Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Yield Estimation Model of Citrus Based on Spectral Data and Agronomic Parameters

With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.

Rice Yield Estimation by Integrating Remote Sensing with Rice Growth Simulation Model

Since remote sensing can provide information on the actual status of an agricultural crop, the integration between remote sensing data and crop growth simulation models has become an important trend for yield estimation and prediction.The main objective of this research was to combine a rice growth simulation model with remote sensing data to estimate rice grain yield for different growing seasons leading to an assessment of rice yield at regional levels. Integration between NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data and the rice growth simulation model ORYZA1 to develop a new software, which was named as Rice-SRS Model, resulted in accurate estimates for rice yield in Shaoxing, China, with an estimation error reduced to 1.03% and 0.79% over-estimation and 0.79% under-estimation for early, single and late season rice, respectively. Selecting suitable dates for remote sensing images was an important factor which could influence estimation accuracy. Thus, given the different growing periods for each rice season, four images were needed for early and late rice, while five images were preferable for single season rice.Estimating rice yield using two or three images was possible, however, if images were obtained during the panicle initiation and heading stages.

Regionalization for Rice Yield Estimation by Remote Sensing in Zhejiang Province

In order to provide a scientific basis for rice yield estimation and improve the accuracy of yield estimation in Zhejiang Province, regionalization indices for rice yield estimation by remote sensing (RS) in the province were determined by considering the special features of yield estimation by RS, and based on analysis of the natural conditions of Zhejiang Province. The indices determined included rice cropping system, agroclimate, landform, surface feature structure and rice yield level, where rice planting system was considered as the main one. Then regionalization for rice yield estimation by RS was completed by spatial neighboring analysis with the Geographical information System (GIS) technology combined with using of tree algorithm. The province was divided into two regions, i. e., the single-cropping rice region which was subdivided into 3 regions including those in mountains of northwest Zhejiang, water network area of north Zhejiang and mountains of south Zhejiang, and double-cropping rice region which was subdivided into 5 regions including those on plain of north Zhejiang, coastal plains and hills of southeast Zhejiang, Jin-Qu Basin of middle Zhejiang, hills of east Zhejiang, and hills and mountains of northwest Zhejiang. This regionalization took the county borders as the region boundaries, kept the regions connective and made the administrative regions integrity and, then, could meet the requirements of rice yield estimation by RS, showing that the results were quite satisfying.

Yield estimation of metallic layers in integrated circuits

In the existing models of estimating the yield and critical area, the defect outline is usually assumed to be circular, but the observed real defect outlines are irregular in shape. In this paper, estimation of the yield and critical area is made using the Monte Carlo technique and the relationship between the errors of yield estimated by circular defect and the rectangle degree of the defect is analysed. The rectangular model of a real defect is presented, and the yield model is provided correspondingly. The models take into account an outline similar to that of an original defect, the characteristics of two-dimensional distribution of defects, the feature of a layout routing, and the character of yield estimation. In order to make the models practicable, the critical area computations related to rectangular defect and regular （vertical or horizontal） routing are discussed. The critical areas associated with rectangular defect and non- regular routing are developed also, based on the mathematical morphology. The experimental results show that the new yield model may predict the yield caused by real defects more accurately than the circular model. It is significant that the yield is accurately estimated using the proposed model for IC metals.

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

Crop Yield Estimation with Farmers＇ Appraisal on Weather Condition

Crop yield is mainly affected by weather condition, inputs, and agriculture policies. In the crop yield estimation, farmers＇ perception on weather conditions lead to the assessment of how well yield would be compared to the previous seasons. This paper applies Bayesian estimation method to estimate crop yield with farmers＇ appraisal on weather condition. The paper shows that crop yield estimation with farmers＇ appraisal on weather condition takes into account risk proportionally to climate change. In light of the United Nations efforts aimed to build a consolidated agriculture statistical system across countries, the statistical model developed here should provide an important tool both for the crop yield estimation and food price analysis.

