Improving the Food and Agriculture Sector Tehran Stock Exchange by using Artificial Intelligence

Subject Areas : Agriculture Marketing and Commercialization

Hamid Mir ¹ (Department of accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran,)
Ramin Zaraatgari ² (Accounting Department of Sistan and Baluchestan University, Zahedan,)
Reza Sotoudeh ³ (Department of Accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran)

Received: 2021-02-09 Accepted : 2021-09-05 Published : 2021-12-01

Keywords:

References:

• Rockström J. & Williams J. & Daily G. (2017), Sustainable intensification of agriculture for human prosperity and global sustainability, Ambio, 46(1): 4-17.


• Krishna K. (2016), Push Button Agriculture: Robotics, Drones, Satellite-Guided Soil and Crop Management, USA: Apple Academic Press, Waretown, NJ, 2(1): 17-28.


• Ben­­_Ayed R. & Kamoun_Grati N. & Rebai A. (2013), “An overview of the authentication of olive tree and oil”, Comprehensive Reviews in Food Science and Food Safety, 12(1): 218-227.


• Tripoli M. & Schmidhuber J. (2018), Emerging Opportunities for the Application of Blockchain in the Agri-Food Industry, Rome, Italy: FAO and ICTSD, 13(4): 115-224.


• Mohri M. & Rostamizadeh A. (2018), Talwalkar, Foundations of Machine Learning, USA: MIT Press, Cambridge, MA, 6(2): 85-96.


• Ahir K. & Govani K. & Gajera R. & Shah M. (2020), Application on virtual reality for enhanced education learning, military training and sports, Augmented Human Research, 7(5): 112-119.


• Patelli N. & Mandrioli M. (2020), “Blockchain technology and traceability in the agrifood industry,” Journal of Food Science, 85(11): 3670-3678.


• Jha K. & Doshi A. & Patel P. Shah M. (2019), “A comprehensive review on automation in agriculture using artificial intelligence,” Artificial Intelligence in Agriculture, 2(4): 1-12.


• Gao Z. & Shao Y. & Xuan G. (2020), “Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning,” Artificial Intelligence in Agriculture, 4(1): 31-38.


• Liakos K. & Busato P. & Moshou D. & Pearson S. & Bochtis D. (2018), “Machine learning in agriculture: a review”, Sensors, 18(8): 2674-2018.


• Sujatha R. & Chatterjee M. & Jhanjhi N. (2021), “Performance of deep learning vs machine learning in plant leaf disease detection,” Microprocessors and Microsystems, 80(2): 103-116.


• Gharaei A. & Karimi M. & Hoseini Shekarabi S. (2019), “An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: the outer approximation with equality relaxation and augmented penalty algorithm”, Applied Mathematical Modelling, 69(1): 223-254.


• Traore B. & Kamsu-Foguem B. & Tangara F. (2017), “Data mining techniques on satellite images for discovery of risk areas”, Expert Systems with Applications, 7(2): 443-456.


• Jordan M. & Mitchell T. (2015), “Machine learning: trends, perspectives, and prospects”, Science, 34(2): 113-125.


• Crane-Droesch A. (2018), “Machine learning methods for crop yield prediction and climate change impact assessment in agriculture,” Environmental Research Letters, 13(11): 114003-114012.


• Fente D. & Singh G. (2018), “Weather forecasting using artificial neural network,” Proceedingsof the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 11(1): 127-13.


• Suchithra M. & Pai M. (2020), “Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters,” Information Processing in Agriculture, 7(1): 72-82.


• Kamilaris A. & Prenafeta-Boldú F. (2018), “Deep learning in agriculture: a survey,” Computers and Electronics in Agriculture, 14(7): 70-90.


• Suprem A. & Mahalik N. & Kim K. (2013), “A review on application of technology systems, standards and interfaces for agriculture and food sector”, Computer Standards & Interfaces, 33(4): 355-364.


• O’Grady M. (2019), “Edge computing: a tractable model for smart agriculture?” Artificial Intelligence in Agriculture, 3(1): 42-51.


• Morellos A. & Pantazi X. & Moshou D. (2018), “Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy,” Biosystems Engineering, 15(2): 104-116.


• Zhang C. (2018), “Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation,” Science of The Total Environment, 12(4): 631-632.


• Ahumada O. & Villalobos J. (2009), “Application of planning models in the agri-food supply chain: a review”, European Journal of Operational Research, 19(6): 1-20.


• Nahvi B. & Habibi J. & Mohammadi K. & Shamshirband S. (2016), “Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature”, Computers and Electronics in Agriculture, 12(4): 150-160.


• Kumar R. & Singh M. & Kumar P. & Singh J. (2019), “Crop Selection Method to maximize crop yield rate using machine learning technique,” in Proceedings of the 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 8(1): 138-145.


• Elavarasan D. & Vincent D. & Sharma V. & Zomaya A. (2018), “Forecasting yield by integrating agrarian factors and machine learning models: a survey,” Computers and Electronics in Agriculture, 15(5): 257-282.


• Goap A. & Sharma D. & Shukla A. (2018), “An IoT based smart irrigation management system using Machine learning and open source technologies,” Computers and Electronics in Agriculture, 15(5): 41-49.