Deep Neural Network System Using Ontology to Recommend Organic Fertilizers for a Sustainable Agriculture

Abstract

The highest challenge humankind is facing in the current time period is the enormous population growth and the need to meet the food and nutrient to the population. To meet the enormous production, the farmers are more relayed on the usage of chemicals to increase the food production during the cultivation process. Inclination towards chemical fertilizers is because of their popularity and availability, the over usage of these chemicals is a root cause of many major problems like nutrient-less crops, soil quality degradation, and environmental hazards in the long run. The availability of a knowledge base of the soil quality parameters and their related organic fertilizers according to the farmer's region can decrease the inclination towards the utilization of chemical fertilizers and adopt the usage of organic fertilizers. To help in this process of a major change in farming we built an ontology oriented deep learning model which recommends the farmers in choosing the best organic fertilizers based on the soil quality. The domain ontology construction for agriculture is based on semantic language which can be reused in the future. The knowledge base is then utilized by the deep learning model to process the data and recommend the best suitable fertilizers.