DESIGN OF A SMART AGRICULTURE SYSTEM FOR PEST MANAGEMENT BASED ON TRANSFER LEARNING AND IOT TECHNIQUES

Subject Area: Computer Science

Pests are organism that causes damage to crops in a farm land. For many years, this organism has continued to evolve and some have even become resistant to the traditional pest control measures which are more of a reactive approach. Several existing literatures which have also provides recommendation to better manage pest, however there is gap in the need of models which considers region specific pest to ensure a reliable system for pest management. Hence, the aim of this study is design and implementation of pest management system for precision agriculture using integrated transfer learning and internet of things technique. The methodology to be used for this work is the dynamic system development model. The tested for primary data collection is Aninri and the pests considered are weevil, caterpillar, and whitefly. The secondary data source is Kaggle. Then total sample size of data collected is 18138. A notification algorithm was developed with simple mail transfer protocol, while rule-based approach was applied to develop pest control model, using data collected from pest related domain experts. The models were integrated as a system for pest management in smart agriculture. Experimental validation was carried out considering insects collected from different farms, the results recorded successful pest classification, and notification of control recommendations. In conclusion, this work has successfully presented a reliable solution of the real time management of pest.