Classification and Estimation of Crop Yield Prediction in Karnataka using LSTM with Attention Mechanism

Authors

  • Nandini Geddlehally Renukaradya Department of Information Science and Engineering, Sri Siddhartha Institute of Technology, SSAHE, Tumakuru, and Visvesvaraya Technological University, Belagavi-590018, India
  • Kishore Gopala Rao Department of Information Science and Engineering, Jyothy Institute of Technology, Kanakapura, and Visvesvaraya Technological University, Belagavi-590018, India
  • Anand Babu Jayachandra Department of Information Science and Engineering, Malnad College of Engineering, Hassan, and Visvesvaraya Technological University, Belagavi-590018, India

Keywords:

Attention mechanism, Correlation-based feature selection algorithm, Feature selection, Crop yield prediction, Long short-term memory, Variance inflation factor

Abstract

Agriculture is an important occupation across the world with the dependency on the weather and rainfall. The objective of this paper is an early prediction of crop yield by using the climate, soil, and temperature factors. In this research, the classification-based crop yield prediction is proposed by using the Long Short-Term Memory (LSTM) with Attention Mechanism. The manual data is collected from the Economics and Statistics, Government of Karnataka department. This method utilized the dataset from the Department of Economics and Statistics of three crops named jowar, paddy, and ragi. The linear interpolation method is utilized for filling the missing and null values in the dataset. The feature selection process helps in the Correlation based Feature Selection Algorithm (CBFA) and Variance Inflation Factor Algorithm (VIF) for selecting and removing correlated feature sets. The model performance is evaluated by using Accuracy, R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The proposed LSTM model delivers the results through evaluation metrics such as accuracy, R2, MAE, MSE, and RMSE values about 98.23%, 0.43, 0.131, 0.054 and 0.232 respectively.

Downloads

Download data is not yet available.

References

A. Suruliandi, G. Mariammal, and S. P. Raja, “Crop prediction based on soil and environmental characteristics using feature selection techniques,” Math. Comput. Modell. Dyn. Syst., vol. 27, no. 1, pp. 117140, Jan. 2021.

D. Paudel, H. Boogaard, A. de Wit, M. van der Velde, M. Claverie, L. Nisini, S. Janssen, S. Osinga, and I. N. Athanasiadis, “Machine learning for regional crop yield forecasting in Europe,” Field Crops Res., vol. 276, p. 108377, Feb. 2022.

S. Iniyan and R. Jebakumar, “Mutual information feature selection (MIFS) based crop yield prediction on corn and soybean crops using multilayer stacked ensemble regression (MSER),” Wireless Pers. Commun., vol. 126, no. 3, pp. 19351964, Oct. 2022.

D. Paudel, H. Boogaard, A. de Wit, S. Janssen, S. Osinga, C. Pylianidis, and I. N. Athanasiadis, “Machine learning for large-scale crop yield forecasting,” Agric. Syst., vol. 187, p. 103016, Feb. 2021.

D. Elavarasan and P. M. D. R. Vincent, “Fuzzy deep learning-based crop yield prediction model for sustainable agronomical frameworks,” Neural Comput. Appl., vol. 33, no. 20, pp. 1320513224, Oct. 2021.

F. Abbas, H. Afzaal, A. A. Farooque, and S. Tang, “Crop yield prediction through proximal sensing and machine learning algorithms,” Agronomy, vol. 10, no. 7, p. 1046, Jul. 2020.

S. Fei, M. A. Hassan, Y. Xiao, X. Su, Z. Chen, Q. Cheng, F. Duan, R. Chen, and Y. Ma, “UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat,” Precis. Agric., vol. 24, no. 1, pp. 187212, Feb. 2023.

P. Nevavuori, N. Narra, P. Linna, and T. Lipping, “Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models,” Remote Sens., vol. 12, no. 23, p. 4000, Dec. 2020.

M. P. S. Gopal and R. Bhargavi, “Performance evaluation of best feature subsets for crop yield prediction using machine learning algorithms”, Applied Artificial Intelligence, vol. 33, no. 7, pp. 621642, Jun. 2019.

