ICTACT Journal on Soft Computing

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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
     

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

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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