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Paper Title
Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System
Authors
Manraj Singh Sidhu , Rachana Rajput , Gursajan Thapa
Keywords
LSTM, SARIMA, crop price forecasting, soil mineral analysis, crop recommendation, Random Forest, deep learning, agricultural AI, Indian agriculture, decision-support systems
Abstract
This research presents an integrated AI framework addressing two critical challenges in Indian agriculture: (1) crop price forecasting for 2026 and (2) data-driven crop recommendation based on soil mineral composition. Using 16 years of historical price data (2010-2025) for 16 major Indian crops, we implement LSTM neural networks and compare with traditional SARIMA models, demonstrating LSTM superiority with 15-25% higher accuracy (R2 = 0.88 vs 0.82 for SARIMA, RMSE reduction of 18.7%, MAPE improvement of 29.3%). The LSTM model generates month-by-month 2026 price predictions with trend analysis
(UP/DOWN/FLAT), capturing complex non-linear market dynamics. Simultaneously, we introduce a novel Soil Mineral-Based Crop Recommendation System using Random Forest classification that analyzes soil nutrients (Nitrogen, Phosphorus, Potassium, pH, Organic Carbon) and environmental conditions to recommend optimal crops for specific soil profiles, achieving 96.8% classification accuracy. This dual-pipeline system enables farmers to: (a) identify suitable crops based on soil properties through machine learning, and (b) optimize selling timing through AI-driven price forecasts. Together, these two components form a comprehensive decision-support system enabling farmers to transition from reactive, experience-based agriculture to proactive, data-driven decision-making, ultimately supporting income stabilization and food security objectives across India's agricultural landscape.
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IJCRT — journal style
"Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 2, page no.d147-d158, February 2026, Available :https://ijcrt.org/papers/IJCRT2602360.pdf
APA — 7th edition
Sidhu, M. S., Rajput, R., & Thapa, G. (2026). Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System. International Journal of Creative Research Thoughts, 14(2), d147-d158. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602360
MLA — 9th edition
Sidhu, Manraj Singh, et al. "Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System." International Journal of Creative Research Thoughts, vol. 14, no. 2, 2026, pp. d147-d158, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602360.
Chicago — 17th, bibliography
Sidhu, Manraj Singh, Rachana Rajput, and Gursajan Thapa. "Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System." International Journal of Creative Research Thoughts 14, no. 2 (2026): d147-d158. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602360.
Harvard — author–date
Sidhu, M.S., Rajput, R. and Thapa, G. (2026) 'Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System', International Journal of Creative Research Thoughts, 14(2), pp. d147-d158. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602360
IEEE — numbered reference
M. S. Sidhu, R. Rajput and G. Thapa, "Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System," IJCRT, vol. 14, no. 2, pp. d147-d158, Feb. 2026.
Vancouver — biomedical
Sidhu MS, Rajput R, Thapa G. Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System. IJCRT. 2026 Feb;14(2):d147-d158.
AMA — 11th edition
Sidhu MS, Rajput R, Thapa G. Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System. IJCRT. 2026;14(2):d147-d158. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602360
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2602360,
author = {Manraj Singh Sidhu and Rachana Rajput and Gursajan Thapa},
title = {Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {2},
pages = {d147-d158},
year = {2026},
month = {February},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602360}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Sidhu, Manraj Singh
AU - Rajput, Rachana
AU - Thapa, Gursajan
TI - Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 2
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602360
SP - d147-d158
ER -
Issue
Volume 14 Issue 2,
February-2026
Pages : d147-d158
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 14, Issue 2, February 2026
- Pages
- d147-d158
- Published
- February 2026
- Registration ID
- IJCRT_301089
- Published Paper ID
- IJCRT2602360
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Bathinda, Punjab, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 195
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602360
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2602360.pdf
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