ITM Web Conf.
Volume 44, 2022International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|Number of page(s)||6|
|Published online||05 May 2022|
- Kevin Tom Thomas, Varsha, S., Merin Mary Saji, Lisha Varghese and Jinu Er. Thoma, “Crop Prediction Using Machine Learning”, International Journal of Future Generation Communication and Networking, Vol. 13, Issue: 3, pp. 1896–1901, 2020 [Google Scholar]
- Mahendra, N., Dhanush Vishwakarma, Nischitha, K., Ashwini, and Manjuraju M.R. “Crop Prediction using Machine Learning Approaches”, INTERNATIONAL JOURNALOF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 9, Issue 8 (August 2020). [Google Scholar]
- Mansi Shinde, Kimaya Ekbote, Sonali Ghorpade, Sanket Pawar and Shubhada Mone, “Crop Recommendation and Fertilizer Purchase System”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 7 (2), 665–667, 2016. [Google Scholar]
- Kiran Shinde, Jerrin Andrei and Amey Oke, “WebBased Recommendation System for Farmers”, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169, Volume: 3 Issue: 3 1444–1448. [Google Scholar]
- P.A.S. Chakraborty, A. Kumar and O.R. Pooniwala, “Intelligent Crop Recommendation System using Machine Learning,” 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 843–848, doi: 10.1109/ICCMC51019.2021.9418375, 2021. [Google Scholar]
- S. Jain and D. Ramesh, “Machine Learning convergence for weather based crop selection,” IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–6, doi: 10.1109/SCEECS48394.2020.75, 2020. [Google Scholar]
- Suresh, P. Ganesh Kumar and M. Ramalatha, “Prediction of major crop yields of Tamilnadu using K-means and Modified KNN,” 3rd International Conference on Communication and Electronics Systems (ICCES), pp. 88–93, doi: 10.1109/CESYS.2018.8723956, 2018. [Google Scholar]
- S. Pudumalar, E. Ramanujam, R. H. Rajashree, C. Kavya, T. Kiruthika and J. Nisha, “Crop recommendation system for precision agriculture,” Eighth International conference on Advanced Computing (ICoAC), pp. 32–36, doi: 10.1109/ICoAC.2017.7951740, 2017. [CrossRef] [Google Scholar]
- R. Kumar, M. P. Singh, P. Kumar and J. P. Singh, “Crop Selection Method to maximize crop yield rate using machine learning technique,” International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), pp. 138–145, doi: 10.1109/ICSTM.2015.7225403, 2015. [Google Scholar]
- S.M. Pande, P.K. Ramesh, A. Anmol, B.R. Aishwarya, K. Rohilla and K. Shaurya, “Crop Recommender System Using Machine Learning Approach,” 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1066–1071, doi: 10.1109/ICCMC51019.2021.9418351, 2021 [Google Scholar]
- Raj, Angu & Balashanmugam, ThiyaneswaranDr. & Jayanthi, J. & Yoganathan, N. & Srinivasan, PCrop. . Recommendation on Analyzing Soil Using Machine Learning. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 12. 1784–1791, 2021. [CrossRef] [Google Scholar]
- T. Ragunthar, S. Selvakumar and G. Ilamurugan, “ Counsel System for Effective Farming using Data Mining Algorithm”, International Journal of Pure And Applied Mathematics, Volume 117 No. 21, 921–924 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (online version), 2017. [Google Scholar]
- Shridhar Mhaiskar, Chinmay Patil, Piyush Wadhai, Aniket Patil and Vaishali Deshmukh, “A Survey on Predicting Suitable Crops for Cultivation Using IoT ”, International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization), Website: www.ijircce.com, Vol. 5, Issue 1, January 2017. [Google Scholar]
- Lakshmi N., Priya M., Sahana Mrs. Shetty and Mr. Manjunath C.R., “Crop Recommendation System for Precision Agriculture”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), ISSN: 2321-9653; IC Value: 45.98; SJImpact Factor: 6.887, Volume 6 Issue 5, May 2018. [Google Scholar]
- Lakshmi, N., Priya, M., Sahana Mrs. Shetty and Manjunath C.R.Mr., “Crop Recommendation System for Precision Agriculture”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), ISSN: 2321-9653; IC Value: 45.98; SJImpact Factor: 6.887, Volume 6 Issue 5, May 2018. [Google Scholar]
- M.V.R. Vivek, D.V.V.S.S. Sri Harsha and P. Sardar Maran, “A Surveyon Crop Recommendation Using Machine Learning”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 22773878, Volume-7, Issue-5C, February 2019. [Google Scholar]
- Zhang, H. et al. Design and Implementation of Crop Recommendation Fertilization Decision System Based on WEBGIS at Village Scale. In: Lia D., Liu, Y., Chen, Y. (eds) Computer and Computing Technologies in Agriculture IV. CCTA 2010. IFIP Advances in Information and Communication Technology, vol 345. Springer, Berlin, Heidelberg, 2011. [Google Scholar]
- Patel, Harsh & Prajapati, Purvi. “Study and Analysis of Decision Tree Based Classification Algorithms”. International Journal of Computer Sciences and Engineering. 6. 74–78. doi: 10.26438/ijcse/v6i10.7478, 2018. [CrossRef] [Google Scholar]
- Kaviani, Pouria & Dhotre, Sunita. “Short Survey on Naive Bayes Algorithm”. International Journal of Advance Research in Computer Science and Management. 04, 2017. [Google Scholar]
- Evgeniou, Theodoros & Pontil, Massimiliano. Support Vector Machines: Theory and Applications. 2049. 249–257. doi: 10.1007/3-540-44673-7_12, 2001. [Google Scholar]
- Rong, Shen & Bao-Wen, Zhang. “The research of regression model in machine learning field”. MATEC Web of Conferences. 176. 01033. doi: 10.1051/matecconf/201817601033, 2018. [CrossRef] [EDP Sciences] [Google Scholar]
- Schonlau, Matthias, and Rosie Yuyan Zou. “The Random Forest Algorithm for Statistical Learning.” The Stata Journal, vol. 20, no. 1 pp. 3–29, doi: 10.1177/1536867X20909688, 2018. [Google Scholar]
- Santhanam, Ramraj & Uzir, Nishant & Raman, Sunil & Banerjee, Shatadeep. Experimenting XGBoost Algorithm for Prediction and Classification of Different Datasets, 2017. [Google Scholar]
- Weather Forecast API https://openweathermap.org/api [Google Scholar]
- https://www.kaggle.com/atharvaingle/crop-recommendation-dataset [Google Scholar]
- https://www.kaggle.com/abhinand05/crop-production-in-india [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.