Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning

Crop yield estimation and prediction constitutes a key issue in agricultural management, particularly under the context of demographic pressure and climate change. Currently, the main challenge in estimating crop yields based on remotely sensed data and data-driven methods is how to cope with small datasets and the limited amount of annotated samples. In order to cope with small datasets and the limited amount of annotated samples and improve the accuracy of winter wheat yield estimation in the Guanzhong Plain, PR China, this study proposed a method of combining generative adversarial networks (GANs) and convolutional neural network (CNN) for comprehensive growth monitoring of winter wheat, in which the remotely sensed leaf area index (LAI), vegetation temperature condition index (VTCI) and meteorological data at four growth stages of winter wheat during 2012–2017 were generated as the inputs of multi-layer convolutional neural networks (CNNs), and GAN was employed to artificially increase the number of training samples. Then, a linear regression model between the simulated comprehensive growth monitoring (I) and the measured yields was established to estimate yields of winter wheat in the Guanzhong Plain pixel by pixel. The final results showed when GAN was used to double the size of the training samples, and the simulation values obtained by CNN based on augmented samples using GAN provided a better training (R2 = 0.95, RMSE = 0.05), validation (R2 = 0.54, RMSE = 0.16) and testing (R2 = 0.50, RMSE = 0.14) performance than that just using the original samples. The achieved best pixel-scale yield estimation accuracy of winter wheat (R2 = 0.50, RMSE = 591.46 kg/ha) in the Guanzhong Plain. These results showed that small samples can be enlarged by GAN, thus, more important features for reflecting the growth conditions and yields of winter wheat from the remotely sensed indices and meteorological indices can be extracted, and indicated that CNN accompanied with GAN could contribute a lot to the comprehensive growth monitoring and yield estimation of winter wheat and data augmentation methods are extremely useful for the application of small samples in deep learning.

Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.

Crop yield is one of the variables used to assess the impact of droughts on agriculture. Crop growth models calculate yield and variables related to plant development and become more suitable for crop yield estimation. However, these models are limited in that specific data are needed for computation. Given this limitation, machine learning (ML) models are often widely utilised instead, but their use with the spatial characteristics of droughts as input data is limited. This research explored the spatial extent of drought (area) as input data for building an approach to predict seasonal crop yield. This ML approach is made up of two components. The first includes polynomial regression (PR) models, and the second considers artificial neural network (ANN) models. In this approach, the purpose was to evaluate both types of ML models (PR and ANN) and integrate them into one operational tool. The logic is as follows: ANN models determine the most accurate predictions, but in practice, issues regarding data retrieval and processing can make the use of equations, i.e. PR, preferable. The proposed approach provides these PR equations to perform such calculations with early and preliminary input. The estimates can be further improved when the ANN models are run with the final input data. The results indicated that the empirical equations (PR) produced good predictions when using drought area as the input. ANN provides better estimates, in general. This research will improve drought monitoring systems for assessing drought effects. Since it is currently possible to calculate drought areas within these systems, the direct application of the prediction of drought effects is possible to integrate by following approaches such as the one presented or similar.

