An Efficient Crop Yield Prediction System Using Machine Learning




Farming, Regression, Crop Prediction, Mean Absolute Error, MAE, Root mean square Error, RMSE, R2 Score


Farming is considered the biggest factor in strengthening the economy of any country. It also has significant effects on GDP growth. However, due to a lack of information and consultation, farmers suffer from significant crop losses every year. Typically, farmers consult agricultural officers for detecting crop diseases. However, the accuracy of predictions made by agricultural officers based on their experience is not always reliable. If the exact issues are not identified at right time then it results in a heavy crop loss. To address this issue, Computational Intelligence, also known as Machine Learning, can be applied based on historical data. In this study, an intelligent crop yield prediction algorithm is developed using various types of regression-based algorithms. The Crop Yield Prediction Dataset from the Kaggle repository is used for model training and evaluation. Among all different regression methods Random Forest has shown the better performance in terms of R2 score and other errors.


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Nigam, A., Garg, S., Agrawal, A., & Agrawal, P. (2019). Crop Yield Prediction Using Machine Learning Algorithms. 2019 Fifth International Conference on Image Information Processing (ICIIP). DOI:

Champaneri, M., Chachpara, D., Chandvidkar, C., & Rathod, M. (2016). Crop yield prediction using machine learning. Technology, 9, 38.

Abbas, F., Afzaal, H., Farooque, A. A., & Tang, S. (2020). Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms. Agronomy, 10(7), 1046. DOI:

Sellam, V., & Poovammal, E. (2016). Prediction of crop yield using regression analysis. Indian Journal of Science and Technology, 9(38), 1-5. DOI:

P. S. Nishant, P. Sai Venkat, B. L. Avinash and B. Jabber, " Crop Yield Prediction based on Indian Agriculture using Machine Learning," 2020 International Confer-ence for Emerging Technology (INCET), Belgaum, India, 2020, (pp. 1-4). DOI:

Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. DOI:

Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9. DOI:

Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21 DOI:

Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023. DOI:

Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. DOI:

Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 DOI:

Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. DOI:

Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69. DOI:

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. DOI:

Swain D, Mehta U, Bhatt A, et al. A robust chronic kidney disease classifier using machine learning. Electronics 2023; 12(1): 212. doi: 10.3390/electronics12010212 DOI:

H. W. Herwanto, A. N. Handayani, A. P. Wibawa, K. L. Chandrika and K. Arai, "Comparison of Min-Max, Z-Score and Decimal Scaling Normalization for Zoning Feature Extraction on Javanese Character Recognition," 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Malang, Indonesia, 2021, pp. 1-3, doi: 10.1109/ICEEIE52663.2021.9616665. DOI:

A. Lakshmanarao, M. N. Kumar, K. S. V. Ratnakar and Y. Satwika, "Crop Yield Prediction using Regression Models in Machine Learning," 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2023, pp. 423-426, doi: 10.1109/ICAAIC56838.2023. 10141462. DOI:

J. He, L. Ding, L. Jiang and L. Ma, "Kernel ridge regression classification," 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 2014, pp. 2263-2267, doi: 10.1109/IJCNN.2014.6889396. DOI:

R. Muthukrishnan and R. Rohini, "LASSO: A feature selection technique in predictive modeling for machine learning," 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, India, 2016, pp. 18-20, doi: 10.1109/ICACA.2016.7887916. DOI:

P. Dong, H. Peng, X. Cheng, Y. Xing, X. Zhou and D. Huang, "A Random Forest Regression Model for Predicting Residual Stresses and Cutting Forces Introduced by Turning IN718 Alloy," 2019 IEEE International Conference on Computation, Communication and Engineering (ICCCE), Fujian, China, 2019, pp. 5-8, doi: 10.1109/ICCCE48422.2019.9010767. DOI:

M. Atanasovski, M. Kostov, B. Arapinoski and M. Spirovski, "K-Nearest Neighbor Regression for Forecasting Electricity Demand," 2020 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Niš, Serbia, 2020, pp. 110-113, doi: 10.1109/ICEST49890.2020.9232768. DOI:

Kumar, S., Neware, N., Jain, A., Swain, D., Singh, P. (2020). Automatic Helmet Detection in Real-Time and Surveillance Video., Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. DOI:

Swain, Drdebabrata & Satapathy, Santosh & Acharya, Biswaranjan & Shukla, Madhu & Gerogiannis, Vassilis & Kanavos, Andreas & Giakovis, Dimitris. (2022). Deep Learning Models for Yoga Pose Monitoring. Algorithms. 15. 403. 10.3390/a15110403. DOI:

E. Brilliandy, H. Lucky, A. Hartanto, D. Suhartono and M. Nurzaki, "Using Regression to Predict Number of Tourism in Indonesia based of Global COVID-19 Cases," 2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS), IPOH, Malaysia, 2022, pp. 310-315, doi: 10.1109/AiDAS56890.2022.9918731. DOI:

S. A. Septianingrum, M. Alfian Dzikri, M. A. Soeleman, P. Pujiono and M. Muslih, "Performance Analysis of Multiple Linear Regression and Random Forest for an Estimate of the Price of a House," 2022 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, 2022, pp. 415-418, doi: 10.1109/iSemantic55962.2022.9920454. DOI:

J. Qi, J. Du, S. M. Siniscalchi, X. Ma and C. -H. Lee, "Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression," in IEEE Transactions on Signal Processing, vol. 68, pp. 3411-3422, 2020, doi: 10.1109/TSP.2020.2993164. DOI:




How to Cite

D. Swain, S. Lakum, S. Patel, P. Patro, and Jatin, “An Efficient Crop Yield Prediction System Using Machine Learning”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.