Study in Soybean Yield Forecast Application Based on Hopfield ANN Model

Using the artificial nerve network′s knowledge, establish the estimate′s mathematics model of the soybean′s yield, and by the model we can increase accuracy of the soybean yield forecast.

Crop Yield Forecasted Model Based on Time Series Techniques

Traditional studies on potential yield mainly referred to attainable yield： the maximum yield which could be reached by a crop in a given environment. The new concept of crop yield under average climate conditions was defined in this paper, which was affected by advancement of science and technology. Based on the new concept of crop yield, the time series techniques relying on past yield data was employed to set up a forecasting model. The model was tested by using average grain yields of Liaoning Province in China from 1949 to 2005. The testing combined dynamic n-choosing and micro tendency rectification, and an average forecasting error was 1.24%. In the trend line of yield change, and then a yield turning point might occur, in which case the inflexion model was used to solve the problem of yield turn point.

Remote Sensing and GIS Based Spectro-Agrometeorological Maize Yield Forecast Model for South Tigray Zone, Ethiopia

Remote-sensing data acquired by satellite imageries have a wide scope in agricultural applications owing to their synoptic and repetitive coverage. This study reports the development of an operational spectro-agrometereological yield model for maize crop derived from time series data of SPOT VEGETATION, actual and potential evapotranspiration and rainfall estimate satellite data for the years 2003-2012. Indices of these input data were utilized to validate their strength in explaining grain yield recorded by the Central Statistical Agency through correlation analyses. Crop masking at crop land area was applied and refined using agro-ecological zones suitable for maize. Rainfall estimates and average Normalized Difference Vegetation Index were found highly correlated to maize yield with the former accounting for 85% variation and the latter 80%, respectively. The developed spectro-agrometeorological yield model was successfully validated against the predicted Zone level yields estimated by Central Statistical Agency (r2 = 0.88, RMSE = 1.405 q·ha-1 and 21% coefficient of variation). Thus, remote sensing and geographical information system based maize yield forecast improved quality and timelines of the data besides distinguishing yield production levels/areas and making intervention very easy for the decision makers thereby proving the clear potential of spectro-agrometeorological factors for maize yield forecasting, particularly for Ethiopia.

Durum wheat yield forecasting using machine learning

A reliable and accurate forecasting model for crop yields is crucial for effective decision-making in every agricultural sector.Machine learning approaches allow for building such predictive models,but the quality of predictions decreases if data is scarce.In this work,we proposed data-augmentation for wheat yield forecasting in the presence of small data sets of two distinct Provinces in Algeria.We first increased the dimension of each data set by adding more features,and then we augmented the size of the data by merging the two data sets.To assess the effectiveness of data-augmentation approaches,we conducted three sets of experiments based on three data sets:the primary data sets,data sets with additional features and the augmented data sets obtained by merging,using five regression models(Support Vector Regression,Random Forest,Extreme Learning Machine,Artificial Neural Network,Deep Neural Network).To evaluate the models,we used cross-validation;the results showed an overall increase in performance with the augmented data.DNN outperformed the other models for the first Province with a Root Mean Square Error(RMSE)of 0.04 q/ha and R_Squared(R^(2))of 0.96,whereas the Random Forest outperformed the other models for the second Province with RMSE of 0.05 q/ha.

Forecasting of Runoff and Sediment Yield Using Artificial Neural Networks

Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.

Forecasting of water yield of deep-buried iron mine in Yanzhou, Shandong

This paper compares analytical and numerical methods by taking the forecasting of water yield of deep-buried iron mine in Yanzhou, Shandong as an example. Regarding the analytical method, the equation of infinite and bilateral water inflow boundary is used to forecast the water yield, and in the case of numerical simulation, we employed the GMS software to establish a model and further to forecast the water yield. On the one hand, through applying the analytical method, the maximum water yield of mine 1 500 m deep below the surface was calculated to be 13 645.17 m3/d; on the other hand, through adopting the numerical method, we obtained the predicted result of 3 816.16 m3/d. Meanwhile, by using the boundary generalization in the above-mentioned two methods, and through a comparative analysis of the actual hydro-geological conditions in this deep-buried mine, which also concerns the advantages and disadvantages of the two methods respectively, this paper draws the conclusion that the analytical method is only applicable in ideal conditions, but numerical method is eligible to be used in complex hydro-geological conditions. Therefore, it is more applicable to employ the numerical method to forecast water yield of deep-buried iron mine in Yanzhou, Shandong.

