Research Cell: An International Journal of Engineering Sciences

Open Access Open Access  Restricted Access Subscription Access

A Review on Crop Yield Prediction Using Learning Techniques


Affiliations
1 Research Scholar, Department of Computer Science, GNDU, Amritsar, India
2 Associate Professor, Department of Computer Science, GNDU, Amritsar, India
3 Assistant Professor, Department of Computer Engineering and Technology, GNDU, Amritsar, India
 

Food is regarded as a basic human requirement that is met through agriculture. Beyond meeting fundamental human requirements, agriculture is seen as a global source of employment in developing countries like India. Sustainable crop production is a persistent issue for farmers. Getting the best crop production has always been difficult for farmers since environmental circumstances are always changing. Land types, resource availability, and weather variability are the main causes of unpredictable crop yields. Therefore, scientists from all around the world are working to develop methods that can efficiently and accurately predict crop yields well in advance so that farmers may take the necessary steps to address upcoming issues. Crop production depends entirely on timely observation and advice. Farmers can reduce their losses if appropriate recommendations and information about the crop is provided. Machine learning is the prevailing technology that helps farmer to minimize agriculture losses. The study's primary goal is to explore different learning techniques used to predict crop yield. Reviews carried out in agriculture sector indicated a strong preference for deep learning methods and hybrid models for crop yield prediction.

Keywords

Machine Learning, Deep Learning, Crop Yield, Prediction.
User
Notifications
Font Size

  • “Agriculture in India: Industry Overview, Market Size, Role in Development...| IBEF.” https://www.ibef.org/industry/agriculture-india (accessed Oct. 05, 2022).

  • M. K. Dharani, R. Thamilselvan, P. Natesan, P. C. D. Kalaivaani, and S. Santhoshkumar, “Review on Crop Prediction Using Deep Learning Techniques,” J. Phys. Conf. Ser., vol. 1767, no. 1, 2021, doi: 10.1088/1742-6596/1767/1/012026.

  • N. Bali and A. Singla, “Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India,” Appl. Artif. Intell., vol. 35, no. 15, pp. 1304–1328, 2021, doi: 10.1080/08839514.2021.1976091.

  • B. Sharma, J. K. P. S. Yadav, and S. Yadav, “Predict Crop Production in India Using Machine Learning Technique: A Survey,” ICRITO 2020 - IEEE 8th Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir., no. 978, pp. 993–997, 2020, doi: 10.1109/ICRITO48877.2020.9197953.

  • B. M. Nayana, K. R. Kumar, and C. Chesneau, “Wheat Yield Prediction in India Using Principal Component Analysis-Multivariate Adaptive Regression Splines (PCA-MARS),” AgriEngineering, vol. 4, no. 2, pp. 461–474, 2022, doi: 10.3390/agriengineering4020030.

  • B. E and G. A, “Rice Crop Yield Prediction Using Random Forest and Deep Neural Network - An Integrated Approach,” SSRN Electron. J., no. Icsmdi, 2021, doi: 10.2139/ssrn.3852547.

  • N. Bali and A. Singla, “Emerging Trends in Machine Learning to Predict Crop Yield and Study Its Influential Factors: A Survey,” Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 95–112, 2022, doi: 10.1007/s11831-021-09569-8.

  • D. Elavarasan and P. M. Durairaj Vincent, “Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications,” IEEE Access, vol. 8, pp. 86886–86901, 2020, doi: 10.1109/ACCESS.2020.2992480.

  • J. Pant, R. P. Pant, M. Kumar Singh, D. Pratap Singh, and H. Pant, “Analysis of agricultural crop yield prediction using statistical techniques of machine learning,” Mater. Today Proc., vol. 46, no. xxxx, pp. 10922–10926, 2021, doi: 10.1016/j.matpr.2021.01.948.

  • M. K. D. P, N. Malyadri, M. S. Srikanth, and A. B. J, “recommendation model for Crop and Fertilizer,” vol. 8, no. 5, pp. 10531–10539, 2021.

  • A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, pp. 4843–4873, 2021, doi: 10.1109/ACCESS.2020.3048415.

  • S. Vaishnavi, M. Shobana, R. Sabitha, and S. Karthik, “Agricultural Crop Recommendations based on Productivity and Season,” 2021 7th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2021, pp. 883–886, 2021, doi: 10.1109/ICACCS51430.2021.9441736.

  • A. H. Tidake, Y. K. Sharma, and V. S. Deshpande, “Design Efficient Model to Increase Crop Yield Using Deep Learning,” Proceeding - 1st Int. Conf. Innov. Trends Adv. Eng. Technol. ICITAET 2019, no. Dl, pp. 221–226, 2019, doi: 10.1109/ICITAET47105.2019.9170227.

