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.
R. Thirumurugan1, Taufeeq Ahmed2, Rajeeb Lochan Moharana3, Abhijeet Das4
Indian Institute of Technology Hyderabad, India1, University of Technology and Applied Sciences-Nizwa, Sultanate of Oman2, Odisha University of Agriculture and Technology, India3, C.V. Raman Global University, India4
Precision Agriculture, Artificial Intelligence, Data Mining, 3D Convolutional Neural Network, Crop Production
| January | February | March | April | May | June | July | August | September | October | November | December |
| 0 | 15 | 10 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 14 , Issue: 3 , Pages: 3270 - 3274 )
Date of Publication :
January 2024
Page Views :
136
Full Text Views :
30
|