Crop yields are important for food security and people’s living standards, and it is therefore very important to predict the yield in a timely manner. This study used different vegetation indices and red-edge parameters calculated based on the canopy reflectance obtained from near-surface hyperspectral data and UAV hyperspectral data and used the partial least squares regression (PLSR) and artificial neural network (ANN) methods to estimate the yield of winter wheat at different growth stages. Verification was performed based on these two types of hyperspectral remote sensing data and the yield was estimated using vegetation indices and a combination of vegetation indices and red-edge parameters as the modeling independent variables, respectively, using PLSR and ANN regression, respectively. The results showed that, for the same data source, the optimal vegetation index for estimating the yield was the same in all of the studied growth stages; however, the optimal red-edge parameters were different for different growth stages. Compared with using only the vegetation indices as the modeling factor to estimate yield, the combination of the vegetation indices and red-edge parameters obtained superior estimation results. Additionally, the accuracy of yield estimation was shown to be improved by using the PLSR and ANN methods, with the yield estimation model constructed using the PLSR method having a better prediction effect. Moreover, the yield prediction model obtained using the near-surface hyperspectral sensors had a higher fitting and accuracy than the model obtained using the UAV hyperspectral remote sensing data (the results were based on the specific growth stressors, N and water supply). This study shows that the use of a combination of vegetation indices and red-edge parameters achieved an improved yield estimation compared to the use of vegetation indices alone. In the future, the selection of suitable sensors and methods needs to be considered when constructing models to estimate crop yield.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Sufficient remote sensing observation data during crop main growing season is of great importance in improving the accuracy of data assimilation of crop model. The optical remote sensing data are susceptible to cloud and rain, so the amount of clear optical data is very limited in cloudy weather or rainy day. Synthetic Aperture Radar (SAR) is not dependent on cloud cover or light conditions, it can penetrate through clouds and have all-weather capabilities. This allows for a more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. So, the aim of this article is to improve the accuracy for winter wheat yield estimation by joint assimilation of SAR and optical satellite images into crop model. In this study, SAR images are acquired by C-band SAR sensor boarded on Sentinel-1 satellites, and optical images are obtained from Sentinel-2 satellites. Remote sensing data and ground data are all collected during the main growth and development stages of winter wheat. Both normalized difference vegetation index (NDVI) derived from Sentinel-2 images and backscattering coefficients and polarimetric indicators computed from Sentinel-1 images are used in water cloud model to derive soil moisture (SM) time series images. To improve the prediction of crop yields at filed scale, we incorporate remotely sensed soil moisture into the WOrld FOod STudies (WOFOST) model using Ensemble Kalman filter (EnKF) algorithm. In general, the results show that data assimilation schemes of remotely sensed soil moisture slightly improved the correlation of observed and simulated yields (R2 = 0.30; RMSE =782 kg ha−1) compared to the situation without data assimilation (R2 = 0.14; RMSE = 1398 kg ha−1). Results of this study indicate that the potential for assimilation SAR and optical remote sensing data to improve field yield estimates is relatively low, limitations are due to insufficient no-cloud optical remotely sensed data and root zone soil moisture information. Consequently, the results of this study demonstrate the potential and usefulness of assimilating both SAR and optical remote sensing data into crop model for crop monitoring and yield estimation. Moreover, this also provides the reference for crop yield estimation with data assimilation in other agricultural landscapes.

Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data.

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

Systematic literature review of machine learning based software development effort estimation models

Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ETo estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ETo at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ETo estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ETo estimation. Among the applied ML models, the ETo tree boost (TB) ML model outperformed the other ML models in estimating ETo in diverse climatic conditions based on statistical indicators (R2, NSE, r, RMSE, and MAE). Moreover, the observed R2, NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ETo point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ETo maps. Based on the ETo maps, this study suggests mainly a focus on areas with high ETo values and proper irrigation scheduling of crops to ensure water sustainability.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Accurate precipitation forecasting is a vital task for many domains, such as agriculture, water management, flood prevention, and crop yield estimation. The use of machine learning (ML) approaches has improved precipitation forecasting accuracy, exhibiting promising results in capturing the intricate connections between various meteorological variables and precipitation patterns. However, given the vast array of available ML models, a comparative analysis is imperative for identifying the most effective models for precipitation prediction. This study aims to examine the capacities of ML algorithms to forecast precipitation based on weather data for the city of Casablanca, Morocco, which faces challenges in water management and climate change adaptation. Eight different ML models’ performances are compared: linear regression, polynomial regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), XGBoost, and an ensemble learning model. These models are evaluated based on their mean absolute error (MAE), mean squared error (MSE), and R-squared (R2 ) value to determine their effectiveness. The study showcases the potential of ML models in predicting precipitation by utilizing meteorological parameters such as temperature, humidity, wind speed, and pressure.

Enhancing estimation of water quality index using stacking machine learning techniques: The case of Southern Bug River.

Crop division is a prerequisite to crop yield estimation by remote sensing. Proper division will be very helpful to attain an accurate estimation. In this study, division indicators were selected and GIS techniques and clustering analysis were used to carry out a crop division for yield estimation by remote sensing. As a result, two two-level divisions, crop structure division and production type division were gained. These crop divisions which considered both natural conditions and agriculture techniques, were used to provide sampling regions and yield estimating units as guidance to the distribution of sampling points, lines and regions for crop yield estimation by remote sensing. Based on these crop divisions, accurate crop yield estimation can be obtained by remote sensing.