Sensor Based Nutrient Recommender System for Solanum lycopersicum

The goal of this study is to spread information about how to use sensor data to establish a precision-based recommender system for nutrition management. Agriculture encompasses crop and livestock production, aquaculture, fisheries and forestry for food and non-food products. Agriculture was the key development in the rise of sedentary human civilization, whereby farming of domesticated species created food surpluses that enabled people to live in cities. The restoration of the nutrient is essential, in this view there is need to adopt precision agriculture system which change crop related plans and regulations, whereas nutrient management is a major domain that is needed to be spotlight in the field of farming techniques. The experimental study of this research work is conducted using a terrace garden. The nutrient management with respect to the horticulture crop tomato is considered as the main crop. The samples are divided into two groups, A and B, which represent samples with and without the use of natural fertilizers and manures, respectively. The results of the analysis of the growth parameters are shown. This paper describes the data collecting process utilizing sensors and an Arduino kit. During the period of growth, the effects of pests and any remedies used are documented. For the purpose of creating suggestions for nutrient management systems, the expert advice provided in the soil test results is taken into account.