Prediction of Field-Scale Wheat Yield Using Machine Learning Method and Multi-Spectral UAV Data

Bian, Chaofa; Shi, Hongtao; Wu, Suqin; Zhang, Kefei; Wei, Meng; Zhao, Yuhao; Sun, Yuqing; Zhuang, Huifu; Zhang, Xuewei; Chen, Shuo

doi:10.3390/rs14061474

Cited by 57 publications

(41 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Satellite remote sensing (RS) is widely applied in agricultural research. Vegetation indices (VIs) calculated from satellite data are the most common means of predicting crop yield ( Bian et al., 2022 ). VIs can describe such biotic features as the canopy structure, chlorophyll, and nitrogen content of crops and different indices indicate different features.…”

Section: Introductionmentioning

confidence: 99%

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

et al. 2023

View full text Add to dashboard Cite

Accurate and timely estimation of cotton yield over large areas is essential for precision agriculture, facilitating the operation of commodity markets and guiding agronomic management practices. Remote sensing (RS) and crop models are effective means to predict cotton yield in the field. The satellite vegetation indices (VIs) can describe crop yield variations over large areas but can’t take the exact environmental impact into consideration. Climate variables (CVs), the result of the influence of spatial heterogeneity in large regions, can provide environmental information for better estimation of cotton yield. In this study, the most important VIs and CVs for estimating county-level cotton yield across Xinjiang Province were screened out. We found that the VIs of canopy structure and chlorophyll contents, and the CVs of moisture, were the most significant factors for cotton growth. For yield estimation, we utilized four approaches: least absolute shrinkage and selection operator regression (LASSO), support vector regression (SVR), random forest regression (RFR) and long short-term memory (LSTM). Due to its ability to capture temporal features over the long term, LSTM performed best, with an R2 of 0.76, root mean square error (RMSE) of 150 kg/ha and relative RMSE (rRMSE) of 8.67%; moreover, an additional 10% of the variance could be explained by adding CVs to the VIs. For the within-season yield estimation using LSTM, predictions made 2 months before harvest were the most accurate (R2 = 0.65, RMSE = 220 kg/ha, rRMSE = 15.97%). Our study demonstrated the feasibility of yield estimation and early prediction at the county level over large cotton cultivation areas by integrating satellite and environmental data.

show abstract

Section: Introductionmentioning

confidence: 99%

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Previous studies have explored correlations between multitemporal VIs and crop yields based on Random Forest (RF), Support Vector Machines (SVM), etc. However, these traditional deep learning-based methods require large, extensive data, limiting their application to yield predictions with fewer data [73][74][75][76]. Therefore, the BHT-ARIMA model applied in yield estimation has greater potential than other methods.…”

Section: Discussionmentioning

confidence: 99%

Winter Wheat Yield Estimation Based on Optimal Weighted Vegetation Index and BHT-ARIMA Model

Deng

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

This study aims to use remote sensing (RS) time-series data to explore the intrinsic relationship between crop growth and yield formation at different fertility stages and construct a high-precision winter wheat yield estimation model applicable to short time-series RS data. Sentinel-2 images were acquired in this study at six key phenological stages (rejuvenation stage, rising stage, jointing stage, heading stage, filling stage, filling-maturity stage) of winter wheat growth, and various vegetation indexes (VIs) at different fertility stages were calculated. Based on the characteristics of yield data continuity, the RReliefF algorithm was introduced to filter the optimal vegetation index combinations suitable for the yield estimation of winter wheat for all fertility stages. The Absolutely Objective Improved Analytic Hierarchy Process (AOIAHP) was innovatively proposed to determine the proportional contribution of crop growth to yield formation in six different phenological stages. The selected VIs consisting of MTCI(RE2), EVI, REP, MTCI(RE1), RECI(RE1), NDVI(RE1), NDVI(RE3), NDVI(RE2), NDVI, and MSAVI were then fused with the weights of different fertility periods to obtain time-series weighted data. For the characteristics of short time length and a small number of sequences of RS time-series data in yield estimation, this study applied the multiplexed delayed embedding transformation (MDT) technique to realize the data augmentation of the original short time series. Tucker decomposition was performed on the block Hankel tensor (BHT) obtained after MDT enhancement, and the core tensor was extracted while preserving the intrinsic connection of the time-series data. Finally, the resulting multidimensional core tensor was trained with the Autoregressive Integrated Moving Average (ARIMA) model to obtain the BHT-ARIMA model for wheat yield estimation. Compared to the performance of the BHT-ARIMA model with unweighted time-series data as input, the weighted time-series input significantly improves yield estimation accuracy. The coefficients of determination (R2) were improved from 0.325 to 0.583. The root mean square error (RMSE) decreased from 492.990 to 323.637 kg/ha, the mean absolute error (MAE) dropped from 350.625 to 255.954, and the mean absolute percentage error (MAPE) decreased from 4.332% to 3.186%. Besides, BHT-ARMA and BHT-CNN models were also used to compare with BHT-ARIMA. The results indicated that the BHT-ARIMA model still had the best yield prediction accuracy. The proposed method of this study will provide fast and accurate guidance for crop yield estimation and will be of great value for the processing and application of time-series RS data.

show abstract

“…In the next section, we will discuss the recommended ML/DL techniques for the prediction of weight in pounds based on food choice in the proposed methodology. Moreover, in [14][15][16][17][18][19][20], ML approaches are presented to predict suitable food selection with respect to individual choice, food environment, and preference, and obesity, regulation during food choice, food prices, supply chain risk prediction, and crop yield prediction. Shams et al [21] present a healthy nutrition analysis, as they recommended a suitable diet during the COVID-19 pandemic.…”

Section: Related Workmentioning

confidence: 99%

Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

Elshewey¹,

Shams²,

Tarek³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Food choice motives (i.e., mood, health, natural content, convenience, sensory appeal, price, familiarities, ethical concerns, and weight control) have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the world. Researchers from several domains have presented several models addressing issues influencing food choice over the years. However, a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure. In this paper, four Deep Learning (DL) models and one Machine Learning (ML) model are utilized to predict the weight in pounds based on food choices. The Long Short-Term Memory (LSTM) model, stacked-LSTM model, Conventional Neural Network (CNN) model, and CNN-LSTM model are the used deep learning models. While the applied ML model is the K-Nearest Neighbor (KNN) regressor. The efficiency of the proposed model was determined based on the error rate obtained from the experimental results. The findings indicated that Mean Absolute Error (MAE) is 0.0087, the Mean Square Error (MSE) is 0.00011, the Median Absolute Error (MedAE) is 0.006, the Root Mean Square Error (RMSE) is 0.011, and the Mean Absolute Percentage Error (MAPE) is 21. Therefore, the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM, CNN, CNN-LSTM, and KNN regressor.

show abstract

Prediction of Field-Scale Wheat Yield Using Machine Learning Method and Multi-Spectral UAV Data

Cited by 57 publications

References 79 publications

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

Winter Wheat Yield Estimation Based on Optimal Weighted Vegetation Index and BHT-ARIMA Model

Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

Contact Info

Product

Resources

About