Comparative Analysis on Crop Yield Forecasting using Machine Learning Techniques

Sharma, Shubham; Walia, Gurleen Kaur; Singh, Kanwalpreet; Batra, Vanshika; Sekhon, Amandeep Kaur; Kumar, Aniket; Rawal, Kirti; Ghai, Deepika

doi:10.2478/plua-2024-0015

Abstract

Global overpopulation necessitates increased crop yields, yet available arable land is limited. The study compares and evaluates the performance of three machine learning algorithms—Random Forest (RF), Extra Trees (ET), and Artificial Neural Network (ANN)—in crop yield prediction. Using 28,242 samples with seven features from 101 countries, we evaluated these models based on Mean Absolute Error (MAE), R-squared (R^2), and Mean Squared Error (MSE). The ET regression model demonstrated superior performance, achieving an MAE of 5249.03, the lowest among the models tested. Despite having the highest R^2 value of 0.9873, the ANN exhibited higher MAE and MSE values, indicating less reliability. The RF model showed intermediate results. With a prediction accuracy of 97.5%, the ET model proved to be the most effective for crop yield prediction, achieving the highest accuracy reported to date. Future research should explore more advanced algorithms and larger datasets to validate these findings further.

References

n.d. Artificial Neural Network Tutorial. javaTpoint. https://www.javatpoint.com/artificial-neural-network.
Search in Google Scholar Back to article
Batool, D., Shahbaz, M., Shahzad Asif, H., Shaukat, K., Alam, T.M., Hameed, I.A., ... & Luo, S. (2022). A hybrid approach to tea crop yield prediction using simulation models and machine learning. Plants, 11(15), 1925. DOI: 10.3390/plants11151925
Search in Google Scholar Back to article
Bodapati, N., Himavaishnavi, J., Rohitha, V., Jagadeeswari, D.L., & Bhavana, P. (2022). Analyzing crop yield using machine learning. In 2022 International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, (pp. 1-8). IEEE. DOI: 10.1109/ICEARS53579.2022.9752242.
Search in Google Scholar Back to article
Bonthu, H. (2021). Analytics Vidhya. 21 May. https://www.analyticsvidhya.com/blog/2021/05/detecting-and-treating-outliers-treating-the-oddone-out/.
Search in Google Scholar Back to article
Budu, E. (2022). Random Forest Vs. Extremely Randomized Trees. Baeldung. 1 August. https://www.baeldung.com/cs/random-forest-vs-extremely-randomized-trees.
Search in Google Scholar Back to article
Chandraprabha, M., & Dhanaraj, R.K. (2021). Soil based prediction for crop yield using predictive analytics. In 2021 3^rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, (pp. 265-270). IEEE. DOI: 10.1109/ICAC3N53548.2021.9725758.
Search in Google Scholar Back to article
Chaya (2020). Random Forest Regression. Level Up Coding. 9 June. https://levelup.gitconnected.com/random-forest-regression-209c0f354c84.
Search in Google Scholar Back to article
Dahikar, S.S., & Rode, S.V. (2014). Agricultural crop yield prediction using artificial neural network approach. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2(1), 683-686. DOI: 7c68a32212c1f86f535f4c1658ff68399d0a9ddd.
Search in Google Scholar Back to article
Dharwadkar, N.V., Kalmani, V.H., & Thapa, V. (2023). Crop yield prediction using deep learning algorithm based on CNN-LSTM with attention layer and skip connection. Preprint. DOI: 10.21203/rs.3.rs-3118781/v1.
Search in Google Scholar Back to article
Elavarasan, D., & Vincent, P.D.R. (2021). A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. Journal of Ambient Intelligence and Humanized Computing, 12(11), 10009-10022. DOI: 10.1007/s12652-020-02752-y.
Search in Google Scholar Back to article
Feng, P., Wang, B., Li Liu, D., Waters, C., Xiao, D., Shi, L., & Yu, Q. (2020). Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agricultural and Forest Meteorology, 285, 107922. DOI: 10.1016/j.agrformet.2020.107922.
Search in Google Scholar Back to article
