基于多层感知机的温室内番茄茎直径变化预测模型

【目的】研究温室内番茄茎直径变化量动态预测模型,为番茄需水规律提供一定决策支持。【方法】采用多层感知机算法与植物生理生态信息相融合的方法,建立一个包含空气温湿度、土壤湿度、叶片温度、茎直径变化量及光合有效辐射的基于多层感知机算法的茎直径变化量预测模型。采用3层隐含层神经网络对经过正则化及归一化的6维训练集数据向量,进行全连接式训练后,得出预测模型,验证集数据输入预测模型后得出1维输出向量,对输出向量进行反归一化进而得出茎直径变化量预测值。【结果】建立的基于多层感知机短时番茄茎直径变化量动态预测模型的预测值与实测值的回归系数(R²)为0.901,均方根误差(RMSE)为0.175。【结论】该模型适用于温室番茄短时茎直径变化量动态预测,具有较好的应用场景。

The Research of the Prediction Model of Stem Diameter Variation Based on Multilayer Perceptron for Greenhouse Tomato

【Objective】 Aiming at the uncertain parameters of tomato water demand law and the non-dynamic analysis of system, the dynamic prediction model of tomato stem diameter change in greenhouse was studied in the hope providing some decision support for tomato water demand. 【Method】This research mainly uses the method of integrating the multi-layer perceptron algorithm with plant physiological and ecological information, established a stem diameter variations prediction model, which contains many types of data ,like air temperature, humidity, soil humidity, leaf temperature, stem diameter variations and photosynthetic effective radiation. In this paper, the three-layer hidden layer neural network is adopted to conduct fully connected training on the data vector of the 6-dimensional training set after regularization and normalization, and then the 1-dimensional output vector is obtained after input the validation set data . Finally, the output vector is inverse normalized to obtain the predicted value of stem diameter variations.【Result】The regression coefficient of the predicated value and the measured value (R²) and the root mean square error (RMSE) were 0.901 and 0.175, respectively, based on the dynamic prediction model of the short-term tomato stem diameter variations on which multilayer perceptron was established. 【Conclusion】The model is applicable to the dynamic prediction of short-term stem diameter variations of greenhouse tomato, and has a good application scenario, which can provide a strong basis for the decision to meet the crop water demand.