ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Optimizing Crop Management and Production with Artificial Intelligence Data Mining Using 3D Convolutional Neural Network for Precision Agriculture


Affiliations
1 Department of Electrical Engineering, Indian Institute of Technology Hyderabad, India
2 Computer Engineering, College of Engineering and Technology, University of Technology and Applied Sciences-Nizwa, Oman
3 Seed Science and Technology, College of Agriculture, Odisha University of Agriculture and Technology, India
4 Department of Civil Engineering, C.V. Raman Global University, India
     

   Subscribe/Renew Journal


In precision agriculture, optimizing crop management is essential for sustainable and efficient food production. This research leverages artificial intelligence (AI) data mining techniques, specifically employing a 3D CNN, to enhance precision in wheat crop production. The background underscores the need for advanced technologies in agriculture to address the challenges of increasing global demand and environmental sustainability. The method involves the utilization of 3D CNN for simultaneous feature extraction and prediction, providing a holistic approach to crop monitoring. The contribution of this research lies in the integration of AI-driven data mining to streamline crop management processes, resulting in improved resource utilization and increased yield. The application of 3D CNN demonstrated superior performance in accurately predicting wheat crop production. The model effectively extracted intricate spatial and temporal features, contributing to enhanced decision-making capabilities for farmers. The findings highlight the potential of AI-driven precision agriculture in revolutionizing crop management, offering a scalable solution for sustainable food production.

Keywords

Precision Agriculture, Artificial Intelligence, Data Mining, 3D Convolutional Neural Network, Crop Production.
Subscription Login to verify subscription
User
Notifications
Font Size

  • K. Thiyagarajan and R. Kalaiyarasi, “Status Paper on Rice in Tamilnadu”, Rice Knowledge Management Portal Publisher, 2010.

  • Paddy Expert Systems, Available at: http://agritech.tnau.ac.in/expert_system/paddy/Index.html, Accessed in 2020.

  • R. Anirudh Reddy, G. Laasya, T. Sowmya, P. Sindhuja and M. Basha, “Image Processing for Weed Detection”, International Journal of Engineering Technology, Management and Applied Sciences, Vol. 5, No. 4, pp. 485-489, 2017.

  • R.R. Mangala and A. Padmapriya, “Prediction Based Agro Advisory System for Crop Protection”, Proceedings of International Conference on Intelligent Data Communication Technologies and Internet of Things, pp. 1-7, 2019.

  • H. Liu, S. Lee and C. Saunders, “Development of a Machine Vision System for Weed Detection during both of OffSeason and In-Season in Broad Acre No-Tillage Cropping Lands”, American Journal of Agricultural and Biological Sciences, Vol. 9, pp. 174-193,2014.

  • T. Sarvini, T. Sneha, G.S. Sukanya Gowthami, S. Sushmitha and R Kumaraswamy, “Performance Comparison of Weed Detection Algorithms”, Proceedings of International Conference on Communication and Signal Processing, pp. 843-847, 2019.

  • R.S. Gray, “Agriculture, Transportation, and the COVID‐19 Crisis”, Canadian Journal of Agricultural Economics/Revue Canadienne D'agroeconomie, Vol. 68, No. 2, pp. 239-243, 2020.

  • M. Bhatia and G.M.J. Bhat, “Agriculture Supply Chain Management-An Operational Perspective”, Brazilian Journal of Operations and Production Management, Vol. 17, No. 4, pp. 1-18, 2020.

  • I. Itang, H. Sufyati, and M. Fahlevi, “Supply Chain Management, Supply Chain Flexibility and Firm Performance: An Empirical Investigation of Agriculture Companies in Indonesia”, Uncertain Supply Chain Management, Vol. 10, No. 1, pp. 155-160, 2022.

  • Ian Goodfellow, YoshuaBengio and Aaron Courville, “Deep Learning”, MIT press, 2016.

  • Xanthoula Eirini, Dimitrios Moshou and Alexandra A. Tamouridou. “Automated Leaf Disease Detection in Different Crop Species through Image Features Analysis and One Class Classifiers”, Computers and Electronics in Agriculture, Vol. 156, pp. 96-104, 2019.


Abstract Views: 102

PDF Views: 0




  • Optimizing Crop Management and Production with Artificial Intelligence Data Mining Using 3D Convolutional Neural Network for Precision Agriculture

Abstract Views: 102  |  PDF Views: 0

Authors

R. Thirumurugan
Department of Electrical Engineering, Indian Institute of Technology Hyderabad, India
Taufeeq Ahmed
Computer Engineering, College of Engineering and Technology, University of Technology and Applied Sciences-Nizwa, Oman
Rajeeb Lochan Moharana
Seed Science and Technology, College of Agriculture, Odisha University of Agriculture and Technology, India
Abhijeet Das
Department of Civil Engineering, C.V. Raman Global University, India

Abstract


In precision agriculture, optimizing crop management is essential for sustainable and efficient food production. This research leverages artificial intelligence (AI) data mining techniques, specifically employing a 3D CNN, to enhance precision in wheat crop production. The background underscores the need for advanced technologies in agriculture to address the challenges of increasing global demand and environmental sustainability. The method involves the utilization of 3D CNN for simultaneous feature extraction and prediction, providing a holistic approach to crop monitoring. The contribution of this research lies in the integration of AI-driven data mining to streamline crop management processes, resulting in improved resource utilization and increased yield. The application of 3D CNN demonstrated superior performance in accurately predicting wheat crop production. The model effectively extracted intricate spatial and temporal features, contributing to enhanced decision-making capabilities for farmers. The findings highlight the potential of AI-driven precision agriculture in revolutionizing crop management, offering a scalable solution for sustainable food production.

Keywords


Precision Agriculture, Artificial Intelligence, Data Mining, 3D Convolutional Neural Network, Crop Production.

References