Artificial intelligence agriculture recommendation model (AIARM)

https://doi.org/10.53730/ijhs.v6nS2.5370

Authors

  • G. Jaya Lakshmi Department of Information Technology, V R Siddhartha Engineering College Vijayawada, Andhra Pradesh, India
  • Shams Amer Najy Hilmi Engineering Building and construction technology, Al Esraa University College, Iraq
  • Ahmed J. Obaid Faculty of Computer Science and Mathematics, University of Kufa, Iraq

Keywords:

agricultural data, land suitability, usage sensors, multi-layer perceptron, sensor statistics agriculture, smart agriculture

Abstract

Agricultural production is extremely important to the global economy. Agribusiness not only provides food and raw materials, but also provides employment opportunities to a large portion of the population.Increased agricultural production and per-capita income in rural areas, combined with industrialization and urbanization, resulting in increased demand for industrial goods.According to a analysis conducted by the Food and Agriculture Organization, the world's population is projected to increase by another two billion people by 2050, while cropland is only expected to increase by 5%. As a result, to increase agricultural productivity, smart and capable farming approaches are needed. Agriculture land suitability appraisement is a required tools for agriculture advancement. The fast growth of wireless networks has resulted in the development of low-cost Internet of Things (IoT) devices that are favored as a useful methodology for agricultural autonomy and decision making. The proposed model, called the Artificial Intelligence Agriculture Recommendation Model (AIARM), incorporates sensory networks and artificial intelligence programs like neural networks and multi-layer perceptron to determine crop readiness, crop prediction, and fertilizer recommendations. Instead of a binary split, the proposed system divides agricultural land into four categories of decisions, which are fair, appropriate, fairly equitable, and not appropriate to guide farmers accurately.

Downloads

Download data is not yet available.

References

Fanyu Bu a , Xin Wang,”A smart agriculture IoT system based on deep reinforcement learning”,Elsevier B.V,2019

SAI SREE LAYA CHUKKAPALLI1 , SUDIP MITTAL2 , MAANAK GUPTA3 , MAHMOUD ABDELSALAM 4 , ANUPAM JOSHI1 , RAVI SANDHU6 , KARUNA JOSHI7,”Ontologies and Artificial Intelligence Systems for the Cooperative Smart Farming Ecosystem”,10.1109/ACCESS.2020.3022763, IEEE Access,2017.

Rehna Baby Joseph ,Lakshmi M.B,Dr. Salini Suresh,Dr. R. Sunder,”Innovative Analysis of Precision Farming Techniques with Artificial Intelligence”,IEEE Xplore Part Number: CFP20K58-ART; ISBN: 978-1-7281-4167-1,2020.

Bhanu K N ,Jasmine H J,Mahadevaswamy H S,”Machine learning Implementation in IoT based Intelligent System for Agriculture”,2020 International Conference for Emerging Technology (INCET),IEEE,2020.

Priyanka Kanupuru1 ,N.V. Uma Reddy 2 ,”Survey on IoT and its Applications in Agriculture”,978-1-5386-7949-4/18 ©2018 IEEE,2018.

Richa Singh,Sarthak Srivastava,Rajan Mishra,”AI and IoT Based Monitoring System for Increasing the Yield in Crop Production”,2020 International Conference on Electrical and Electronics Engineering (ICE3-2020),IEEE,2020.

Sebastian Sadowski, PetrosSpachos,”Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities.”,Computers and Electronics in Agriculture 172 (2020) 105338,0168-1699/ © 2020 Elsevier B.V,2020.

PremkumarChithaluru, Fadi Al-Turjman, Manoj Kumar, Thompson Stephan,”I-AREOR: An Energy-balanced Clustering Protocol for implementing Green IoT for smart cities”,Elsevier,2020.

Bruno Citoni, Francesco Fioranelli, Muhammad A. Imran, and Qammer H. Abbasi,”Internet of Things and LoRaWAN-Enabled Future Smart Farming.”IEEE Internet of Things Magazine,2020.

