Agro-Mate: A Virtual Assister to Maximize Crop Yield in Agriculture Sector

This paper presents a decision support system that supports farmers to take accurate decisions and help them with soil quality determination, best crop selection, rice disease prediction, and disaster prediction for the wet zone of Sri Lanka. This project has incorporated technologies such as Deep Learning, Image Processing, the Internet of Things, and Machine Learning that can aid farmers or investors to maximize yield. ‘Agro-Mate’ consists of four components which are soil quality determination, best crop selection, rice disease prediction, and natural disaster prediction. Also, the application suggests fertilizer when soil is lacking quality and provides recommendations whenever rice diseases or natural disasters are identified. An android mobile application is developed which users will utilize to access the system and make use of it. The proposed system facilitates the farmer in accurate decision-making to gain more quality and quantity of crops. ‘Agro-mate’ is more likely to increase the productivity of crops. In the future, this paper will be included with the test and evaluations results to prove the proposed decision-making concept.