Machine learning for weather-specific crop recommendation
Keywords:
decision tree, random forest, crop yield, soil parameters, crop recommendationAbstract
Agriculture and its related sectors are unquestionably the most important sources of income in rural India. Additionally, having a big impact on the nation's GDP is the agriculture sector. The sector of agriculture is so large, which is great for the nation. The crop production per hectare, however, falls short of international standards. This is one of the most likely causes of the greater rate of suicide among marginal farmers in India. This research proposed best recommendation system. The proposed system recommends crops for farmers to grow based on input from the farmer’s field, such as the temperature, soil, moisture, and nutrient like NPK, pH, and rainfall. Machine learning algorithms allow for optimal crop selection to be made in light of all relevant parameters. three popular machine learning algorithms were tested in this study which includes the Decision Tree, the Random Forest (RF), and the Logistic Regression. The Random Forest among them demonstrated the highest outcomes with 99.32% accuracy.
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A. Kumar, S. Sarkar, and C. Pradhan, “Recommendation system for crop identification and pest control technique in agriculture,” in 2019 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2019, pp. 0185–0189.
A. L. Samuel, “Some studies in machine learning using the game of checkers,” IBM Journal of research and development, vol. 3, no. 3, pp. 210–229, 1959.
A. M. Patokar and V. V. Gohokar, "Automatic Investigation of Micronutrients and fertilizer dispense System using Microcontroller," 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), 2018, pp. 1737-1739, doi: 10.1109/ICRIEECE44171.2018.9008500.
A. Sang and S. K. Vishwakarma, “A ranking based recommender system for cold start & data sparsity problem,” in 2017 Tenth International Conference on Contemporary Computing (IC3). IEEE, 2017, pp. 1–3.
D. A. Bondre and S. Mahagaonkar, “Prediction of crop yield and fertilizer recommendation using machine learning algorithms,” International Journal of Engineering Applied Sciences and Technology, vol. 4, no. 5, pp. 371–376, 2019.
G. Suresh, A. S. Kumar, S. Lekashri, and R. Manikandan, “Efficient crop yield recommendation system using machine learning for digital farming,” International Journal of Modern Agriculture, vol. 10, no. 1, pp. 906–914, 2021.
J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Systems with Applications, vol. 69, pp. 29–39, 2017.
K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, p. 2674, 2018.
K. K. Fletcher, “A method for dealing with data sparsity and cold-start limitations in service recommendation using personalized preferences,” in 2017 IEEE international conference on cognitive computing (ICCC). IEEE, 2017, pp. 72–79.
K. Patel and H. B. Patel, “A state-of-the-art survey on recommendation system and prospective extensions,” Computers and Electronics in Agriculture, vol. 178, p. 105779, 2020.
M. Paul, S. K. Vishwakarma, and A. Verma, “Analysis of soil behaviour and prediction of crop yield using data mining approach,” in 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015, pp. 766–771.
M. Shinde, K. Ekbote, S. Ghorpade, S. Pawar, and S. Mone, “Crop recommendation and fertilizer purchase system,” International Journal of Computer Science and Information Technologies, vol. 7, no. 2, pp.665–667, 2016.
N. Kumbhar and K. Belerao, “Microblogging reviews based crosslingual sentimental classification for cold-start product recommendation,” in 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2017, pp. 1–4.
N. Saranya , A. Mythili, 2020, Classification of Soil and Crop Suggestion using Machine Learning Techniques, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 02 (February 2020),
P. Bandara, T. Weerasooriya, T. Ruchirawya, W. Nanayakkara, M. Dimantha, and M. Pabasara, “Crop recommendation system,” International Journal of Computer Applications, vol. 975, p. 8887.
Patokar, A.M., Gohokar, V.V. (2018). Precision Agriculture System Design Using Wireless Sensor Network. “Information and Communication Technology. Advances in Intelligent Systems and Computing”, vol 625. Springer, Singapore. https://doi.org/10.1007/978-981-10-5508-9_16
R. K. Rajak, A. Pawar, M. Pendke, P. Shinde, S. Rathod, and A. Devare, “Crop recommendation system to maximize crop yield using machine learning technique,” International Research Journal of Engineering and Technology, vol. 4, no. 12, pp. 950–953, 2017.
S. A. Z. Rahman, K. C. Mitra, and S. M. Islam, “Soil classification using machine learning methods and crop suggestion based on soil series,” in 2018 21st International Conference of Computer and Information Technology (ICCIT). IEEE, 2018, pp. 1–4.
S. Babu, “A software model for precision agriculture for small and marginal farmers,” in 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS). IEEE, 2013, pp. 352–355.
S. N. Seo and R. Mendelsohn, “An analysis of crop choice: Adapting to climate change in south american farms,” Ecological economics, vol. 67, no. 1, pp. 109–116, 2008.
Sk Al Zaminur Rahman, S.M. Mohidul Islam, Kaushik Chandra Mitra” Soil Classification using Machine Learning Methods and Crop Suggestion Based on Soil Series” 2018 21st International Conference of Computer and Information Technology (ICCIT), 21-23 December, 2018
Thomas Lisha Varghese Merin Mary Saji Varsha S Kevin Tom Thomas, Er. Jinu. “Crop Prediction Using Machine Learning | International Journal of Future Generation Communication and Networking.” Crop Prediction Using Machine Learning | International Journal of Future Generation Communication and Networking, 1 July 2020, sersc.org/journals/index.php/IJFGCN/article/view/28189.
Zeel Doshi, Subhash Nadkarni, Rashi Agrawal, Prof. Neepa Shah “AgroConsultant: Intelligent Crop Recommendation System Using Machine Learning Algorithms ” 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)
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