Smart Farming: Crop Recommendation Using Machine Learning with Challenges and Future Ideas

Abstract

This paper addresses the critical challenge of optimizing crop selection in agriculture to enhance food production sustainably. The problem is framed as a multi-class classification task where the goal is to recommend the most suitable crop based on a set of environmental and soil features. While traditional methods rely on time-consuming and labor-intensive expert knowledge, this work proposes a data-driven approach using machine learning. The novelty of our investigation lies in the comprehensive comparative analysis of seven machine learning algorithms and the development of a highly accurate neural network model. We utilize a publicly available dataset from Kaggle, which has been preprocessed to ensure data quality. We provide a detailed account of our feature engineering and hyperparameter tuning processes. Our proposed neural network model, with a specific architecture of 30–20–10 neurons, achieves a validation accuracy of 97.73%. This work also discusses the challenges of deploying such models, including real-world data variability and the need for model interpretability. We demonstrate that our approach, particularly the neural network model, provides a robust, scalable, and adaptable solution for crop recommendation, outperforming other models (in holistic view) like Random Forest which achieved a slightly higher accuracy of 99.5% on this specific dataset but with less generalization potential. The findings of this study can empower farmers to make informed decisions, ultimately leading to improved crop yields, enhanced soil fertility, and greater profitability.

Received: 22 May 2025 | Revised: 11 July 2025 | Accepted: 26 August 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset/data and in TechRxiv at https://www.techrxiv.org/doi/full/10.36227/techrxiv.23504496, reference number [41].

Author Contribution Statement

Devendra Dahiphale: Conceptualization, Methodology, Software, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Pratik Shinde: Validation, Investigation, Writing – review & editing. Koninika Patil: Writing – review & editing. Vijay Dahiphale: Validation, Writing – review & editing, Visualization, Project administration.