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.

Application of neural network model coupling with the partial least-squares method for forecasting watre yield of mine

Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.

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。

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.

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。

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.

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。

石河子棉花生长关键期与产量动态预报研究

棉花是新疆地区主要经济作物之一,开展棉花产量动态预报对生产安全具有重要意义。利用1968—2020年新疆石河子地区4个地面气象基准站逐日气象资料和棉花产量资料,基于积分回归法在棉花全生育期内以旬为时间尺度,分析了影响棉花生产的温光水主要气象要素和关键期,分别建立了石河子地区7月中旬、8月中旬和9月中旬棉花气象产量动态预报模型。结果表明:气温对新疆石河子棉花产量的影响最大,苗期、开花期和吐絮始期为棉花生长的温度关键期,苗期和吐絮始期为正效应显著,开花期为负效应显著;开花期是棉花生长的光照关键期,对棉花产量呈正效应;石河子地区属于灌溉农业区,自然降水量虽呈正效应,但降水量对棉花产量的影响较小。利用积分回归法建立的动态预报模型对2018—2020年石河子地区棉花产量试报,7月中旬、8月中旬及9月中旬的平均准确率分别为85.1%、91.4%和94.3%。基于积分回归法建立的棉花气象产量动态预报模型越接近成熟期准确率越高。利用积分回归原理对棉花产量进行动态预测是可行的,可以应用于棉花产量预测业务,为地方产量预测提供参考。

基于灰色系统理论的玉米产量影响因素分析及预测——以湖南省为例

【目的】了解玉米产量和各种影响因素之间更加深层的关系,精确地预测玉米产量。【方法】使用MATLAB软件,选用湖南省2000—2019年的玉米产量及其相关因素的数据,对玉米产量的影响指标进行灰色关联分析,将其按照灰色关联度大小进行排序。同时使用GM(1,1)灰色预测模型,对湖南省2020—2024年的玉米产量进行预测。【结果】对2000—2019年湖南省玉米产量影响最大的指标为玉米播种面积、农用化肥施用折纯量,其次是农业机械化动力、农用塑料薄膜使用量,这两个指标影响也较大。GM(1,1)模型预测结果表明,2020—2024年湖南省玉米产量逐渐增长。

基于GA-XGBoost算法的河南省粮食产量预测研究

粮食问题关乎国家命运,是国民经济发展基础中的基础。粮食产量的变化直接关系到我国的粮食安全和农业结构的优化调整。为提高河南省粮食产量预测的精度和效率,对河南省粮食产量等相关数据进行归纳分析,利用皮尔逊(Pearson)相关性影响分析确定主要影响河南省粮食产量的因素。针对XGBoost模型容易过拟合、预测不精准的问题,引入遗传算法(GA)对其学习率、树的深度等进行优化,以更准确地预测河南省粮食产量。仿真结果表明:相比于传统的XGBoost模型,GA-XGBoost模型具有更高的预测精度,RMSE仅为0.034。因此,GA-XGBoost预测模型可以对粮食产量进行更为准确的预测。

基于VAR-MGM-BP组合模型的中国粮食产量短期预测

为提高粮食产量的预测精度,合理预测其发展趋势,保障粮食安全,选取2000-2021年中国粮食产量、农业总产值和农业就业人员数据,构建了ARIMA,GM,VAR,MGM,ARIMA-GM-BP,VAR-GM-BP,ARIMA-MGM-BP和VAR-MGM-BP等一系列模型,并以MAPE为评价指标对模型进行拟合和预测精度比较.结果表明:多变量预测模型在预测精度方面优于单变量预测模型,而多变量组合模型又优于单变量组合模型;进一步表明,提升组合模型中单一模型的预测精度有助于提升组合模型的预测精度.最后,研究构建的VAR-MGM-BP组合模型拥有最小的MAPE值,并利用VAR-MGM-BP模型对中国未来五年的粮食产量进行预测.

基于遗传算法优化BP神经网络下马铃薯产量预测模型

马铃薯产量的高效预测对于制定马铃薯生长期间的精准管理决策具有重要意义。为此,针对传统BP神经网络在产量预测中存在的精度差、准确度低等问题,选择遗传算法对单一BP神经网络模型开展网格优化。基于朔州市朔城区沙楞河村2010-2019年田间物联网获取的田间环境数据(土壤含水率和土壤温度)、气象环境数据(大气湿度、大气温度、降雨量)和马铃薯产量,采用BP神经网络及GA-BP神经网络模型对所选地区马铃薯产量进行预测分析。研究结果表明:GA-BP神经网络模型下,马铃薯产量的预测精度明显高于BP神经网络模型,R 2达到0.99327,平均相对误差仅为0.88%。试验证明,GA-BP神经网络模型能够更加科学、合理地进行马铃薯产量预测,说明利用遗传算法优化BP神经网络在马铃薯产量预测中是可行且有效的。