  • S. M. Bharath, S. Manoj, P. Adhappa, P. L. Patagar, and R. Bhaskar, “Crop Yield Prediction with Efficient Use of Fertilizers,” Lect. Notes Electr. Eng., vol. 783, pp. 937–943, 2022, doi: 10.1007/978-981-16-3690-5_87.

  • U. Shruthi, V. Nagaveni, and B. K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection,” 2019 5th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2019, pp. 281–284, Mar. 2019, doi: 10.1109/ICACCS.2019.8728415.

  • S. Agarwal and S. Tarar, “A hybrid approach for crop yield prediction using machine learning and deep learning algorithms,” J. Phys. Conf. Ser., vol. 1714, no. 1, 2021, doi: 10.1088/1742-6596/1714/1/012012.

  • P. Malik, S. Sengupta, and J. S. Jadon, “Comparative Analysis of Soil Properties to Predict Fertility and Crop Yield using Machine Learning Algorithms,” Proc. Conflu. 2021 11th Int. Conf. Cloud Comput. Data Sci. Eng., pp. 1004–1007, 2021, doi: 10.1109/Confluence51648.2021.9377147.

  • R. Kumar, M. P. Singh, P. Kumar, and J. P. Singh, “(Paper 7 Tafur),” no. May, pp. 138–145, 2015.

  • S. Godara and D. Toshniwal, “Deep Learning-based query-count forecasting using farmers’ helpline data,” Comput. Electron. Agric., vol. 196, no. February, 2022, doi: 10.1016/j.compag.2022.106875.

  • V. Meshram, K. Patil, V. Meshram, D. Hanchate, and S. D. Ramkteke, “Machine learning in agriculture domain: A state-of-art survey,” Artif. Intell. Life Sci., vol. 1, no. September, p. 100010, 2021, doi: 10.1016/j.ailsci.2021.100010.

  • S. S. Kale and P. S. Patil, “A Machine Learning Approach to Predict Crop Yield and Success Rate,” 2019 IEEE Pune Sect. Int. Conf. PuneCon 2019, pp. 1–5, 2019, doi: 10.1109/PuneCon46936.2019.9105741.

  • D. J. Reddy and M. R. Kumar, “Crop yield prediction using machine learning algorithm,” Proc. - 5th Int. Conf. Intell. Comput. Control Syst. ICICCS 2021, pp. 1466–1470, May 2021, doi: 10.1109/ICICCS51141.2021.9432236.

  • S. M. Pande, P. K. Ramesh, A. Anmol, B. R. Aishwarya, K. Rohilla, and K. Shaurya, “Crop Recommender System Using Machine Learning Approach,” Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, pp. 1066–1071, Apr. 2021, doi: 10.1109/ICCMC51019.2021.9418351.

  • S. Sharma, S. Rai, and N. C. Krishnan, “Wheat Crop Yield Prediction Using Deep LSTM Model,” 2020, [Online]. Available: http://arxiv.org/abs/2011.01498.

  • S. Lingwal, K. K. Bhatia, and M. Singh, “A novel machine learning approach for rice yield estimation,” Journal of Experimental and Theoretical Artificial Intelligence. 2022, doi: 10.1080/0952813X.2022.2062458.

  • P. S. Nishant, P. Sai Venkat, B. L. Avinash, and B. Jabber, “Crop yield prediction based on indian agriculture using machine learning,” 2020 Int. Conf. Emerg. Technol. INCET 2020, no. October, 2020, doi: 10.1109/INCET49848.2020.9154036.


Abstract Views: 123

PDF Views: 0




  • A Review on Crop Yield Prediction Using Learning Techniques

Abstract Views: 123  |  PDF Views: 0

Authors

Sandeep Kaur
Research Scholar, Department of Computer Science, GNDU, Amritsar, India
Gurvinder Singh
Associate Professor, Department of Computer Science, GNDU, Amritsar, India
Anil Kumar
Assistant Professor, Department of Computer Engineering and Technology, GNDU, Amritsar, India

Abstract


Food is regarded as a basic human requirement that is met through agriculture. Beyond meeting fundamental human requirements, agriculture is seen as a global source of employment in developing countries like India. Sustainable crop production is a persistent issue for farmers. Getting the best crop production has always been difficult for farmers since environmental circumstances are always changing. Land types, resource availability, and weather variability are the main causes of unpredictable crop yields. Therefore, scientists from all around the world are working to develop methods that can efficiently and accurately predict crop yields well in advance so that farmers may take the necessary steps to address upcoming issues. Crop production depends entirely on timely observation and advice. Farmers can reduce their losses if appropriate recommendations and information about the crop is provided. Machine learning is the prevailing technology that helps farmer to minimize agriculture losses. The study's primary goal is to explore different learning techniques used to predict crop yield. Reviews carried out in agriculture sector indicated a strong preference for deep learning methods and hybrid models for crop yield prediction.

Keywords


Machine Learning, Deep Learning, Crop Yield, Prediction.

References