Making Crop Recommendations using Machine Learning Techniques

P N S S V Charishma; S. Rupa Lakshmi; M Vijaya Durga

doi:10.69996/jcai.2024006

Authors

P N S S V Charishma Department of CSE, VSM College of Engineering, Ramachandrapuram, Andhra Pradesh, 533255, India Author
S. Rupa Lakshmi Department of CSE, VSM College of Engineering, Ramachandrapuram, Andhra Pradesh, 533255, India Author
M Vijaya Durga Department of CSE, VSM College of Engineering, Ramachandrapuram, Andhra Pradesh, 533255, India Author

DOI:

https://doi.org/10.69996/jcai.2024006

Keywords:

Machine Learning, Crop quality, SVM, NB, KNN

Abstract

The agricultural crop recommendation system relies to multiple contain characteristics. This study presents a hybrid model for suggesting suitable crops for the southern states of India. The model takes into account multiple factors including Season, temperature, aquifer level, downpours, soil type, fertilisers, and pesticides. A model for recommenders is constructed functioning as a combination of the two utilising machine learning’s classifier method. After you input your crop specifications, the algorithm will give you advice on what to grow. Agricultural crop recommendation systems that utilise technology assist farmers in enhancing agricultural productivity by suggesting appropriate crops based on geographical and meteorological elements. A hybrid recommender model might be developed has been proven to be efficient in suggesting an appropriate crop. In order to better manage agricultural output and inform farmers of shifts in crop market prices, it is highly practical to update crop yield production values. The objective of this work is to apply the crop selection approach in order to address various agricultural challenges and issues faced by farmers. Maximising the crop yield rate enhances the Indian economy. Crop quality is assessed by a rating algorithm. This method also enables the identification of the rates of low- and high-quality crops. The use of an ensemble of classifiers allows for better predictive decisionmaking by deploying a large number of classifiers. Additionally, a ranking procedure is utilised to make decisions and choose the outcomes of the classifiers. This approach is utilised to forecast the expenditure associated with the crop that is produced for subsequent purposes.

References

[1] N. Abdulla, M. Demirci and S. Ozdemir, “Design and evaluation of adaptive deep learning models for weather forecasting,” Eng. Appl. Artif. Intell, vol. 116, no. 105440, 2022.

[2] A. Haridasan, J. Thomas and E. D. Raj, “Deep learning system for paddy plant disease detection and classification,” Environ. Monit. Assess, vol. 195, no. 120, 2023.

[3] G. Sambasivam and G. D. Opiyo, “A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks,” Egypt. Inform J, vol. 22, pp. 27–34, 2021.

[4] R. Manikandan, G. Ranganathan and V. Bindhu, “Deep learning based IoT module for smart farming in different environmental conditions,” Wirel. Pers. Commun, vol. 128, pp.1715–1732, 2023.

[5] A. Gumuscu, M. E. Tenekeci and A. V. Bilgili, “Estimation of wheat planting date using machine learning algorithms based on available climate data,” Sustain. Comput. Inform. Syst, vol. 28, no. 100308, 2020.

[6] A. Chlingaryan, S. Sukkarieh and B. Whelan, “Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review,” Comput. Electron. Agric. vol. 151, pp. 61–69, 2018.

[7] Jignasha, M. Jethva, Nikhil Gondaliya and Vinita Shah, “A Review on Data Mining Techniques for Fertilizer Recommendation International,” Journal of Scientific Research in Computer Science Engineering and Information Technology, vol. 2, no. 6, 2017.

[8] M. C. S. Geetha, “A Survey on Data Mining Techniques in Agriculture,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, pp. 887-892, 2015.

[9] S. Srija, R. Geetha Chanda, S. Lavanya and Dr. M. Kalpana, “AgroNutri Android Application,” International Journal of Advanced Research in Computer and Communication Engineering, vol.5, no. 5, 2016.

[10] Karandeep Kaur, “Machine Learning: Applications in Indian Agriculture,” International Journal of Advanced Research in Science, Communication and Technology, vol. 3, no.1, 2023.

[11] A. Uwe, Schneider, Petr Havlik, Erwin Schmid, Hugo Valin, Aline Mosnier et al., “Impacts of population growth, economic development, and technical change on global food production and consumption,” Agricultural Systems, vol. 104, no. 2, pp. 204-215,2011.

[12] W. James Jones, M. John Antle, O. Bruno Basso, J. Kenneth Boote, T. Richard Conant et al., “Brief history of agricultural systems modeling,” Agricultural Systems, vol. 155, pp. 240-254, 2017.

[13] S. Rajeswari, K. Suthendran and K. Rajakumar, "A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics," 2017 International Conference on Intelligent Computing and Control (I2C2), Coimbatore, India, pp. 1-5, 2017.

[14] O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow and M. N. Hindia, "An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges," in IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3758-3773, 2018.

[15] X. Sun, J. Zhang, C. Wang and T. Zhang, "Circulation mode selection based on cost analysis," 2017 4th International Conference on Industrial Economics System and Industrial Security Engineering (IEIS), Kyoto, Japan, pp. 1-4, 2017.

[16] Zongkai Shao, "Support vector machine-based fuzzy self-learning control for induction machines," 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, Chengdu, China, pp. 12-16, 2010.

[17] M. M. Rahman, N. Haq and R. M. Rahman, "Machine Learning Facilitated Rice Prediction in Bangladesh," 2014 Annual Global Online Conference on Information and Computer Technology, Louisville, KY, USA, pp. 1-4, 2014.