Analysis and Forecasting of the Impact of Climatic Parameters on the Yield of Rain-Fed Rice Cultivation in the Office Riz Mopti in Mali

During the period spanning the 1970s and1980s, countries in the West African Sahel experienced severe drought. Its impact on agriculture and ecosystems has highlighted the importance of monitoring the Sahelian rainy season. In Sahelian countries such as Mali, rainfall is the major determinant of crop production. Unfortunately, rainfall is highly variable in time and space. Therefore, this study is conducted to analyze and forecast the impact of climatic parameters on the rain-fed rice yield cultivation in the Office Riz Mopti region. The data were collected from satellite imagery, archived meteorology data, yield and rice characteristics. The study employed Hanning filter to highlight interannual fluctuation, a test of Pettitt and the standardized precipitation index (SPI) to analyze the rainfall variability. Climate change scenarios under the RCP 8.5 scenario (HadGEM-2 ES) and agroclimatic (Cropwat) model are carried out to simulate the future climate and its impact on rice yields. The results of satellite image classifications of 1986 and 2016 show an increase of rice fields with a noticeable decrease of bare soil. The analysis of the SPI reveals that over the 30 years considered, 56.67% of the rainy seasons were dry (1986-2006) and 43.33% were wet (2007-2015). The modelling approach is applied over 1986-2006 and 2007-2015 periods—considered as typical dry and rainy years—and applied over the future, with forecasts of climate change scenarios in 2034. The results show a decrease in potential yield during dry and slightly wet years. The yields of rain-fed rice will be generally low between 2016 and 2027. Deficits are observed over the entire study area, in comparison with the potential yield. Thus, this situation could expose the population to food insecurity.

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region

Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel model for predicting sugarcane yield in Bundaberg region from time series Landsat data. From the freely available Landsat archive, 98 cloud free (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31st (2001-2015) were sourced for this study. The images were masked using the field boundary layer vector files of each year and the GNDVI was calculated. An analysis of average green normalized difference vegetation index (GNDVI) values from all sugarcane crops grown within the Bundaberg region over the 15 year period identified the beginning of April as the peak growth stage and, therefore, the optimum time for satellite image based yield forecasting. As the GNDVI is an indicator of crop vigor, the model derived maximum GNDVI was regressed against historical sugarcane yield data, which showed a significant correlation with R2 = 0.69 and RMSE = 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible and a modest technique to predict sugarcane yield in Bundaberg region.

Study on Growth Monitoring and Yield Prediction of Winter Wheat in the South of Shanxi Province Based on MERSI Data and ALMANAC Crop Model

Accurate crop growth monitoring and yield forecasting have important implications for food security and agricultural macro-control. Crop simulation and satellite remote sensing have their own advantages, combining the two can improve the real-time mechanism and accuracy of agricultural monitoring and evaluation. The research is based on the MERSI data carried by China’s new generation Fengyun-3 meteorological satellite, combined with the US ALMANAC crop model, established the NDVI-LAI model and realized the acquisition of LAI data from point to surface. Because of the principle of the relationship between the morphological changes of LAI curve and the growth of crops, an index that can be used to determine the growth of crops is established to realize real-time, dynamic and wide-scale monitoring of winter wheat growth. At the same time, the index was used to select the different key growth stages of winter wheat for yield estimation. The results showed that the relative error of total yield during the filling period was low, nearly 5%. The research results show that the combination of domestic meteorological satellite Fengyun-3 and ALMANAC crop model for crop growth monitoring and yield estimation is feasible, and further expands the application range of domestic satellites.

