论文标题
CYPUR-NN：使用回归和神经网络的作物产量预测
CYPUR-NN: Crop Yield Prediction Using Regression and Neural Networks
论文作者
论文摘要
我们最近使用稻田和相关条件的历史数据的研究包括湿度，发光和温度。通过结合回归模型和神经网络（NN），可以产生对稻田产量的高度令人满意的预测。模拟表明，我们的模型可以高精度预测稻田，同时检测可能存在并忽略人眼的疾病。使用回归和神经网络（CYPUR-NN）的作物产量预测是一种系统，该系统将促进农业学家和农民从图片中预测产量或通过Web界面进入值。 CYPUR-NN已在库存图像上进行了测试，实验结果很有希望。
Our recent study using historic data of paddy yield and associated conditions include humidity, luminescence, and temperature. By incorporating regression models and neural networks (NN), one can produce highly satisfactory forecasting of paddy yield. Simulations indicate that our model can predict paddy yield with high accuracy while concurrently detecting diseases that may exist and are oblivious to the human eye. Crop Yield Prediction Using Regression and Neural Networks (CYPUR-NN) is developed here as a system that will facilitate agriculturists and farmers to predict yield from a picture or by entering values via a web interface. CYPUR-NN has been tested on stock images and the experimental results are promising.