PetrosSpachos,”Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices.”,IoT 2020, 1, 2; doi:10.3390/iot1010002,mdpi,2020.

Xiang Feng,Fang Yan,Xiaoyu Liu ,”Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture”,Springer Science+Business Media, LLC, part of Springer Nature ,2019

FakhriAlam Khan1,Awais Ahmad1,Muhammad Imran,”Energy Optimization of PR-LEACH Routing Scheme Using Distance Awareness in Internet of Things Networks”,© Springer Science+Business Media, LLC, part of Springer Nature ,2018

Tien N. Nguyen,Cuu V. Ho,Thien T. T. Le,”A Topology control Algorithm in wireless sensror Network.”,978-1-7281-5353-7/19/$31.00 ©2019 IEEE,2019.

Esteban Municio, Steven Latre,Johann M. Marquez-Barja,”Extending Network Programmability to the Things Overlay using Distributed Industrial IoT Protocols”,IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 14,2019.

HongmingCai, Senior Member, IEEE, Boyi Xu, Member, IEEE, Lihong Jiang, Member, IEEE, and Athanasios V. Vasilakos, Senior Member, IEEE, “iot –based big data storage systems in cloud computing perspective and challenges.”2327-4662 (c) 2016 IEEE,2016.

Yin Zhang, Xiao Ma, Jing Zhang, M. Shamim Hossain, Ghulam Muhammad, and Syed Umar Amin,”Edge Intelligence in the Cognitive Internet of Things: Improving Sensitivity and Interactivity”,IEEE Network,2019.

Shubo Liu,LiqingGuo,Heather Webb,Xiao Yao ,Xiao Chang,”Internet of Things Monitoring System of Modern Eco-agriculture Based on Cloud Computing”,2169-3536 (c) IEEE,2018.

Kamran Ahmad Awan 1 , IkramUd Din 1 , Ahmad Almogren 2, and HishamAlmajed ,”AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things”,Sensors 2020, 20, 6174; doi:10.3390/s20216174,mdpi,2020.

Seyyed Yasser Hashemi1,Fereidoon Shams Aliee1,Fuzzy,”Dynamic and Trust Based Routing Protocol for IoT.”Journal of Network and Systems Management,SpringerScience+Business Media, LLC, part of Springer Nature ,2020.

Disha Garg, Samiya Khan, and MansafAlam,”Integrative Use of IoT and Deep Learning for Agricultural Applications”,© Springer Nature Switzerland,2020

KirtanJha, AalapDoshi, Poojan Patel, Manan Shah,”A comprehensive review on automation in agriculture using artificial intelligence”,2589-7217/© 2019 Elsevier B.V,2019.

AbhijitPathaka , Mohammad AmazUddina , Md. JainalAbedina , Karl Anderssonb , RashedMustafac , Mohammad Shahadat Hossainc,”IoT based Smart System to Support Agricultural Parameters:A Case Study.”,Elsevier B.V,2019.

E.Alreshidi, Smart Sustainable Agriculture (SSA) solution underpinned by Internet of Things (IoT) and Artificial Intelligence (AI). arXiv 2019

AnushaVangala, Ashok Kumar Das, NeerajKumar,MamounAlazab,”Smart Secure Sensing for IoT-Based Agriculture: Blockchain Perspective”,IEEE Sensors Journal 1,2020.

WEI-JIAN HU1 , JIE FAN1 , YONG-XING DU1 , BAO-SHAN LI1 , NEAL N. XIONG2 , ERNST BEKKERING3.,“MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases”,10.1109/ACCESS.2020.3001237, IEEE Access,2017.

NuttakarnKitpo, Yosuke Kugai, Masahiro Inoue, Taketoshi Yokemura and Shinichi Satomura†,”Internet of Things for Greenhouse Monitoring System Using Deep Learning and Bot Notification Services.” ,JSPS KAKENHI,2019

Fanyu Bu a, Xin Wang b,”A smart agriculture IoT system based on deep reinforcement learning”,0167-739X/© 2019 Elsevier B.V,2019

AruulMozhi Varman S,Arvind Ram Baskaran,Aravindh S,PrabhuE,”Deep Learning and IoT for Smart Agriculture using WSN”,IEEE International Conference on Computational Intelligence and Computing Research,2017.

