Design of a Comprehensive Sensor based Soil and Crop Analysis System Model using Machine Learning Algorithms

In this contemporary world, the evolution of technology is the best thing since sliced bread. There is an incredible growth of technology within the field of agriculture, but there are things that the world needs to modernize. In order to achieve excellent growth in smart farming, an additional feature to the soil monitoring system that generates various sorts of statistics on moisture content, temperature, soil type, soil diseases that might occur, suggesting seeds for crop rotations, etc. have been added this may drastically reduce the difficulties in crop production which indirectly increases productivity. This plan would also increase the interest of many people within the field of agriculture as most of the farmers left this occupation due to insufficient productivity. On the whole, this increases the productivity, economy, and also indirectly job opportunities for many. Here, sensors are used to capture the field image and transfers the captured image to SolDier system which is a system built using machine learning models such as regression, clustering, classification. SoilDet helps us to collect information about the soil hence plants associated with it which in turn helps the farmer to achieve more insight into the soil, resulting in a bloom in the field of smart farming using IoT.