IoT, Big Data Analytics and Deep Learning for Sustainable Precision Agriculture

Abstract

Agriculture is undergoing a digital transformation because of population growth, climate change, and food security concerns. Agriculture is influenced by information technology in terms of cost reduction, efficiency, and sustainability. Precision agriculture employs IoT, deep learning, predictive analytics, and AI-based technologies to aid in the detection of plant diseases, pests, and poor plant nutrition in the field. The study's objectives are as follows: 1) evaluate the role of smart technologies and their impact on precision agriculture sustainability; 2) assess the typical application of IoT data analytic and deep learning in precision agriculture; and 3) investigate the barriers to the adoption of sustainable precision farming. IoT technologies collect data and relay it to data analytics and deep learning for in-depth analysis. According to the findings, data assists farmers in managing crop variety, phenotypes and selection, crop performance, soil quality, pH level, irrigation, and fertilizer application quantity. Technological issues, safety, privacy, cost, and legal issues also influence the adoption of these technologies.

Keywords: IoT, Big Data Analytics, Deep Learning, Precision Agriculture, Sustainability

DOI: 10.7176/JIEA/13-1-03

Publication date: February 28^th 2023