Modern aspects of informatization of agricultural production based on modeling and forecasting the production process of lentils under different conditions of moisture supply

S. Lavrenko, N. Lavrenko, M. Maksymov
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Abstract

The article presents the results of the application of modern systems for modeling and forecasting the production process of lentils in the Southern Steppe of Ukraine. The correlation-regression analysis shows the high reliability and practical value of the obtained mathematical models of growing lentils for grain depending on the conventional tillage, fertilizer rate, and plant density under different moisture conditions; that is confirmed by the curves based on the experimental data and calculations. Mathematical models of lentils grain yield under different moisture conditions were compiled according to the obtained regression coefficients and free members: without irrigation - Y=1,5896+0,0032×Х1+0,0007×Х2-0,2561×Х3, and when applying irrigation Y=1,0200+0,0051×Х1+0,0022×Х2+0,2656×Х3. The following results were obtained for the dependent variable for different conditions of moisture supply after conducting a regression-normalized analysis of the researched factors in view of yield of lentils, where: in variant without irrigation R = 0.7059; R2 = 0.4983; adjusted R2 = 0.4682; F (3,50) = 16,551 p <0,00000 and standard estimation error was 0,1232; in variant with irrigation R = 0,6131; R2 = 0.3759; adjusted R2 = 0.3385; F (3.50) = 10.04 p <0.00003 and the standard estimation error was 0,2591. Nonlinear multilayer artificial neuron models have been developed for the first time to predict lentils grain yields. Generalized regression artificial neural network GRNN (4-12-7-1) with 12 neurons in the first hidden layer and seven ones in the second hidden layer; learning productivity was 0.215; control productivity was 0.290; test productivity was 0.362; learning error was 0.136; control error was 0.049; test error was 0.066. Taking into account nonlinear patterns of factor effect on lentils grain yield the multiple correlation was 0.96. Based on the results of ranking the researched factors' effect on the dynamics of formation and yield of lentils, it was found that moisture conditions (water consumption, m3/ha) with an impact factor of 4.21 which exceeds other researched factors by almost 2.2 times, are in the first place. Plant density (million/ha) was in second place with a factor of 1.62. The rate of mineral fertilizers (kg/ha of active substance) was in third place, which was slightly inferior to the density of standing plants, resulting in a total of 1.61. The depth of tillage (cm) was in the last fourth place with an impact factor of 1.01.
基于现代农业生产信息化的小扁豆在不同水分供给条件下的生产过程建模与预测
本文介绍了应用现代系统对乌克兰南部草原扁豆生产过程进行建模和预测的结果。相关回归分析表明,在不同水分条件下,根据常规耕作方式、施肥量和种植密度建立的粮食用扁豆生长数学模型具有较高的可靠性和实用价值;基于实验数据和计算的曲线证实了这一点。根据得到的回归系数和自由系数,编制不同水分条件下扁豆产量的数学模型:不灌溉时Y=1,5896+0,0032×Х1+0,0007×Х2-0,2561×Х3,灌溉时Y=1,0200+0,0051×Х1+0,0022×Х2+0,2656×Х3。针对小扁豆产量,对研究因子进行回归归一化分析,得到不同供水量条件下的因变量为:无灌溉变量R = 0.7059;R2 = 0.4983;调整后R2 = 0.4682;F (3,50) = 16,551 p <0,00000,标准估计误差为0,1232;灌水变异R = 0,6131;R2 = 0.3759;调整后R2 = 0.3385;F (3.50) = 10.04 p <0.00003,标准估计误差为0,2591。本文首次建立了非线性多层人工神经元模型来预测扁豆籽粒产量。广义回归人工神经网络GRNN(4-12-7-1),第一隐层有12个神经元,第二隐层有7个神经元;学习效率为0.215;对照生产率为0.290;测试生产率为0.362;学习误差为0.136;控制误差为0.049;测试误差为0.066。考虑因子影响扁豆籽粒产量的非线性模式,多重相关系数为0.96。结果表明,水分条件(耗水量,m3/ha)对扁豆形成和产量的影响因子排名第一,影响因子为4.21,是其他研究因子的近2.2倍。植物密度(百万/公顷)次之,因子为1.62。矿质肥料的利用率(kg/ hm2)位居第三,略低于立木密度,为1.61。耕深(cm)排在最后4位,影响因子为1.01。
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