U. Bhimavarapu, G. Battineni, and N. Chintalapudi, “Improved Optimization Algorithm in LSTM to Predict Crop Yield,” Computers, vol. 12, no. 1, p. 10, Jan. 2023.

S. Gowda and S. Reddy, “Design and implementation of crop yield prediction model in agriculture,” International Journal of Scientific & Technology Research, vol. 8, no. 1, pp. 544549, Jan. 2020.

K. Moraye, A. Pavate, S. Nikam, and S. Thakkar, “Crop Yield Prediction Using Random Forest Algorithm for Major Cities in Maharashtra State,” International Journal of Innovative Research in Computer Science & Technology (IJIRCST), vol. 9, no. 2, pp. 4044, Mar. 2021.

S. S. Kale and P. S. Patil, “A Machine Learning Approach to Predict Crop Yield and Success Rate,” in 2019 IEEE Pune Section International Conference (PuneCon), Pune, India, 2019, pp. 15.

V. Jadhav and Monisha, “Wheat and Jowar Crop Yield Prediction Model using Ensemble Technique,” in Proceedings of the 4th International Conference on Advances in Science & Technology (ICAST2021), 2021.

D. Israni, K. Masalia, T. Khasgiwal, T. Khasgiwal, M. Tolani, and M. Edinburgh, “Crop-Yield Prediction and Crop Recommendation System,” 2022. Available at SSRN 4111856.

P. S. M. Gopal and R. Bhargavi, “A novel approach for efficient crop yield prediction,” Comput. Electron. Agric., vol. 165, p. 104968, Oct. 2019.

S. Shidnal, M. V. Latte, and A. Kapoor, “Crop yield prediction: two-tiered machine learning model approach,” Int. J. Inf. Technol., vol. 13, no. 5, pp. 19831991, Oct. 2021.

A. Gupta and P. Nahar, “Classification and yield prediction in smart agriculture system using IoT,” J. Ambient Intell. Hum. Comput., vol. 14, no. 8, pp. 1023510244, Aug. 2023.

E. Khosla, R. Dharavath, and R. Priya, “Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression,” Environ. Dev. Sustainability, vol. 22, no. 6, pp. 56875708, Aug. 2020.

A. Kaneko, T. Kennedy, L. Mei, C. Sintek, M. Burke, S. Ermon, and D. Lobell, “Deep learning for crop yield prediction in Africa,” in International Conference on Machine Learning AI for Social Good Workshop, Long Beach, United States, 2019.

O. N. Oyelade, A. E. -S. Ezugwu, and H. Chiroma, “CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection,” IEEE Access, vol. 9, pp. 7790577919, 2021.

A. Picornell, J. Oteros, R. Ruiz-Mata, M. Recio, M.M. Trigo, M. Martínez-Bracero, B. Lara, A. Serrano-García, C. Galán, H. García-Mozo, P. Alcázar, R. Pérez-Badia, B. Cabezudo, J. Romero-Morte, and J. Rojo, “Methods for interpolating missing data in aerobiological databases,” Environ. Res., vol. 200, p. 111391, Sep. 2021.

F. Pereira, H. Lopes, P. Maia, B. Meyer, J. Nocon, P. Jouhten, D. Konstantinidis, E. Kafkia, M. Rocha, P. Kötter, and I. Rocha, “Model‐guided development of an evolutionarily stable yeast chassis,” Mol. Syst. Biol., vol. 17, no. 7, p. e10253, Jul. 2021.

Downloads

Published

24.03.2024

How to Cite

Geddlehally Renukaradya, N. ., Rao, K. G. ., & Jayachandra, A. B. . (2024). Classification and Estimation of Crop Yield Prediction in Karnataka using LSTM with Attention Mechanism. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 89–96. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5226

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

License

Copyright (c) 2024 Nandini Geddlehally Renukaradya, Kishore Gopala Rao, Anand Babu Jayachandra

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.