Self-Learning IOT based Integrated Management for Improving the Quality of Yield in Pomegranate for Indian conditions

Abstract

India is the largest producer of pomegranate in world. It produces five Lakh tones of total global production of ten Lakh tones, yet the export is only 5000 tones. The reason for shortfall in exports is its inadequacy to meet the global export standards. Various challenges like fertilizer & pest management, irrigation management etc.  exist in production of high-quality fruits. Due to large scale geographical distribution of product with larger variations in climate, soil conditions it is very difficult to manage production using a general rule. The production management must be adapted to each specific field condition with a goal to achieve high quality of yield. This work proposes a self-learning data mining technique to learn the optimum conditions for higher quality yield and enforce the learnt rules using IOT based controller. The learning is done from a multidimensional view of irrigation, pest and fertilizer management to achieve optimum fruit quality.