Crop Health Monitoring Using Iot Sensors Based Regression Model

Farmers of today face the problem of decreased crop yield. One of the main things a farmer can do to increase crop yield is to select the correct crop. Not performing analyses like metrological factors checking and soil analysis and having less knowledge about soil fertility leads to less crop yield. Knowing the above details will boost crop production. Farmers face considerable challenges in selecting the right crop since the climate does not follow a pattern, and the farmers need to gain basic knowledge regarding crop selection and modern farming methods. When farmers select the same crop every season, the soil will lose fertility, leading to low crop yield. Machine learning (ML) algorithms and IoT devices are used in the proposed study to make a system that can perform accurate, practical and valuable decision-making. The farmer can achieve maximal yield with the help of the system. This system will suggest to the farmer how to select the right crop. Compared to the laboratory testing performed in the olden days, the proposed regression model is reliable, where manual errors can be avoided. In the area of agriculture, the main priority is the selection of the correct crop. As a contribution to agriculture, Smart Crop Selection model based on machine learning and IoT was developed. Metrological information and soil factors are the data used for our system. Potassium, CO2, EC, temperature, soil’s humidity, rainfall, nitrogen, phosphorus and pH value are the factors used by the system for crop selection. Only some of these factors are employed by the existing system, making it inefficient compared to our proposed model. Our proposed system sends real-time sensory data to analyze the various factors.