in a different region than the Konya region, which constitutes the data set we used, are presented. R 2 and RMSE performance metrics obtained from Cao et al In another study on the estimation of wheat yield in the literature, Tian et al [30] developed the LSTM model. With this model, artificial neural networks and SVM, which are machine learning methods, are compared for wheat yield estimation.…”

Section: Figure 7 Proposed Lstm Model Performance Outputmentioning

confidence: 99%

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Buğday Veri̇m Tahmi̇ni̇ İçi̇n Yeni̇lemeli̇ Si̇ni̇r Aği Tabanli Model Geli̇şti̇rme

ÇETİNER

Kara

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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In the study carried out in line with the stated purposes, monthly rain, humidity and temperature data, wheat production amount, and wheat productivity data of Konya province between 1980-2020 were used. Using these data, wheat productivity estimation was performed with (Gated Recurrent Units) GRU and Long Short Term Memory (LSTM) methods, which are Recurrent Neural Network (RNN) based algorithms. When wheat productivity estimation performance was examined with the implemented GRU-based model, 0.9550, 0.0059, 0.0280, 0.0623, 7.45 values were obtained for the R 2 score, MSE, RMSE, MAE and MAPE values, respectively. In the performance results obtained with the LSTM method, which is another RNN-based method, 0.9667, 0.0054, 0.0280, 0.0614, 7.33 values were obtained for the R 2 score, MSE, RMSE, MAE and MAPE values, respectively. Although the LSTM method gave better results than the GRU method, the training modelling time of the LSTM method took longer than that of the GRU method.

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Section: Figure 7 Proposed Lstm Model Performance Outputmentioning

confidence: 99%

“…Kim et al [29] developed a deep neural network model for crop yield prediction using a meteorological dataset from 2006 to 2015. Tian et al [30] developed an LSTM model using meteorological data from the People's Republic of China. With the model they developed, they estimate the wheat yield.…”

Section: Introductionmentioning

confidence: 99%

Buğday Veri̇m Tahmi̇ni̇ İçi̇n Yeni̇lemeli̇ Si̇ni̇r Aği Tabanli Model Geli̇şti̇rme

ÇETİNER

Kara

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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“…2, the memory unit of the LSTM neural network maintains three gates at each time step, including the forget gate, input gate and output gate. Due to the gating, the LSTM neural network can realize filtering and information storage functions [42].…”

Section: ) Lstmmentioning

confidence: 99%

Using LSTM Neural Network Based on Improved PSO and Attention Mechanism for Predicting the Effluent COD in a Wastewater Treatment Plant

Liu

Shi²,

Liu

et al. 2021

IEEE Access

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Enhancing the monitoring capabilities of wastewater treatment plant (WWTP) key features can accomplish accurate prediction to help WWTPs develop a plan, which is of great significance to control regional water environmental pollution. Chemical oxygen demand (COD) is one of the key features of wastewater treatment. Traditional monitoring methods are time consuming and have high costs making it difficult to meet the needs of rapid monitoring in practical applications. To address this issue, a method for optimizing a long short term memory (LSTM) neural network model based on adaptive hybrid mutation particle swarm optimization (AHMPSO) and an attention mechanism (AM) is proposed. As the hyperparameters of the LSTM are difficult to select, AHMPSO is used to optimize the LSTM. A nonlinear variable inertia weight with random factors is introduced to balance the global search ability and the local search ability and to improve the convergence speed of the PSO algorithm. In addition, the hybrid mutation strategy is added in the search process to reduce the risk of particles falling into local optimal solutions. Finally, an AM is added to the LSTM model to mine local water quality features to improve the effluent COD prediction accuracy. Compared with other models (LSTM, LSTM-AM, and PSO-LSTM-AM), the RMSE(the root mean square error) of the optimized model decreased by 7.803%-19.499%, the MAE(the mean absolute error) of the optimized model decreased by 9.669%-27.551%, the MAPE(the mean absolute error) of the optimized model decreased by 8.993%-25.996%, and the R 2 (the coefficient of determination) value of the optimized model increased by 3.313%-11.229%. The experimental results show that the optimized model has better performance and achieves a more accurate prediction of the effluent COD.

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“…The main advantages of statistical models are their 40 simplicity and less dependence on calibration data; however, they are particularly vulnerable to co-linearity problems and noise of inputs (Lobell and Burke, 2010). Fortunately, machine learning (ML) approaches provide innovative alternatives to statistical models and can address the nonlinear relationships between the predictor variables and crop yield, which have demonstrated their superior performance in many applications (Cai et al, 2019;Cao et al, 2021;Li et al, 2021;Jin et al, 2018). For instance, Kang et al (2020) compared the performances of a set of statistical and ML methods and indicated that 45 all ML models achieved better accuracy in predicting county-level maize yield.…”

mentioning

confidence: 99%

“…Emerging breakthroughs in algorithms such as deep learning (DL) approaches have accomplished more accurate crop yield estimation (Jeong et al, 2022;. For example, the long short-term memory (LSTM) model adopts a recurrent neural network structure that can recognize sequential information for long time periods and capture sophisticated nonlinear relationships, showing superior performance over ML models in yield prediction (Jiang et al, 2019;Tian et al, 2021). 50…”

mentioning

confidence: 99%

GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approaches

Luo

Zhang

Cao

et al. 2022

Preprint

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Abstract. Accurate and spatially explicit information on crop yield over large areas is paramount for ensuring global food security and guiding policy-making. However, most public datasets are coarse resolution in both space and time. Here, we used data-driven models to develop a 4-km dataset of global wheat yield (GlobalWheatYield4km) from 1982 to 2020. First, we proposed a phenology-based approach to map spatial distribution. Then we determined the optimal grid-scale yield estimation model by comparing the performance of two data-driven models (i.e., Random Forest (RF) and Long Short-Term Memory (LSTM)), with publicly available data (i.e., satellite and climatic data from the Google Earth Engine (GEE) platform, soil properties, and subnational statistics covering ~11000 political units). The results showed that GlobalWheatYield4km captured 82 % of yield variations with RMSE of 619.8 kg/ha. In addition, our dataset had a higher accuracy (R2 ~0.73) as compared with Spatial Production Allocation Model (R2 ~ 0.49) across all regions and years. The GlobalWheatYield4km dataset will play important roles in modelling crop system and assessing climate impact over larger areas ((DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.10025006; Luo et al., 2022b).

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An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China

Cited by 105 publications

References 46 publications

Buğday Veri̇m Tahmi̇ni̇ İçi̇n Yeni̇lemeli̇ Si̇ni̇r Aği Tabanli Model Geli̇şti̇rme

Buğday Veri̇m Tahmi̇ni̇ İçi̇n Yeni̇lemeli̇ Si̇ni̇r Aği Tabanli Model Geli̇şti̇rme

Using LSTM Neural Network Based on Improved PSO and Attention Mechanism for Predicting the Effluent COD in a Wastewater Treatment Plant

GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approaches

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