Machine Learning for Soil Fertility and Plant Nutrient Management using Back Propagation Neural Networks

The objective of this paper is to analysis of main soil properties such as organic matter, essential plant nutrients , micronutrient that affects the growth of crop s and find out the suitable relationship percentage among those properties usi ng Supervised Learning , Back Propagation Neural N etwork. Although these parameters can be measured directly, their measurement is difficult and expensive. Back Propagation N etworks (BPN) are trained with re ference crops growth properties available nutrient status and its ability to provide nutrients out of its own reserves and through external applications for crop production in both cases , BPN will find and suggest the correct correlation percentage among those properties. This machine learning system is divided into three steps, first s ampling (Different soil with same number of properties with different p arameters) second Back Propa gation Algorithm and third Weight updating . The performance of the Back Propagation N eural network model will be evaluated using a test data set. Results will show that ar tificial neural network with certain number of neurons in hidden layer had better pe rformance in predicting soil properties than multivariate regression. In conclusion, the result of this study showed that training is very important in increasing the model accuracy of one region and result in the form of a guide to recognizing soil proper ties relevant to plant growth and protection