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首页> 外文期刊>International Journal of Vegetable Science >Predicting Eggplant Individual Fruit Weight Using an Artificial Neural Network
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Predicting Eggplant Individual Fruit Weight Using an Artificial Neural Network

机译：使用人工神经网络预测茄子个体果实重量

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摘要

Estimation of relationships between inconstant factors can be helpful to calculate amounts of variation of a particular character with respect to others. In Eggplant (Solanum melongena L) this information could be used to improve fruit yield. Effectsof agronomic and phenologic factors were studied by applying an artificial neural network (ANN) as a displaying instrument to determine how plant length; individual fruit weight, length, and width; number of fruit per plant; ratio of fruit length to fruit width; total yield; number of days to flowering; number of days to first harvest; canopy temperature; chlorophyll; and relative water content affected individual fruit weight of eggplant. There was a high accuracy obtained for the 7-4-1 ANN model basedon these parameters (R2 = 93%; mean prediction error [MPE] = 2.01; mean square deviation [MSD] = 2.35). A sensitivity analysis was performed and the ratio of fruit length to fruit width, number of days to first harvest, and number of days to flowering had the greatest impact on individual fruit weight. The highest standard deviation was for total yield and individual fruit weight, respectively (308.8 and 67.5), and correlation coefficients were high between fruit weight and number of days to flowering(0.99**) and individual fruit weight and total yield (0.88**). Sensitivity analysis indicated that the ratio of fruit length to fruit width and fruit length have high and lesser effects on final individual fruit weight. Total yield is the main factor forproducing change in individual fruit weight.
机译：估计不动因子之间的关系可以有助于计算关于他人的特定角色的变化量。在茄子（Solanum Melongena L）此信息可用于提高果产量。通过将人工神经网络（ANN）作为显示仪器来研究农艺和现象因素的影响，以确定植物长度的方式;个体果实重量，长度和宽度;每株植物的水果数量;果实长度与果实宽度的比率;总产;开花的天数;第一次收获的天数;冠层温度;叶绿素;相对水含量影响茄子的个体果实重量。基于7-4-1 ANN模型获得的高精度（R2 = 93％;平均预测误差[MPE] = 2.01;均方偏差[MSD] = 2.35）。进行敏感性分析，果实长度与果实宽度的比率，第一次收获的天数，以及开花的天数对个体果实重量产生了最大的影响。最高标准偏差是总产量和单个果子重量（308.8和67.5），果子重量和开花的天数之间的相关系数高（0.99 **）和个体果实重量和总收率（0.88 ** ）。敏感性分析表明，果实长度与果实宽度和果实长度的比率对最终单个果子重量具有高且较小的效果。总产量是生产各种果实重量变化的主要因素。

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