Comparative Analysis of Machine Learning Algorithms for Effective Crop Recommendation

Title

Comparative Analysis of Machine Learning Algorithms for Effective Crop Recommendation

Creator

Gamya, K.; Saxena, Surabhi; Singhal, Neha

Description

The global call for sustainable farming necessitates a move away from traditional crop selection methods. These conventional approaches, often relying on farmer intuition, are imprecise and scale poorly in the face of complex environmental variables. Machine Learning (ML) models offer a robust, data-driven solution. By analyzing multifaceted data-spanning soil chemistry, weather patterns, precipitation trends, and historical yield performance-ML models can significantly enhance decision-making, optimize resource utilization, and improve overall crop outcomes. This paper delivers an extensive comparative review of key ML algorithms employed for crop recommendation, including Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). We also explore the critical role of Explainable AI (XAI) in building model transparency. Our study evaluates these models on the metrics of accuracy, interpretability, and computational overhead. The research also investigates hybrid methods that integrate deep learning with conventional ML to enhance predictive power. Our comparative findings highlight the strengths and weaknesses of each model, concluding that ANN and XAI-based approaches demonstrate the highest accuracy and adaptability for diverse agricultural conditions. We also identify significant challenges, such as data imbalances and the absence of real-time data, and discuss future trends like the integration of IoT, remote sensing, and federated learning, which will be key to making precision farming scalable and accessible. 2025 IEEE.

Source

1st IEEE International Conference on Data Science and Intelligent Network Computing, ICDSINC 2025;pp.886-890

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

AI in Farming; Crop Recommendation; Machine Learning; Precision Agriculture; Soil Analysis

Coverage

Gamya K., Department of Computer Science, Christ University, Bengaluru, India; Saxena S., Department of Computer Science, Christ University, Bengaluru, India; Singhal N., Department of Computer Science, Christ University, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833156524-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICDSINC66221.2025.11448153

https://www.scopus.com/pages/publications/105037428427?origin=resultslist