Nermeen Gamal Rezk,Ezz El-Din Hemdan,Abdel-Fattah Attia1,Ayman El-Sayed,Mohamed A. El-Rashidy,”An efficient IoT based smart farming system using machine learning algorithms”,Springer Science+Business Media, LLC, part of Springer Nature ,2020

Fahad Kamraan Syed,Agniswar Paul,Ajay Kumar,JaideepCherukuri,”Low-cost IoT+ML design for smart farming with multiple applications”,10th ICCCNT 2019 July 6-8, 2019, IIT - Kanpur Kanpur, India,IEEE – 45670,2019

DavideBrunelli, Andrea Albanese, Donato d’Acunto, and Matteo Nardello.”Energy Neutral Machine Learning Based IoT Device for Pest Detection in Precision Agriculture”,IEEE Xplore,2019.

Ana Laura Diedrichs, Facundo Bromberg, Diego Dujovne, KeomaBrun-Laguna,ThomasWatteyne,”Prediction of frost events using machine learning and IoT sensing devices”,, IEEE Internet of Things Journal 1,2018.

AmarendraGoapa,DeepakSharmab , A.K. Shuklab , C. Rama Krishnaa,”An IoT based smart irrigation management system using Machine learning and open source technologies”,2018 Elsevier B.V,2018

Arnab Kumar Saha1 , Jayeeta Saha2 , Radhika Ray3 , Sachet Sircar4 , Subhojit Dutta4 , SoummyoPriyo Chattopadhyay1 , HimadriNath Saha1,”IOT-Based Drone for Improvement of Crop Quality in Agricultural Field”,978-1-5386-4649-6/18/$31.00 ©2018 IEEE,2018.

Ahmed, A.N.; de Hussain, I.D. Internet of Things (IoT) for smart precision agriculture and farming in rural areas. IEEE Internet Things J. 2018

A. Goldstein, L. Fink, A. Meitin, S. Bohadana, O. Lutenberg, and G. Ravid, “Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge,” Precis. Agric., vol. 19, no. 3, pp. 421–444, Jun. 2018.

Y. Zhong and M. Zhao, “Research on deep learning in apple leaf disease recognition,” Comput. Electron. Agric., vol. 168, p. 105146, Jan. 2020.

C. Maione, B. L. Batista, A. D. Campiglia, F. Barbosa, and R. M. Barbosa, “Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry,” Comput. Electron. Agric., vol. 121, pp. 101–107, Feb. 2016.

A. Goap, D. Sharma, A. K. Shukla, and C. Rama Krishna, “An IoT based smart irrigation management system using Machine learning and open source technologies,” Comput. Electron. Agric., vol. 155, pp. 41–49, Dec. 2018.

Akbar, A., Agarwal, P., Obaid, A. (2022). Recommendation engines-neural embedding to graph-based: Techniques and evaluations. International Journal of Nonlinear Analysis and Applications, 13(1), 2411-2423. doi: 10.22075/ijnaa.2022.5941.

Chandrashekhar Meshram, Rabha W. Ibrahim, Ahmed J. Obaid, Sarita Gajbhiye Meshram, Akshaykumar Meshram, Alaa Mohamed Abd El-Latif, Fractional chaotic maps based short signature scheme under human-centered IoT environments, Journal of Advanced Research, 2020, ISSN 2090-1232, https://doi.org/10.1016/j.jare.2020.08.015.

Published

01-04-2022

How to Cite

Lakshmi, G. J., Hilmi, S. A. N., & Obaid, A. J. (2022). Artificial intelligence agriculture recommendation model (AIARM). International Journal of Health Sciences, 6(S2), 1782–1808. https://doi.org/10.53730/ijhs.v6nS2.5370

Issue

Section

Peer Review Articles