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

Three dimensional apple tree organs classification and yield estimation algorithm based on multifeatures fusion and support vector machine

The automatic classification of apple tree organs is of great significance for automatic pruning of apple trees,automatic picking of apple fruits,and estimation of fruit yield.How-ever,there are some problems of dense foliage,partial occlusion and clustering of apple fruits.All of the problems above would contribute to the difficulties of organs classification and yield estimation of the apple trees.In this paper a method based on Color and Shape Multi-features Fusion and Support Vector Machine(SVM)for 3D apple tree organs classifi-cation and yield estimation was proposed.The method was designed for dwarf and densely planted apple trees at the early and late maturity stages.196-dimensional feature vectors composed with Red Green Blue(RGB),Hue Saturation Value(HSV),Curvatures,Fast Point Feature Histogram(FPFH),and Spin Image were extracted firstly.And then the SVM based on linear kernel function was trained,after that the trained SVM was used for apple tree organs classification.Then the position weighted smoothing algorithm was used for clas-sified apple tree organs smoothing.Then the agglomerative hierarchical clustering algo-rithm was used to recognize single apple fruit for yield estimation.On the same training and test set the experimental results showed that the SVM based on linear kernel function outperformed the KNN algorithm and Ensemble algorithm.The Recall,Precision and F1 score of the proposed method for yield estimation were 93.75%,96.15%and 94.93%respec-tively.In summary,to solve the problems of apple tree organs classification and yield esti-mation in natural apple orchard,a novelty method based on multi-features fusion and SVM was proposed and achieve good performance.Moreover,the proposed method could pro-vide technical support for automatic apple picking,automatic pruning of fruit trees,and automatic information acquisition and management in orchards.

An empirical formula for yield estimation from singly truncated performance data of qualified semiconductor devices

The problem of yield estimation merely from performance test data of qualified semiconductor devices is studied. An empirical formula is presented to calculate the yield directly by the sample mean and standard de- viation of singly truncated normal samples based on the theoretical relation between process capability indices and the yield. Firstly, we compare four commonly used normality tests under different conditions, and simulation results show that the Shapiro-Wilk test is the most powerful test in recognizing singly truncated normal samples. Secondly, the maximum likelihood estimation method and the empirical formula are compared by Monte Carlo simulation. The results show that the simple empirical formulas can achieve almost the same accuracy as the max- imum likelihood estimation method but with a much lower amount of calculations when estimating yield from singly truncated normal samples. In addition, the empirical formula can also be used for doubly truncated normal samples when some specific conditions are met. Practical examples of yield estimation from academic and IC test data are given to verify the effectiveness of the proposed method.

A numerical integration-based yield estimation method for integrated circuits

A novel integration-based yield estimation method is developed for yield optimization of integrated circuits.This method tries to integrate the joint probability density function on the acceptability region directly. To achieve this goal,the simulated performance data of unknown distribution should be converted to follow a multivariate normal distribution by using Box-Cox transformation（BCT）.In order to reduce the estimation variances of the model parameters of the density function,orthogonal array-based modified Latin hypercube sampling （OA-MLHS） is presented to generate samples in the disturbance space during simulations.The principle of variance reduction of model parameters estimation through OA-MLHS together with BCT is also discussed.Two yield estimation examples,a fourth-order OTA-C filter and a three-dimensional（3D） quadratic function are used for comparison of our method with Monte Carlo based methods including Latin hypercube sampling and importance sampling under several combinations of sample sizes and yield values.Extensive simulations show that our method is superior to other methods with respect to accuracy and efficiency under all of the given cases.Therefore,our method is more suitable for parametric yield optimization.

A Method for Estimation of Wheat Yield Loss Caused by Drought in Northwestern China

To develop a suitable method for monitoring wheat yield loss caused by drought for dry farming areas in northwestern China, daily ET0 and ETC were calculated using KC and FAO- PM from 1961 to 2000, and wheat evapotranspiration with an interval of 10 days was estimated with soil water balance equation for the mountainous areas in southern Ningxia, China. Actual water consumption and water requirements of wheat during growing season was calculated using soil water balance equation by correcting leakage of soil water and run-off of precipitation every year. A model for estimation of yield loss by drought was established based on crop growth-water consumption function and yield potential. The results show that it is an effective method for monitoring drought and estimating yield loss. This method is suitable for monitoring drought and estimating yield loss of wheat in dry farming areas in northwestern China.

Comparison on Winter Wheat Yield Estimating Models Based on Radarsat-2 and HJ Satellite in Huaihe River Region

The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.