Fritz, S., See, L., Bayas, J. C. L., Waldner, F., Jacques, D. C., Becker-Reshef, I., ... & McCallum, I. (2019). A comparison of global agricultural monitoring systems and current gaps. Agricultural Systems, 168, 258-272. DOI: 10.1016/j.agsy.2018.05.010.
Search in Google Scholar Back to article
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63, 3-42. DOI: 10.1007/s10994-006-6226-1.
Search in Google Scholar Back to article
Ghai, D., Tripathi, S. L., Saxena, S., Chanda, M., & Alazab, M. (Eds.). (2022). Machine Learning Algorithms for Signal and Image Processing. John Wiley & Sons. DOI: 10.1002/9781119861850.
Search in Google Scholar Back to article
Gopal, P.M., & Bhargavi, R. (2019). A novel approach for efficient crop yield prediction. Computers and Electronics in Agriculture, 165, 104968. DOI: 10.1016/j.compag.2019.104968.
Search in Google Scholar Back to article
Huilgol, P. (2020). Feature transformation and scaling techniques to boost your model performance. https://www.analyticsvidhya.com/blog/2020/07/types-of-feature-transformation-and-scaling/.
Search in Google Scholar Back to article
Iniyan, S., Varma, V.A., & Naidu, C.T. (2023). Crop yield prediction using machine learning techniques. Advances in Engineering Software, 175, 103326. DOI: 10.1016/j.advengsoft.2022.103326.
Search in Google Scholar Back to article
Jeong, J.H., Resop, J.P., Mueller, N.D., Fleisher, D.H., Yun, K., Butler, E.E., ... & Kim, S.H. (2016). Random forests for global and regional crop yield predictions. PloS one, 11(6), e0156571. DOI: 10.1371/journal.pone.0156571.
Search in Google Scholar Back to article
Jhajharia, K., Mathur, P., Jain, S., & Nijhawan, S. (2023). Crop yield prediction using machine learning and deep learning techniques. Procedia Computer Science, 218, 406-417. DOI: 10.1016/j.procs.2023.01.023.
Search in Google Scholar Back to article
Kanyutu, K. (2023). Exploratory Data Analysis Ultimate Guide. DEV Community. 26 February. https://dev.to/kim_kanyutu/exploratory-data-analysis-ultimate-guide-35e1.
Search in Google Scholar Back to article
Khaki, S., & Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science, 10, 452963. DOI: 10.3389/fpls.2019.00621.
Search in Google Scholar Back to article
Khaki, S., Wang, L., & Archontoulis, S.V. (2020). A CNN-RNN framework for crop yield prediction. Frontiers in Plant Science, 10, 492736. DOI: 10.3389/fpls.2019.01750.
Search in Google Scholar Back to article
Kim, N., Ha, K.J., Park, N.W., Cho, J., Hong, S., & Lee, Y.W. (2019). A comparison between major artificial intelligence models for crop yield prediction: Case study of the midwestern United States, 2006–2015. ISPRS International Journal of Geo-Information, 8(5), 240. DOI: 10.3390/ijgi8050240.
Search in Google Scholar Back to article
Kumar, Y.J.N., Spandana, V., Vaishnavi, V.S., Neha, K., & Devi, V.G. R. R. (2020). Supervised machine learning approach for crop yield prediction in agriculture sector. In 2020 5^th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, (pp. 736-741). IEEE. DOI: 10.1109/ICCES48766.2020.9137868.
Search in Google Scholar Back to article
Kuwata, K., & Shibasaki, R. (2016). Estimating corn yield in the United States with MODIS EVI and machine learning methods. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 131-136. DOI: 10.5194/isprsannals-III-8-131-2016.
Search in Google Scholar Back to article
Mishra, S., Mishra, D., & Santra, G. H. (2016). Applications of machine learning techniques in agricultural crop production: a review paper. Indian Journal of Science and Technology, 9(38), 1-14. DOI: 10.17485/ijst/2016/v9i38/95032.
Search in Google Scholar Back to article
Nigam, A., Garg, S., Agrawal, A., & Agrawal, P. (2019). Crop yield prediction using machine learning algorithms. In 2019 5^th International Conference on Image Information Processing (ICIIP), Shimla, India, (pp. 125-130). IEEE. DOI: 10.1109/ICIIP47207.2019.8985951.
Search in Google Scholar Back to article
Nithya, V., Josephine, M.S., & Jeyabalaraja, V. (2023). IoT-based crop yield prediction system in Indian sub-continent using machine learning techniques. Remote Sensing in Earth Systems Sciences, 6(3), 156-166. DOI: 10.1007/s41976-023-00097-6.
