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Estimating crop yield prior to harvest plays a crucial role in agricultural decision making. Machine learning models that use remote sensing (RS) data can provide quick and early estimates of crop yield. Previously, timeseries gross primary production (GPP) synthesized from RS data has been employed in agricultural applications. However, relevant research on using GPP data and deep learning models in crop yield prediction is scarce. The aim of this study was to assess the capability of MODIS-derived GPP data and deep transfer learning to predict crop yield. We developed and trained deep neural network (DNN), one-dimensional convolutional neural network (1D-CNN), and gated recurrent unit (GRU) models to predict county-level corn and soybean yields. Then we evaluated the performance of crop-type and location transfer learning strategies in crop yield prediction. The results of this study can be summarized as follows: (1) GPP data and deep transfer learning have the potential to predict crop yield with reasonable prediction accuracy; (2) in the context of transfer learning, crop-type transfer learning produced a coefficient of determination (R2) ranging from 0.521 to 0.784 for soybean and from 0.644 to 0.903 for corn. Meanwhile, location transfer learning yielded R2 values ranging from 0.690 to 0.931 for the Great Plains (GP) climatic zone and 0.480 to 0.801 for the Eastern Temperate Forest (ETF) climatic zone; and (3) For corn and soybean yield predictions, the 1D-CNN model (R2 = 0.864 for corn and 0.750 for soybean) outperformed the DNN and GRU models. Our results reveal that MODIS-derived GPP data and deep transfer learning can be used to effectively predict county-level crop yield.

Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R2 = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R2 = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R2 = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R2 and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data. [ABSTRACT FROM AUTHOR]

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn's vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production. [ABSTRACT FROM AUTHOR]

Ensuring food security in precision agriculture demands early prediction of corn yield in the USA at international, regional, and local levels. Accurate corn yield estimation can play a crucial role in averting famine by offering insights into food availability during the growing season. To address this, we propose a Concatenate-based 2D-CNN-BILSTM model that integrates Sentinel-1, Sentinel-2, and Soil GRIDS (global gridded soil information) data for corn yield estimation in Iowa State from 2018 to 2021. This approach utilizes Sentinel-2 features, including spectral bands (Blue, Green, Red, Red Edge 1/2/3, NIR, n-NIR, and SWIR 1/2), and vegetation indices (NDVI, LSWI, DVI, RVI, WDRVI, SAVI, VARIGREEN, and GNDVI), alongside Sentinel 1 features (VV, VH, difference VV, and VH, and RVI), and soil data (Silt, Clay, Sand, CEC, and pH) as initial inputs. To extract high-level features from this data each month, a dedicated 2D-CNN was designed. This 2D-CNN concatenates high-level features from the previous month with low-level features of the subsequent month, serving as input features for the model. Additionally, to incorporate single-time soil data features, another 2D-CNN was implemented. Finally, high-level features from soil, Sentinel-1, and Sentinel-2 data were concatenated and fed into a BILSTM layer for accurate corn yield prediction. Comparative analysis against random forest (RF), Concatenate-based 2D-CNN, and 2D-CNN models, using metrics like RMSE, MAE, MAPE, and the Index of Agreement, revealed the superiority of our model. It achieved an Index of Agreement of 84.67% with an RMSE of 0.698 t/ha. The Concatenate-based 2D-CNN model also performed well with an RMSE of 0.799 t/ha and an Index of Agreement of 72.71%. The 2D-CNN model followed closely with an RMSE of 0.834 t/ha and an Index of Agreement of 69.90%. In contrast, the RF model lagged with an RMSE of 1.073 t/ha and an Index of Agreement of 69.60%. Integration of Sentinel 1–2 and Soil-GRIDs data with the Concatenate-based 2D-CNN-BILSTM model significantly improved accuracy. Combining soil data with Sentinel 1–2 features reduced the RMSE by 16 kg and increased the Index of Agreement by 2.59%. This study highlighted the potential of advanced machine learning (ML)/deep learning (DL) models in achieving precise and reliable predictions, which could support sustainable agricultural practices and food-security initiatives.

