Integrated IoT System for Prediction of Diseases in the Vineyards - Jovanovska, Elena; CHorbev, Ivan; Davcev, Danco; Mitreski, Kosta (IEEE, 2022)
Smart Agriculture is becoming more accepted by food industries, since they can see the difference in terms of quality of product, greater harvest, and lower cost expenses. Vine production has a great worldly role, so manufacturers are keen to experiment with new technologies that could improve their product. From a scientific perspective, we propose an integrated system for prediction of diseases in vineyards that provides services for data collection (from vineyard plantations), preprocessing, and integrated prediction of vineyard diseases using several models for prediction. Previous experiences showed that it is possible to develop a complete integrated system for prediction of diseases in viticulture and hence to provide a high efficient system for high quality vine production. Therefore, we will test the initial models when a decent set of data will be collected. The main contribution of this paper is comparing different methods based on experimental data from the prototype. This will allow us to build an integrated system for prediction of diseases in vineyards.
Keywords: Internet of Things (loT) , prediction models , vineyard diseases , machine learning , deep learning