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Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: 10.64388/IREV10I3-1722922
Abstract
Crop recommendation systems that integrate soil and climate data with artificial intelligence (AI) have expanded rapidly since 2020, spanning classical machine learning (ML), ensemble methods, deep learning, explainable AI (XAI), and Internet of Things (IoT)-enabled sensing. This review critically synthesizes 35 sources — 33 peer-reviewed journal articles and conference papers plus 2 preprints retained only for background context — comprising 12 studies verified in full against their primary text and 23 studies verified at the bibliographic level, to examine what has been attempted, which data and algorithms have been used, and how reliable the reported results are. The reviewed literature shows convergent use of a narrow feature set (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and recurring near-ceiling accuracy, including cases at or above 98% [1], [5], [6] and, in one case, a reported 1.00 across accuracy, precision, recall, and F1-score following class-balancing [7]. Cross-examination of dataset descriptions across studies reveals inconsistent provenance and documentation: structurally similar seven-feature datasets are described with different national contexts and reported sample sizes ranging from 2,100 to 3,000 records [5], [6], [9]. Validation practice is dominated by random hold-out or k-fold cross-validation; among the studies examined in full, only one tested spatial cross-validation, reporting a substantial performance decline (AUC 0.89 to 0.55–0.62) relative to random-split results [4]. Explainable AI and uncertainty quantification remain minority practices in the reviewed literature. A prior conference proceedings paper self-described as a "systematic literature review" of graph-based crop recommendation is examined directly and found not to meet standard systematic-review methodology [2]. Building on these findings, this review identifies evidence-supported research gaps and proposes a conceptual framework intended to inform more reliable, explainable, and field-validated crop recommendation systems.
Keywords
crop recommendation; explainable ai; machine learning; precision agriculture; soil and climate data
References
[1] S. Shastri, S. Kumar, V. Mansotra, and R. Salgotra, “Advancing crop recommendation system with supervised machine learning and explainable artificial intelligence,” Scientific Reports, vol. 15, art. 25498, 2025. Crossref
[2] P. Ayesha Barvin and T. Sampradeepraj, “Crop recommendation systems based on soil and environmental factors using graph convolution neural network: A systematic literature review,” Engineering Proceedings, vol. 58, no. 1, art. 97, 2023. Crossref
[3] M. Y. Shams, S. A. Gamel, and F. M. Talaat, “Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making,” Neural Computing and Applications, vol. 36, no. 11, pp. 5695–5714, 2024. Crossref
[4] S. Moharana, S. R. Naitam, D. O. Shirale, S. K. Adamala, N. Dash, C. Amrutha, A. Raghuvanshi, S. Karthikeyan, D. R. Biswas, and A. Patil, “Modeling climate-resilient crop suitability in central India using machine learning and species distribution modeling approaches,” Theoretical and Applied Climatology, vol. 156, art. 604, 2025.
[5] Afzal, S. Amjad, M. Raza, M. Munir, F. Villar, and A. Ashraf, “Incorporating soil information with machine learning for crop recommendation to improve agricultural output,” Scientific Reports, vol. 15, art. 8560, 2025.
[6] Dey, J. Ferdous, and R. Ahmed, “Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables,” Heliyon, vol. 10, no. 3, art. e25112, 2024. Crossref [Tier B — authorship, affiliation, and methodology confirmed via primary/near-primary sources; specific accuracy figures remain cross-source verified]
[7] M. K. Senapaty, A. Ray, and N. Padhy, “A decision support system for crop recommendation using machine learning classification algorithms,” Agriculture, vol. 14, no. 8, art. 1256, 2024. Crossref
[8] S. Rani, A. K. Mishra, A. Kataria, S. Mallik, and H. Qin, “Machine learning-based optimal crop selection system in smart agriculture,” Scientific Reports, vol. 13, art. 15997, 2023. Crossref
[9] G. Singh and S. Sharma, “Enhancing precision agriculture through cloud based transformative crop recommendation model,” Scientific Reports, vol. 15, art. 9138, 2025. Crossref
[10] Y. Akkem, S. K. Biswas, and A. Varanasi, “Role of explainable AI in crop recommendation technique of smart farming,” International Journal of Intelligent Systems and Applications, vol. 17, no. 1, pp. 31–52, 2025. Crossref
[11] Authors not independently confirmed, “Interpretable deep learning models for independent fertilizer and crop recommendation,” Scientific Reports, 2025. [FLAGGED — authorship unconfirmed after two independent verification attempts; reported descriptively only and not used to support any conclusion beyond this qualified instance]
[12] Y. Mahale, N. Khan, K. Kulkarni, S. A. Wagle, P. Pareek, K. Kotecha, T. Choudhury, and A. Sharma, “Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique,” Discover Sustainability, vol. 5, no. 1, pp. 1–23, 2024.
How to cite this paper
@article{1722922,
author = {Snehal Sanjay Raut, Dr. Yogesh V. Chimate},
title = {Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {1342-1360},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722922.pdf},
abstract = {Crop recommendation systems that integrate soil and climate data with artificial intelligence (AI) have expanded rapidly since 2020, spanning classical machine learning (ML), ensemble methods, deep learning, explainable AI (XAI), and Internet of Things (IoT)-enabled sensing. This review critically synthesizes 35 sources — 33 peer-reviewed journal articles and conference papers plus 2 preprints retained only for background context — comprising 12 studies verified in full against their primary text and 23 studies verified at the bibliographic level, to examine what has been attempted, which data and algorithms have been used, and how reliable the reported results are. The reviewed literature shows convergent use of a narrow feature set (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and recurring near-ceiling accuracy, including cases at or above 98% [1], [5], [6] and, in one case, a reported 1.00 across accuracy, precision, recall, and F1-score following class-balancing [7]. Cross-examination of dataset descriptions across studies reveals inconsistent provenance and documentation: structurally similar seven-feature datasets are described with different national contexts and reported sample sizes ranging from 2,100 to 3,000 records [5], [6], [9]. Validation practice is dominated by random hold-out or k-fold cross-validation; among the studies examined in full, only one tested spatial cross-validation, reporting a substantial performance decline (AUC 0.89 to 0.55–0.62) relative to random-split results [4]. Explainable AI and uncertainty quantification remain minority practices in the reviewed literature. A prior conference proceedings paper self-described as a "systematic literature review" of graph-based crop recommendation is examined directly and found not to meet standard systematic-review methodology [2]. Building on these findings, this review identifies evidence-supported research gaps and proposes a conceptual framework intended to inform more reliable, explainable, and field-validated crop recommendation systems.},
keywords = {crop recommendation; explainable ai; machine learning; precision agriculture; soil and climate data},
month = {September},
doi = {https://doi.org/10.64388/IREV10I3-1722922}
}