• This study evaluated the feasibility of accurately predicting corn (Zea mays) yield at the management zone level. • Data fusion of visible and near-infrared spectroscopy-derived soil properties, remote sensing-derived crop spectral indices, and machine learning algorithms was used. • A clustering analysis was used to develop MZs in order to implement variable-rate nitrogen fertilization in a drip-irrigated corn field. • The developed models demonstrated moderate accuracy for corn yield prediction. • Further research should include supplementary spectral crop canopy indices and alternative deep and machine learning approaches. The integration of advanced technologies, such as soil proximal sensing, remote sensing, and machine learning, has revolutionized agricultural practices, particularly for corn yield prediction. This interdisciplinary approach harnesses the power of cutting-edge sensors to gather high-resolution data on soil conditions coupled with remote sensing technologies that provide a comprehensive view of crop health and environmental factors. This study aimed to evaluate the feasibility of accurately predicting corn (Zea mays) yield at the management zones (MZs) level using the fusion of visible and near-infrared spectroscopy (Vis-NIRS)-derived soil properties, remote sensing-derived crop spectral indices, and machine learning algorithms. Clustering analysis was used to develop MZs to implement variable-rate nitrogen fertilization (VRNF) in a drip-irrigated corn field. Site-specific models to forecast corn yield at the MZs level were developed using Sentinel 2A-derived spectral indices and machine learning regression algorithms. Partial least squares Vis-NIR spectral regression modelling for MZs development achieved high accuracy in terms of the coefficient of determination (R2) which was ranged from 0.60 to 0.99 in cross-validation and from 0.52 to 0.78 in online validation. The developed corn yield prediction models demonstrated moderate efficacy, as evidenced by the R2 values ranging from 0.50 to 0.71. Further research should include supplementary spectral crop canopy indices and the application of alternative deep and machine learning approaches to improve the accuracy of the prediction models. [ABSTRACT FROM AUTHOR]

Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R2 = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R2 = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R2 = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R2 and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data.

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn’s vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.

Crop yield varies spatially and temporally due to several limiting factors, including soil type, nutrient availability water availability, weather, and crop varieties. It is difficult to quantify the severity of these limiting factors on crop yield as well as the spatial variation of crop yield before harvest. Remotely sensed aerial images can be used to improve estimates of mid-season crop yields. However, the success of such approaches is typically dependent on the image classification technique used. In this article, we develop an automated self-organizing map (SOM) neural image classification technique and evaluate it with respect to the widely used ISODATA clustering method using visual assessment of preservation of image texture. We also develop hybrid SOM-back-propagation neural network (BPNN) models to predict crop yield from the Oakes Irrigation Test Area (OITA) research sites at Oakes, North Dakota. We used the SOM image classification technique to classify the composite visible and near-infrared (NIR) band aerial images from several parts of the OITA research site. SOM cluster centroids from individual grid images were used as inputs to the BPNN yield prediction models created for the years 1997, 1998, 1999, 2000, and 2001. The models using individual red, green, and NIR band digital data provided average corn yield prediction accuracies of more than 80% for every year, except for the NIR model in 1998 (45%). Individual year models were validated with individual band training and testing datasets from the other four years. Models using the green band dataset provided better validation results. Average corn yield prediction accuracies ranged from 55% to a maximum of 96%. Average corn yield prediction accuracies of validated models using the red band and NIR band datasets ranged from 33% to a maximum of 94% and from 36% to a maximum of 95%, respectively. This article establishes the use of hybrid SOM-BPNN modeling techniques to predict corn yield from aerial images at mid-crop season. [ABSTRACT FROM AUTHOR]

As the world's leading corn producer, the United States supplies more than 30% of the global corn production. Accurate and timely estimation of corn yield is therefore essential for commodity trading and global food security. Recently, several deep learning models have been explored for corn yield forecasting. Despite success, most existing models only provide yield estimations without quantifying the uncertainty associated with the predictions. Also, the traditional deep learning approaches typically require a large training set and are easily prone to overfitting when the number of samples in the training set is relatively small. To address these limitations, in this study, we developed a county-level corn yield prediction model based on Bayesian Neural Network (BNN) using multiple data sources that are publicly available, including time-series satellite products, sequential climate observations, soil property maps, and historical corn yield records. Using preceding years since 2001 for model training, the developed BNN model achieved an average coefficient of determination (R2) of 0.77 for late-season prediction across the U.S. Corn Belt in testing years 2010–2019, and outperformed five other state-of-the-art machine learning models. Detailed evaluation in three representative testing years demonstrated that the proposed BNN model could accurately estimate corn yield not only in normal years but also in abnormal years when extreme weather events happened. Moreover, the timeliness of the prediction was evaluated within the growing season with an R2~0.75 achieved by middle August, which is about 2 months before the harvest. We also assessed the predictive uncertainty, and more than 84% of the observed yield records were successfully enveloped in the 95% confidence interval of the predictive yield distribution. Our results also showed that the uncertainty level decreased steadily as time proceeded and stabilized around early August. Uncertainties in yield prediction were mainly induced by the observation noise and also related to the interannual and seasonal variabilities of environmental stress such as heat and water stress. This paper provides a robust framework for the within-season prediction of crop yield and highlights the need to obtain a deeper understanding of the effects of environmental stress on agricultural productivity and crop yield estimation. • A Bayesian neural network was developed for corn yield and uncertainty estimation. • The developed model outperformed five widely used machine learning models. • The near-optimal performance was achieved 2 months before the harvest. • Predictive uncertainty could estimate the confidence level of yield prediction. • RS data noises and environmental stress increased predictive uncertainty. [ABSTRACT FROM AUTHOR]