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Early prediction of winter wheat yield at the regional scale is essential for food policy making and food security, especially in the context of population growth and climate change. Agricultural big data and artificial intelligence (AI) are key technologies for smart agriculture, bringing cost-effective solutions to the agricultural sector. Deep learning-based crop yield forecast has currently emerged as one of the key methods for guiding agricultural production. In this study, we proposed a Bayesian optimization-based long- and short-term memory model (BO-LSTM) to construct a multi-source data fusion-driven crop growth feature extraction algorithm for winter wheat yield prediction. The yield prediction performance of BO-LSTM, support vector machine (SVM), and least absolute shrinkage and selection operator (Lasso) was then compared with multi-source data as input variables. The results showed that effective deep learning hyperparameter optimization is made possible by Bayesian optimization. The BO-LSTM (RMSE = 177.84 kg/ha, R2 = 0.82) model had the highest accuracy of yield prediction with the input combination of "GPP + Climate + LAI + VIs". BO-LSTM and SVM (RMSE = 185.7 kg/ha, R2 = 0.80) methods outperformed linear regression Lasso (RMSE = 214.5 kg/ha, R2 = 0.76) for winter wheat yield estimation. There were also differences between machine learning and deep learning, BO-LSTM outperformed SVM. indicating that the BO-LSTM model was more effective at capturing data correlations. In order to further verify the robustness of the BO-LSTM method, we explored the performance estimation performance of BO-LSTM in different regions. The results demonstrated that the BO-LSTM model could obtain higher estimation accuracy in regions with concentrated distribution of winter wheat cultivation and less influence of human factors. The approach used in this study can be expected to forecast crop yields, both in regions with a deficit of data and globally; it can also simply and effectively forecast winter wheat yields in a timely way utilizing publicly available multi-source data. [ABSTRACT FROM AUTHOR]

SPAD value was measured by a portable chlorophyll instrument, which can reflect the relative chlorophyll content of vegetation well. Chlorophyll is an important organic chemical substance in plants that acquires and transmits energy during photosynthesis. The continuous spectral curve of winter wheat can be obtained rapidly in a specific band range by using hyperspectral remote sensing technology to estimate the SPAD value of winter wheat, which is of great significance to the growth monitoring and yield estimation research of winter wheat. In this study, with winter wheat as the research object, the spectral data and corresponding SPAD value in different growth stages were used as the data source, 20 kinds of data preprocessing spectra and sensitive spectral indices set the data as model input values, the partial least square regression (PLSR) model was established to estimate the SPAD value, and the model estimation results of different model input values at different growth stages were compared in detail. The results showed that the set of sensitive spectral indices selected in this study as input values can effectively improve the accuracy and stability of the PLSR model. In addition, the effects of 20 spectral data pretreatment methods on the estimation results of the SPAD value were compared and analyzed in different growth stages. It was found that the spectral data pretreated by the combination of wavelet packet denoising, first-order derivative transformation and principal component analysis can improve the accuracy and stability of PLSR model, and it is suitable for all growth stages. The results also showed that the estimation model is highly sensitive to the standard deviation of the SPAD value (STDchl) in sample sets. When the standard deviation is greater than 5.5 SPAD, the larger the STDchl is, the higher the model estimation accuracy is, and the more stable the model is. At this time, the model estimation accuracy is higher (R2V is greater than 0.5, ratio of performance to deviation is greater than 1.4), which can meet the estimation requirements of the SPAD value. [ABSTRACT FROM AUTHOR]

• Using hyperspectral data to retrieve wheat chlorophyll content. • Ridge regression algorithm and Gradient Boosting Regression Tree algorithm are used to establish the model. • The nonlinear inversion model obtained by the gradient boosting regression tree algorithm has higher accuracy than the linear model. Chlorophyll is the basic component of plant leaves. It is closely related to the use of solar radiation, the absorption of atmospheric carbon dioxide and the process of photosynthesis. The level of chlorophyll content has a very important indication of photosynthetic efficiency and development status of plants. Because hyperspectral data has the problem of large amount of data and high redundancy, this paper deals with hyperspectral data from two aspects to establish a prediction model of leaf chlorophyll content in different growth stages of winter wheat. On the one hand, this article does not perform dimensionality reduction processing on hyperspectral data. That is, for the full-band spectral data, the Ridge regression algorithm (abbreviated as Ridge) is used to establish a multiple linear prediction model. Use the Gradient Boosting Regression Tree algorithm (abbreviated as GBRT) and the feedforward neural network based on Back Propagation algorithm (abbreviated as BP) to establish two multiple nonlinear prediction models. On the other hand, dimensionality reduction is performed on hyperspectral data. Use Elastic Net algorithm (abbreviated as EN) to reduce the dimensionality of hyperspectral data to extract sensitive bands, and then use GBRT algorithm and feedforward neural network based on Back Propagation algorithm to establish multivariate nonlinear prediction models (abbreviated as EN-GBRT, EN-BP). After the model accuracy test, the conclusions are as follows: (1) The nonlinear prediction model obtained by the gradient boosting regression tree algorithm has higher accuracy and universality than the linear model in each growth period of winter wheat (R2 GBRT ＞R2 Ridge). (2) The EN-GBRT prediction model has greater advantages in predicting the chlorophyll content of winter wheat at each growth stage (R2 EN-GBRT ＞R2 EN-BP) within two nonlinear prediction models. (3) Comprehensive comparison of the prediction accuracy of GBRT and EN-GBRT models in each growth period. In the jointing period, heading period, flowering period, filling period, milking period and maturity period of winter wheat, the accuracy of prediction using the GBRT model was higher (R2 jointing = 0.81, R2 heading = 0.50, R2 flowering = 0.74, R2 filling = 0.41, R2 milking = 0.84, R2 maturity = 0.95). In the whole growth period of winter wheat, the prediction accuracy of EN-GBRT model was better (R2 whole = 0.85). [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of the winter wheat planting area and its spatial distribution is essential for the implementation of crop growth monitoring and yield estimation, and hence for the development of national agricultural production and food security. In remotely sensed winter wheat mapping based on spectral similarity, the reference curve is obtained by averaging multiple standard curves, which limits mapping accuracy. We propose a spectral reconstruction method based on singular value decomposition (SR-SVD) for winter wheat mapping based on the unique growth characteristics of crops. Using Sentinel-2 A/B satellite data, we tested the SR-SVD method in Puyang County, and Shenzhou City, China. Performance was increased, with the optimal overall accuracy and the Kappa of Puyang County and Shenzhou City were 99.52% and 0.99, and 98.26% and 0.97, respectively. We selected the spectral angle mapper (SAM) and Euclidean Distance (ED) as the similarity measures. Compared to spectral similarity methods, the SR-SVD method significantly improves mapping accuracy, as it avoids excessive extraction, can identify more detailed information, and is advantageous in distinguishing non-winter wheat pixels. Three commonly used supervised classification methods, support vector machine (SVM), maximum likelihood (ML), and minimum distance (MD) were used for comparison. Results indicate that SR-SVD has the highest mapping accuracy and greatly reduces the number of misidentified pixels. Therefore, the SR-SVD method can achieve high-precision crop mapping and provide technical support for monitoring regional crop planting structure information. [ABSTRACT FROM AUTHOR]