International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AgroFusion AI: A Cognitive Multi-Model Framework for Soil-Guided Crop Intelligence and Visual Disease Reasoning |
|---|---|
| ABSTRACT | We describe herein the system designed and put into operation with considerable machine learning facsimiles to attain accuracy and efficiency through automated data analysis and forecasting modelling. This particular approach is to conduct the structured data processing based upon model training that assures credible delivery of optimal decisions. Another huge focus area is the scale of the system and performance and consistency of output results. The experimental evaluation has shown that the method improves prediction accuracy while minimizing manual effort, thus allowing application both in academia and real-life situations. |
| AUTHOR | SANDEEP S K, SANJAY G M, NIRANJAN R M, VEERESH G S, RASHMI S P UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312117 |
| pdf/117_AgroFusion AI A Cognitive Multi-Model Framework for Soil-Guided Crop Intelligence and Visual Disease Reasoning.pdf | |
| KEYWORDS | |
| References | [1] T. M. Mitchell, Machine Learning,McGraw-Hill, New York, 1997. [2] I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann, 2011. [3] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. [4] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. [5] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Elsevier, 2012. [6] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [7] A. Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O’Reilly Media, 2019. |