Forecasting Methods of Agrometeorological Conditions in the Northern Zone of the Republic of Kazakhstan

This work presents the forecast of quality indicators of wheat from weather conditions in the Northern zone of the Republic of Kazakhstan, obtained on the basis of the correlation of protein and gluten content of grain with an average monthly air temperature and precipitation. The equation obtained by the authors allows estimating the quality of grain with the monthly advance, which is important in the organization of harvesting of grain crops.

Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield

The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).

基于天气预报的泾惠渠灌区参考作物滕发量预报模型研究

农业节水背景下,对泾惠渠灌区参考作物滕发量预报方法进行研究,选用Hargreaves-Samani公式作为预报模型,运用中国气象数据网泾河站2008年~2020年13个年份的气象数据进行SPSS参数反演,并采用2022年的实测气象数据进行验证,表明适合泾惠渠灌区的Hargreaves-Samani模型参数为C=0.00122、a=14.19、m=0.259。通过验证,83.2%验证值相对误差在20%以内,同时表明Hargreaves-Samani模型进行参考作物滕发量预报时,呈现“夏季>春季>秋季>冬季”的精度分布规律,可为灌区灌溉预报和智慧水利发展提供实用的理论依据。

基于气候变量的苎麻产量SSA-BP预测模型

苎麻产量与生长期间的气候因子具有极高相关性,基于气候变量构建的苎麻产量预测模型能够有效精准预测最终产量。BP(back propagation)神经网络具有强大的数据分析能力,在作物产量预测建模中得到广泛应用,然而传统BP神经网络存在精度低、鲁棒性差等问题,可采用麻雀搜索算法(sparrow search algorithm,SSA)对BP神经网络模型进行优化。基于2010—2019年苎麻长期定位试验采集的纤维产量、鲜皮产量和气候数据,分析气候因子在10年内的变化趋势及其对多年生苎麻产量的影响,对比构建的BP神经网络模型及优化后的SSA-BP神经网络模型预测苎麻产量的性能,确定最佳的苎麻产量预测模型。结果表明,苎麻产量与季平均气温、季极端最高气温均值、季极端最低气温均值、季日照时数均值4项气候因子具有极显著相关关系。SSA算法能有效优化BP神经网络,基于SSA-BP的苎麻纤维产量预测模型和鲜皮产量预测模型的R^(2)分别为0.5913和0.6791,高于BP神经网络的苎麻纤维产量预测模型(R^(2)=0.4057)和鲜皮产量预测模型(R^(2)=0.5518)。因此,SSA-BP模型能够更加科学、合理地预测苎麻产量,对于苎麻生产的田间管理及统筹规划具有重要指导意义。

作物生长模型研究现状与展望

作物生长模型由最初的作物生长发育模型发展到农业决策支持模型,在科学研究、农业管理、政策制定等方面发挥着越来越重要的作用。本文首先回顾了作物生长模型的发展过程,并按照模型主要驱动因子,将作物生长模型分为土壤因子、光合作用因子和人为因子驱动3类并分别进行了归纳阐述;然后对典型的模型分别从模型模块、时空尺度、可模拟的作物类型等方面进行列表式对比;并对作物生长模型在气候变化评估、生产管理决策支持、资源管理优化等方面的应用,以及面临的极端条件、复杂农业景观和模型复杂度等挑战进行了总结,在此基础上认为遥感数据同化和孪生农场是其发展方向。

基于连续小波变换和机器学习的小麦产量预测

及时、准确的作物估产,对作物管理决策和粮食安全评估具有重要意义。该研究在建立一种耦合连续小波变换(CWT)与机器学习准确预测小麦产量的方法。基于2020年—2021年两年小麦田间试验获取的开花期和灌浆期冠层高光谱数据及产量数据,首先采用CWT提取三种小波特征(WFs),分别为:基于Bortua方法筛选的特征波段(Bortua-WFs)、提取WFs与小麦产量确定系数的前1%(1%R^(2)-WFs)和单一分解尺度下的所有WFs(SS-WFs)。然后采用随机森林(RF)、 K最邻近(KNN)和极端梯度提升(XGBoost)三种机器学习算法构建产量预测模型。最后选取最优的光谱特征,采用相同的方法进行建模并比较。结果表明:(1)三种WFs结合机器学习方法的模型均表现良好,基于Bortua-WFs构建的模型具有更高的精度和稳定性。(2)相比光谱特征模型,Bortua-WFs模型在各生育期的精度均有所提高,开花期的R^(2)精度分别提高了17.5%、 4%和39.6%,灌浆期分别提高了8.4%、 5.6%和16.9%。(3)灌浆期的产量估算模型优于开花期,结合Bortua-WFs和XGBoost的模型表现最佳,R^(2)为0.83, RMSE为0.78 t·ha^(-1)。该研究比较了不同特征和方法相结合的性能,确定了不同方案下的最佳模型精度,为光谱准确预测小麦产量提供技术参考。