Search in Google Scholar Back to article
Oikonomidis, A., Catal, C., & Kassahun, A. (2022). Hybrid deep learning-based models for crop yield prediction. Applied Artificial Intelligence, 36(1), 2031822. DOI: 10.1080/08839514.2022.2031823.
Search in Google Scholar Back to article
Pandith, V., Kour, H., Singh, S., Manhas, J., & Sharma, V. (2020). Performance evaluation of machine learning techniques for mustard crop yield prediction from soil analysis. Journal of Scientific Research, 64(2), 394-398. DOI: 10.37398/JSR.2020.640254.
Search in Google Scholar Back to article
Patel, K., & Patel, H.B. (2021). A comparative analysis of supervised machine learning algorithm for agriculture crop prediction. In 2021 4^th International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, (pp. 1-5). IEEE. DOI: 10.1109/ICECCT52121.2021.9616731.
Search in Google Scholar Back to article
Patel, R. n.d. Crop Yield Prediction Dataset. Kaggle. https://www.kaggle.com/datasets/patelris/crop-yield-prediction-dataset.
Search in Google Scholar Back to article
Satir, O., & Berberoglu, S. (2016). Crop yield prediction under soil salinity using satellite derived vegetation indices. Field Crops Research, 192, 134-143. DOI: 10.1016/j.fcr.2016.04.028.
Search in Google Scholar Back to article
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. DOI: 10.1016/j.neunet.2014.09.003.
Search in Google Scholar Back to article
n.d. Scikit learn. https://scikitlearn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error.
Search in Google Scholar Back to article
n.d. Scikit Learn. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.
Search in Google Scholar Back to article
n.d. Scikit Learn. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score.
Search in Google Scholar Back to article
Segal, M.R. (2004). Machine Learning Benchmarks and Random Forest Regression. UCSF: Center for Bioinformatics and Molecular Biostatistics. Retrieved from https://escholarship.org/uc/item/35x3v9t4.
Search in Google Scholar Back to article
Seldon. (2021). Machine Learning Regression. 29 October. https://www.seldon.io/machine-learning-regression-explained#:~:text=Regression%20is%20a%20technique%20for,used%20to%20predict%20continuous%20outcomes.
Search in Google Scholar Back to article
Singh, G. (2021). Introduction to Artificial Neural Networks. Analytics Vidhya. 6 September. https://www.analyticsvidhya.com/blog/2021/09/introduction-to-artificial-neural-networks/.
Search in Google Scholar Back to article
Sreerama, A.S., & Sagar, B.M. (2020). A machine learning approach to crop yield prediction. International Research Journal of Engineering and Technology (IRJET) 07 (05): 4, 6616-6619.
Search in Google Scholar Back to article
Suresh, N., Ramesh, N.V.K., Inthiyaz, S., Priya, P.P., Nagasowmika, K., Kumar, K.V.H., ... & Reddy, B.N. K. (2021). Crop yield prediction using random forest algorithm. In 2021 7^th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, (pp. 279-282). IEEE. DOI: 10.1109/ICACCS51430.2021.9441871.
Search in Google Scholar Back to article
Swain, D., Lakum, S., Patel, S., & Patro, P. (2024). An Efficient Crop Yield Prediction System Using Machine Learning. EAI Endorsed Transactions on Internet of Things, 10, 1-5. DOI: 10.4108/eetiot.5333.
Search in Google Scholar Back to article
Thankachan, K. n.d. (2022). What? When? How? ExtraTrees Classifier. Towards Data Science. https://towardsdatascience.com/what-when-how-extratrees-classifier-c939f905851c.
Search in Google Scholar Back to article
Venugopal, A., Aparna, S., Mani, J., Mathew, R., & Williams, V. (2021). Crop yield prediction using machine learning algorithms. International Journal of Engineering Research & Technology (IJERT), 9(13), 87-91. DOI: 10.17577/IJERTCONV9IS13019.
Search in Google Scholar Back to article

Comparative Analysis on Crop Yield Forecasting using Machine Learning Techniques

Abstract

Paradigm

My account