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Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture

  • Amit Sharma
  • , Ashutosh Sharma
  • , Alexey Tselykh
  • , Alexander Bozhenyuk
  • , Tanupriya Choudhury
  • , Madani Abdu Alomar
  • , Manuel Sánchez-Chero
  • Southern Federal University
  • Chitkara University
  • University of Petroleum and Energy Studies
  • Symbiosis International University
  • King Abdulaziz University

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

Agriculture encompasses the study, practice, and discipline of plant cultivation. Agriculture has an extensive history dating back thousands of years. Depending on climate and terrain, it began independently in various locations on the planet. In comparison to what could be sustained by foraging and gathering, agriculture has the potential to significantly increase the human population. Throughout the twenty-first century, precision farming (PF) has increased the agricultural output. precision agriculture (PA) is a technology-enabled method of agriculture that assesses, monitors, and evaluates the needs of specific fields and commodities. The primary objective of this farming method, as opposed to conventional farming, is to increase crop yields and profitability through the precise application of inputs. This work describes in depth the development and function of artificial intelligence (AI) and the internet of things (IoT) in contemporary agriculture. Modern day-to-day applications rely extensively on AI and the IoT. Modern agriculture leverages AI and IoT for technological advancement. This improves the accuracy and profitability of modern agriculture. The use of AI and IoT in modern smart precision agricultural applications is highlighted in this work and the method proposed incorporates specific steps in PF and demonstrates superior performance compared to existing classification methods. It achieves a remarkable accuracy of 98.65%, precision of 98.32%, and recall rate of 97.65% while retaining competitive execution time of 0.23 s, when analysing PF using the FAOSTAT benchmark dataset. Additionally, crucial equipment and methods used in PF are described and the vital advantages and real-time tools utilised in PA are covered in detail.

Original languageEnglish
Article number20220713
JournalCentral European Journal of Biology
Volume18
Issue number1
DOIs
StatePublished - 1 Jan 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger

Keywords

  • IoT
  • artificial intelligence
  • methanol
  • renewal energy
  • sustainable development

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