Comparison of machine learning classification algorithms based on weather variables and seed characteristics for the selection of paddy seed


  • DHINAKARAN SAKTHIPRIYA Department of Applied Mathematics & Computational Science, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu
  • CHANDRAKUMAR THANGAVEL Department of Applied Mathematics & Computational Science, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu



Paddy seed, K-nearest neighbour (KNN), Decision tree (DT), Naive bayes (NB), Support vector machine (SVM), Logistic regression (LR)


Selection of seed is very crucial for the farmers before the start of the crop season. In this study therefore, an attempt has been made to compare various machine learning (ML) classification techniques for paddy seed forecast for cultivation in three major paddy producing taluk of Madurai district, Tamil Nadu viz Thirumangalam, Peraiyur, and Usilampatti. Five machine learning classification techniques viz. K-nearest neighbour (KNN), decision tree (DT), naive bayes (NB), support vector machine (SVM), and logistic regression (LR) used in this study were compared based on weather data and seed characteristics for the better predictions of a paddy seed. Various measures were used to evaluate the algorithms, including F1-score, accuracy, precision, and recall. The findings indicated that the KNN (K-Nearest Neighbour) gave a better accuracy, precision, recall, and F1-score values of about 0.99, 0.94, 1.0, and 0.96 correspondingly.  It gave the best result of the paddy seed selection which may be helpful for the farming community in getting higher yield and profit.


Cai, W., Wei, R., Xu, L. and Ding, X. (2022). A method for modelling greenhouse temperature using gradient boost decision tree. Inform. Proc. Agric., 9(3): 343-354.

Cedric, L. S., Adoni, W. Y. H., Aworka, R., Zoueu, J. T., Mutombo, F. K., Krichen, M. and Kimpolo, C. L. M. (2022). Crops yield prediction based on machine learning models: Case of West African countries. Smart Agric. Techn., 2: 100049.

Dang, C., Liu, Y., Yue, H., Qian, J. and Zhu, R. (2021). Autumn crop yield prediction using data-driven approaches: support vector machines, random forest, and deep neural network methods. Canadian J. Rem. Sens., 47(2): 162-181.

Das, B., Nair, B., Reddy, V.K. and Venkatesh, P., (2018). Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India. Intern. J. Biometeorol., 62(10): 1809-1822.

Elbasi, E., Zaki, C., Topcu, A. E., Abdelbaki, W., Zreikat, A. I., Cina, E. and Saker, L. (2023). Crop prediction model using machine learning algorithms. Appl. Sci., 13(16): 9288.

Fei, S., Li, L., Han, Z., Chen, Z. and Xiao, Y. (2022). Combining novel feature selection strategy and hyperspectral vegetation indices to predict crop yield. Plant Methods. 18(1): 119.

Gopal, P. M. and Bhargavi, R. (2019). Optimum feature subset for optimizing crop yield prediction using filter and wrapper approaches. Appl. Eng. Agric., 35(1): 9-14.

Gupta, S., Geetha, A., Sankaran, K. S., Zamani, A. S., Ritonga, M., Raj, R. and Mohammed, H. S. (2022). Machine learning-and feature selection-enabled framework for accurate crop yield prediction. J. Food Quality, 1-7.

Ju, S., Lim, H., Ma, J. W., Kim, S., Lee, K., Zhao, S. and Heo, J. (2021). Optimal county-level crop yield prediction using MODIS-based variables and weather data: A comparative study on machine learning models. Agric. Forest Meteorol., 307: 108530.

Kavita, M., and Mathur, P. (2020). Crop yield estimation in India using machine learning. In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) (220-224). IEEE.

Khan, M. S., Nath, T. D., Hossain, M. M., Mukherjee, A., Hasnath, H. B., Meem, T. M. and Khan, U. (2023). Comparison of multiclass classification techniques using dry bean dataset. International J. Cogn. Comp. Eng., 4: 6-20.

Khan, Y., Kumar, V., Setiya, P. and Satpathi, A. (2023). Comparison of phenological weather indices based statistical, machine learning and hybrid models for soybean yield forecasting in Uttarakhand. J. Agrometeorol., 25(3): 425-431.

Nain, G., Bhardwaj, N., Jaslam, P. M. and Dagar, C. S. (2021). Rice yield forecasting using agro-meteorological variables: A multivariate approach. J. Agrometeorol., 23(1): 100-105.

Oktoviany, P., Knobloch, R. and Korn, R. (2021). A machine learning-based price state prediction model for agricultural commodities using external factors. Decisions Econ. Fin., 44(2): 1063-1085.

Pallathadka, H., Mustafa, M., Sanchez, D. T., Sajja, G. S., Gour, S. and Naved, M. (2023). Impact of machine learning on management, healthcare and agriculture. Materials Today: Proc., 80: 2803-2806.

Setiya, P. and Nain, A.S., (2021). Development of yield prediction model of rice crop for hilly and plain terrains of Uttarakhand. J. Agrometeorol., 23(4): 452-456.

Sridhara, S., Manoj, K.N., Gopakkali, P., Kashyap, G.R., Das, B., Singh, K.K. and Srivastava, A.K. (2023). Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India. Intern. J. Biometeorol., 67(1):165-180.

Suruliandi, A., Mariammal, G. and Raja, S. P. (2021). Crop prediction based on soil and environmental characteristics using feature selection techniques. Math. Comp. Mod. Dynam. Syst., 27(1): 117-140.

Vikram, R., Divij, R., Hishore, N., Naveen, G., and Rudhramoorthy, D. (2021, April). Crop price prediction using machine learning naive Bayes algorithms. In International Conference on Ubiquitous Computing and Intelligent Information Systems (27-34). Singapore: Springer Nature Singapore.

Yudianto, M. R. A., Agustin, T., James, R. M., Rahma, F. I., Rahim, A., and Utami, E. (2021). Rainfall forecasting to recommend crops varieties using moving average and naive bayes methods. Intern. J. Modern Edu. Computer Sci., 13(3): 23-33.




How to Cite

SAKTHIPRIYA , D., & THANGAVEL, C. (2024). Comparison of machine learning classification algorithms based on weather variables and seed characteristics for the selection of paddy seed. Journal of Agrometeorology, 26(2), 209–214.



Research Paper