Remote sensing-based estimation of rice yields using various models:A critical review

Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions.However,they offer little infor-mation on spatial variability effects on farm-scale yield.Remote Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional levels.Much research used RS with rice models for reliable yield estimation.As several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize processing.This paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice yield.First,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS data.Second,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield estimation.Lastly,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling approaches.As rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change assessments.Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.

Critical area computation for real defects and arbitrary conductor shapes

In current critical area models, it is generally assumed the defect outlines are circular and the conductors to be rectangle or the merger of rectangles. However, real defects and conductors associated with optimal layout design exhibit a great variety of shapes. Based on mathematical morphology, a new critical area model is presented, which can be used to estimate the critical area of short circuit, open circuit and pinhole. Based on the new model, the efficient validity check algorithms are explored to extract critical areas of short circuit, open circuit and pinhole from layouts. The results of experiment on an approximate layout of 4 × 4 shifts register show that the new model predicts the critical areas accurately. These results suggest that the proposed model and algorithm could provide new approaches for yield prediction.

Seismic characteristics of the 15 February 2013 bolide explosion in Chelyabinsk, Russia

The seismological characteristics of the 15 February 2013 Chelyabinsk bolide explosion are investigated based on seismograms recorded at 50 stations with epicentral distances ranging from 229 to 4324 km. By using 8–25 s vertical-component Rayleigh waveforms,we obtain a surface-wave magnitude of 4.17±0.31 for this event. According to the relationship among the Rayleigh-wave magnitude,burst height and explosive yield, the explosion yield is estimated to be 686 kt. Using a single-force source to fit the observed Rayleigh waveforms, we obtain a single force of 1.03×10^(12) N, which is equivalent to the impact from the shock wave generated by the bolide explosion.

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning

Accurate watermelon yield estimation is crucial to the agricultural value chain,as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning.The conventional method of watermelon yield estimation relies heavily onmanual labor,which is both time-consuming and labor-intensive.To address this,this work proposes an algorithmic pipeline that utilizes unmanned aerial vehicle(UAV)videos for detection and counting of watermelons.This pipeline uses You Only Look Once version 8 s(YOLOv8s)with panorama stitching and overlap partitioning,which facilitates the overall number estimation ofwatermelons in field.The watermelon detection model,based on YOLOv8s and obtained using transfer learning,achieved a detection accuracy of 99.20%,demonstrating its potential for application in yield estimation.The panorama stitching and overlap partitioning based detection and counting method uses panoramic images as input and effectively mitigates the duplications comparedwith the video tracking based detection and countingmethod.The counting accuracy reached over 96.61%,proving a promising application for yield estimation.The high accuracy demonstrates the feasibility of applying this method for overall yield estimation in large watermelon fields.

Ozone concentrations, flux and potential effect on yield during wheat growth in the NorthwestShandong Plain of China

Ozone（O3） concentration and flux（Fo） were measured using the eddy covariance technique over a wheat field in the Northwest-Shandong Plain of China. The O3-induced wheat yield loss was estimated by utilizing O3exposure-response models. The results showed that：（1） During the growing season（7 March to 7 June, 2012）, the minimum（16.1 ppb V） and maximum（53.3 ppb V）mean O3 concentrations occurred at approximately 6：30 and 16：00, respectively. The mean and maximum of all measured O3 concentrations were 31.3 and 128.4 ppb V, respectively. The variation of O3 concentration was mainly affected by solar radiation and temperature.（2） The mean diurnal variation of deposition velocity（V d） can be divided into four phases, and the maximum occurred at noon（12：00）. Averaged V d during daytime（6：00–18：00） and nighttime（18：00–6：00） were 0.42 and 0.14 cm/sec, respectively. The maximum of measured V d was about1.5 cm/sec. The magnitude of V d was influenced by the wheat growing stage, and its variation was significantly correlated with both global radiation and friction velocity.（3） The maximum mean F o appeared at 14：00, and the maximum measured F o was-33.5 nmol/（m^2·sec）. Averaged F o during daytime and nighttime were-6.9 and-1.5 nmol/（m^2·sec）, respectively.（4） Using O3 exposure-response functions obtained from the USA, Europe, and China, the O3-induced wheat yield reduction in the district was estimated as 12.9% on average（5.5%–23.3%）. Large uncertainties were related to the statistical methods and environmental conditions involved in deriving the exposure-response functions.