Fuzzy Varying Coefficient Bilinear Regression of Yield Series

We construct a fuzzy varying coefficient bilinear regression model to deal with the interval financial data and then adopt the least-squares method based on symmetric fuzzy number space. Firstly, we propose a varying coefficient model on the basis of the fuzzy bilinear regression model. Secondly, we develop the least-squares method according to the complete distance between fuzzy numbers to estimate the coefficients and test the adaptability of the proposed model by means of generalized likelihood ratio test with SSE composite index. Finally, mean square errors and mean absolutely errors are employed to evaluate and compare the fitting of fuzzy auto regression, fuzzy bilinear regression and fuzzy varying coefficient bilinear regression models, and also the forecasting of three models. Empirical analysis turns out that the proposed model has good fitting and forecasting accuracy with regard to other regression models for the capital market.

基于注意力机制的ADE-Bi-IndRNN模型的中国粮食产量预测

为更加准确地预测我国粮食总产量,基于自适应差分进化算法来智能地选择基于注意力机制的双向独立循环神经网络的超参数,并考虑了粮食作物单位产量、农业生产条件、科技因素、农业保险、市场及经济因素五大类影响因素,构建了基于注意力机制的ADE-Bi-IndRNN粮食产量预测模型。经过预测分析得出我国2020—2024的粮食产量分别为6.67亿吨、6.72亿吨、6.80亿吨、6.99亿吨、7.02亿吨,总体呈现震荡上涨趋势,平均年增长率为1.15%。同时,通过对多个变量进行的注意力权重的分析,发现现阶段对我国粮食总产量预测贡献最大的三个变量为:谷物单位面积产量,粮食作物总播种面积,耕地灌溉面积,且政府对农业保险的政策性补贴、粮食进口量、谷物生产价格指数、农业生产资料指数也有助于提升我国的粮食总产量,并据此对我国粮食行业发展提出了建议。

基于GBDT算法的吉林省玉米产量预测模型研究

玉米是我国种植面积最广、产量最高、食用最多的3种主要农作物之一,掌握科学预测玉米产量的技术,可以为农业的种植规划、粮食储存加工、市场调控提供技术支持。该文兼顾气象因素和土壤因素,建立BP神经网络模型、RBF径向基神经网络模型、GBDT梯度提升决策树模型,对吉林省各县市玉米产量进行回归分析,对比分析其误差。实验结果中,GBDT模型预测的产量和真实产量间的拟合程度较高,R2达到0.92,可以在吉林省各县市玉米产量预测中表现出较好的效果。结果表明该模型对吉林省40个县市玉米产量进行预测的可行性,数据易于获取,能够帮助政府农业部门制定相关政策和方针指导生产。

河北省马铃薯不同产量预报方法对比分析

选用1982—2022年河北省17个基本气象观测站逐日资料、马铃薯产量数据以及生育期资料,应用3 a滑动平均法、5 a滑动平均法、五点二次平滑法、Hodrick-Prescott滤波法和二次指数平滑法对马铃薯产量进行分离计算,应用关键气象因子法和气候适宜度法对马铃薯产量进行模拟和检验,分析拟合产量与实际产量的相关系数、均方根误差以及预报准确率等。结果表明:5种产量分离方法的趋势产量总体变化较为一致;气象产量年际波动较大,各气象产量间差异也较大。在基于关键气象因子法和气候适宜度法的产量预报中,均为二次指数平滑产量分离法最好,HP滤波法次之,3 a滑动平均法较差。