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.
翻译:我们最近利用水稻产量和相关条件的历史数据进行的研究包括湿度、光度和温度。通过采用回归模型和神经网络(NN),人们可以对水稻产量作出非常令人满意的预测。模拟表明,我们的模型可以高精确地预测水稻产量,同时检测出可能存在和人类眼所忽视的疾病。 利用倒退和神经网络(CYPUR-NN)作物的Yield预测在此发展成为一个系统,它将促进农业学家和农民从图片中预测产量,或通过网络界面输入价值。 CYPUR-NN已经根据鱼群图象进行了测试,实验结果很有希望。