International Journal of Computer Techniques Volume 12 Issue 4 | CROP CLASSIFIER : RNN VS KNN FOR AGRICULTURAL LAND IMAGE CLASSIFICATION
CROP CLASSIFIER: RNN vs KNN for Agricultural Land Image Classification
Authors:
Telagarapu Ramakrishna – Master of Computer Applications, JNTUH, Kukatpally, Telangana, India
Dr. V. Uma Rani – Professor & Head, Department of Information Technology, JNTUH, Kukatpally, Telangana, India
Sunitha Vanamala – Lecturer, Department of Computer Science, TSWRDCW, Warangal East, Telangana, India
Journal: International Journal of Computer Techniques (IJCT)
Volume: 12 | Issue: 4 | Publication Date: July – August 2025
ISSN: 2394-2231 | Journal URL: https://ijctjournal.org/
Abstract
This paper presents a comparative study of Regression Neural Network (RNN) and K-Nearest Neighbors (KNN) for classifying agricultural land images. Using PCA for feature reduction and PyTorch for model development, the RNN model demonstrated superior accuracy over KNN. A Flask-based web app enables real-time image classification, offering scalable solutions for agricultural monitoring and land management.
Keywords
Agricultural Land Classification, Regression Neural Network, PyTorch, KNN, PCA, Deep Learning, Flask, Image Processing
Conclusion
The RNN model outperformed KNN in classifying complex land images, validating the strength of deep learning in remote sensing applications. The integrated Flask web app provides a practical interface for real-time classification, reinforcing the potential of AI in agriculture and environmental analysis.
References
Includes references from IJCA, IJERT, IEEE, Kaggle, and MIT Press covering land image classification, PCA, and deep learning techniques.
