Analyze Soil Fertility using Deep Learning Convolutional Neural Networks

Keywords: Convolutional Nueral Networks, Soil Fertility, Deep Learning, Computational Power


This research revolves around how plant soil potential can be further discovered and used for farming through detection of relevant nutrients and chemicals within the soil landscapes within areas and even desert climates and how we can improve land soil fertility of the purpose of farming both using Convolutional neural networks which process of imagery in layers and predictive detections of objects within image backgrounds and frontal lobes.
When we view layers for farming beneath the surface to understand suitability of farming done on top. The general model applied can be summarized as follows:
As shown In Appendix 1a, we can see the various layers soil has to assess the possibility of nutrient provision for farming [2].
The Objective is to examine availability of plant nutrients using convolution of Nueral networks to classify open farmlands through image analysis and layering.
Convolution Nueral networks is divided into four steps starting with input of images, drafting a convolution layer, creating a pooling layer and flattening the Nueral network. It can be performed as a machine learning Algorithmic procedure with Python as well as R programming.
CNN divides the images into pixels, edges, frontal lobes and shading through the support of power machine learning libraries and packages like Tensorflow and Keras.