A Multicloud-Based Deep Learning Model for Smart Agricultural Applications

Abstract

Modern agriculture primarily relies on smart agriculture to predict crop yields and make decisions. Crop productivity could suffer due to a lack of farmers, labor shortages in the agricultural sector, adverse weather, etc. Smart farming uses advanced technology to improve the productivity and efficiency of agriculture. Crop yield is increased with smart agriculture, which also keeps an eye on agricultural pests. Artificial intelligence is an innovative technology that uses sensor data to predict the future and make judgments for farmers. AI methods like machine learning and deep learning are the most clever way to boost agricultural productivity. Adopting AI can help with farming issues and promote increased food production. Deep learning is a modern method for processing images and analyzing big data, showing promise for producing superior results. The primary goals of this study are to examine the benefits of employing DL in smart agricultural applications and to suggest a multi-cloud DL architecture for such applications.