基于BP神经网络的番茄干重预测研究

Prediction study of tomato dry weight based on BP neural network

以番茄干重作为正交试验指标,研究温室内番茄生长的环境参数(温度、相对湿度、光照强度)对番茄干重的影响,建立BP神经网络模型,运用MATLAB对试验数据进行训练和模拟,为检验预测的可靠性,采用10-折交叉验证,准确率为95.32%。结果表明,利用BP神经网络得出预测值与实测值接近,具有较好的预测性,可用于干重的预测,能够为温室环境调控提供科学依据。

Orthogonal experimental was carried out using the tomato dry weight as experimental objective, and the impact of environmental parameters （temperature, relative humidity, light intensity） on tomato dry weight was ana- lyzed through experiment. A neural network calculation model was established based on experimental data and the test were made by using MATLAB software, and to better verify effectiveness of the approach, a lO-fold cross valida- tion method was used. Through 10-fold cross validation, model achieved the predictive accuracy of 95.32%. The re- sults showed that the predicted values were in good agreement with the experimental values. This method has high prediction precision, which can be used theory instruction in environment control, and the predicted dry weight of to- mato with the BP neural network method was feasible