Prediction of Crop Yielding rate using machinelearning algorithms

The world population rate is increasing. Production of food grains is alarming with rapid change of environmental conditions. Soil parameters are also much influencing on the crop yielding rate. Precision agriculture has much significance in meeting the demands. This paper aimed to predict the yielding rate by considering the crop decease, soil parameters are Nitrogen, potassium, phosphorous and environmental conditions are ambient temperature, Ph value and humidity. The decease of the crop is analyzed using machine learning algorithms. ENET regression algorithm, LASSO Regression algorithm, Kernel Edge Algorithm and staking algorithm have been considered. The trained data sets are applied to various machine learning algorithms and estimated the infected level of the leaf and eventually the yielding rate of the crop. The results achieved with ENET regression algorithm, LASSO Regression algorithm, Kernel Redge Algorithm and staking algorithm are compared to interpret the bet fit algorithm for agricultural applications. Mean square error value is considered for comparison. ENETandLASSO