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

被引:21
作者
Sharma, Amit [1 ,4 ]
Sharma, Ashutosh [1 ,2 ]
Tselykh, Alexey [4 ]
Bozhenyuk, Alexander [4 ]
Choudhury, Tanupriya [3 ]
Alomar, Madani Abdu [5 ]
Sanchez-Chero, Manuel [6 ]
机构
[1] Chitkara Univ, Inst Engn & Technol, Chandigarh, Punjab, India
[2] Univ Petr & Energy Studies, Sch Comp Sci, Dehra Dun, India
[3] Symbiosis Int Univ, Symbiosis Inst Technol, Pune 412115, Maharashtra, India
[4] Southern Fed Univ, Inst Comp Technol & Informat Secur, Taganrog 347922, Russia
[5] King Abdulaziz Univ, Fac Engn Rabigh, Dept Ind Engn, Jeddah 21589, Saudi Arabia
[6] Univ Nacl Frontera, Fac Ingn Ind Alimentarias & Biotecnol, Sullana, Peru
关键词
IoT; artificial intelligence; methanol; renewal energy; sustainable development;
D O I
10.1515/biol-2022-0713
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:17
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