Explainable AI for Crop Recommendation, Yield Forecasting, and Rainfall Prediction in Smart Agriculture

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 73-79, March 2026.

https://doi.org/10.58482/ijersem.v2i3.10

This work is licensed under a Creative Commons Attribution 4.0 International License.

Full Text PDF

Explainable AI for Crop Recommendation, Yield Forecasting, and Rainfall Prediction in Smart Agriculture

R Priyadarshini

Harshita Sinha

Sai Sowmya

Shaik Masoodh

Maddikera Siva Sankara Prasad

N Vijayalakshmi

Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, India.

Abstract: This paper introduces a machine learning-based crop advice, rainfall prediction, and yield forecasting system to streamline farming activities. The system uses machine learning algorithms such as XGBoost, Random Forest, and ANN to analyze both historical and environmental data to give actionable information. The crop recommendation model interprets the soil and climate conditions, whereas the rainfall prediction model predicts the precipitation. Yield forecasting is a prediction of crop yield, which helps in the effective management of resources. The system has very strong predictive power that can be explained by explainable artificial intelligence methods such as SHAP and LIME, which make the system transparent to the farmers. This will improve decision-making and will be a practice of sustainable agriculture.

Keywords: Crop recommendation, Yield forecasting, Rain Forecasting, StandardScaler, Feature Engineering.

References: 

  1. J. G et al., “Smart solutions for maize farmers: Machine learning-enabled web applications for downy mildew management and enhanced crop yield in India,” European Journal of Agronomy, vol. 164, p. 127441, Dec. 2024, doi: 10.1016/j.eja.2024.127441.
  2. S. S. Basha and B. S. N. Begum, “Weight optimized stacked LSTM with conditional random fields using self-adaptive generalized normal distribution optimizer for crop yield forecasting,” Applied Soft Computing, vol. 184, p. 113760, Aug. 2025, doi: 10.1016/j.asoc.2025.113760.
  3. N. Chaturvedi et al., “Machine learning innovations for accurate crop yield forecasting,” in Elsevier eBooks, 2026, pp. 401–424. doi: 10.1016/b978-0-443-40316-3.00017-5.
  4. T. Jithendra, A. Divya, I. Aljubayri, T. Haritha, and M. Z. Khan, “Two-stage rainfall forecasting and crop classification using puma-optimized ANFIS–SVM,” Smart Agricultural Technology, vol. 13, p. 101719, Dec. 2025, doi: 10.1016/j.atech.2025.101719.
  5. P-Y. Kow et al., “AI-driven weather downscaling for smart agriculture using autoencoders and transformers,” Computers and Electronics in Agriculture, vol. 232, p. 110129, Feb. 2025, doi: 10.1016/j.compag.2025.110129.
  6. A. Chittaragi, B. Patil, M. K. P. Kumar, and P. Devanna, “Hybrid random forest– artificial neural network model based forecasting of anthracnose in bottle gourd across different transplanting windows,” Smart Agricultural Technology, vol. 12, p. 101477, Sep. 2025, doi: 10.1016/j.atech.2025.101477.
  7. S. Beheshti, H. Raheli, S. Zarifian, and A. A. Barati, “Agricultural transition on the brink of crisis: Forecasting development trends in the eastern Urmia Lake Basin,” Results in Engineering, p. 109841, Feb. 2026, doi: 10.1016/j.rineng.2026.109841.
  8. B. V. Kumar and P. V. G. K. Rao, “An effective hybrid attention model for crop yield prediction using IoT-based three-phase prediction with an improved sailfish optimizer,” Expert Systems With Applications, vol. 255, p. 124740, Jul. 2024, doi: 10.1016/j.eswa.2024.124740.
  9. A. Yaganteeswarudu, S. K. Biswas, V. Aruna, and D. Tripathi, “Enhancing Transparency in Smart Farming: Local explanations for crop recommendations using LIME,” Procedia Computer Science, vol. 258, pp. 1993–2005, Jan. 2025, doi: 10.1016/j.procs.2025.04.450.
  10. S. Bam, A. E. Ezugwu, and M. O. Olusanya, “Climate-smart prediction of agricultural pathogens using deep learning,” Smart Agricultural Technology, vol. 12, p. 101464, Sep. 2025, doi: 10.1016/j.atech.2025.101464.
  11. W. Wang and Q. Li, “Smart farming revolution: Leveraging machine learning for sustainable agriculture,” Journal of Cleaner Production, vol. 527, p. 146434, Sep. 2025, doi: 10.1016/j.jclepro.2025.146434.
  12. O. Prabhu, S. Samanth, and K. Chadaga, “A unified model of explainable regression and AES-based data security for soybean yield prediction,” Smart Agricultural Technology, vol. 12, p. 101506, Oct. 2025, doi: 10.1016/j.atech.2025.101506.
Download PDF

This article is part of Volume 2, Issue 3 (March 2026)