Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning
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Abstract
Crop decision is a very complex process. In Agriculture it plays a vital role. Various biotic and abiotic factors affect this decision. Some crucial Environmental factors are Nitrogen Phosphorus, Potassium, pH, Temperature, Humidity, Rainfall. Machine Learning Algorithm can perfectly predict the crop necessary for this environmental condition. Various algorithms and model are used for this process such as feature selection, data cleaning, Training, and testing split etc. Algorithms such as Logistic regression, Decision Tree, Support vector machine, K- Nearest Neighbour, Navies Bayes, Random Forest. A comparison based on the accuracy parameter is presented in this paper along with various training and testing split for optimal choice of best algorithm. This comparison is done on two tools i.e., on Google collab using python and its libraries for implementation of Machine Learning Algorithm and WEKA which is a pre-processing tool to compare various algorithm of machine learning.
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Guruprasad Deshpande, Anjali Sakurkar and Sonali Kale “Opportunities in Crop Deciding Platform” ES Food & Agroforestry, volume 1, September 2020
S. Kangasuba Raja, Rishi R, Sudarshan E, Sirijit V. “Demand-Based Crop Recommended system for farmers” IEEE International Conference on Technological Innovation in ICT for agriculture and Rural Development-2017
Balaji Prabhu B.V., Dr. M Dakshayaini “Demand Prediction Model for forecasting AGRI-Needs of Society”-International conference on inventive computing and informatics-2020
Soumya Sri Attaluri, Nowshatha k Batcha, Raheen Mafias “Crop plantation Recommendation using feature extraction and machine learning techniques”, Journal of Applied Technology and Innovation-2020
Aditya Humnabadkar, Omkar Kulkarni, Mrs. Rajani. P. K, " Automation of Vehicle Identification at Night using Light Source Recognition", 5th International Conference on Computing Communication Control and automation (ICCUBEA 2019), Pune, India, held on 19th to 21st September 2019
Uddin, S., Khan, A., Hossain, M. et al. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 19, 281 (2019).
Kumar Pramanik, K. ., B. Rahane, S. ., Jayamani, V. N. ., Mehra, R. ., C. R., M. ., & V. Athawale, S. . (2023). Cloud Computing based Wireless Sensor Network in Data Transmission With Routing Analysis Protocol and Deep Learning Technique. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 165–169. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2556
Rajani P. K. and Arti Khaparde "Video error concealment using block matching and frequency selective extrapolation algorithms", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431C (19 June 2017)
G. M. Naidu “Applicability of Arima models in the wholesale vegetable market.” Int. J. Agric. Stat. Sci., vol. 11, no. 1, pp. 69–72. (2015)
Mr. Kunal Verma, Mr. Dharmesh Dhabliya. (2015). Design of Hand Motion Assist Robot for Rehabilitation Physiotherapy. International Journal of New Practices in Management and Engineering, 4(04), 07 - 11. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/40
Joachims T. Making large-scale SVM learning practical. SFB 475: Komplexitätsreduktion Multivariaten Datenstrukturen, Univ. Dortmund, Dortmund, Tech. Rep. 1998. p. 28.
Rajani, P. K., Neha Motagi, Komal Nair, and Rupali Narayankar. "Autonomous Smart Device for COVID-19 Detection Using Artificial Intelligence." In Handbook of Research on Applied Intelligence for Health and Clinical Informatics, pp. 128-147. IGI Global, 2022.
B. Sawicka, A. H. Noaema, T. S. Hameed, and B. Krochmal-Marczak, ‘‘Biotic and abiotic factors influencing on the environment and growth of plants,’’ (in Polish), in Proc. Bioró?norodno?? ?rodowiska Znaczenie, Problemy, Wyzwania. Materia?y Konferencyjne, Pu?awy, May 2017.
M. Marenych, O. Verevska, A. Kalinichenko, and M. Dacko, ‘‘Assessment of the impact of weather conditions on the yield of winter wheat in Ukraine in terms of regional,’’ Assoc. Agricult. Agribusiness Econ. Ann. Sci., vol. 16, no. 2, pp. 183–188, 2014.
S. K. Honawad, S. S. Chinchali, K. Pawar, and P. Deshpande, ‘‘Soil classification and suitable crop prediction,’’ in Proc. Nat. Conf. Comput. Biol., Commun., Data Anal. 2017, pp. 25–29.
N. Rale, R. Solanki, D. Bein, J. Andro-Vasko, and W. Bein, ‘‘Prediction of Crop Cultivation,’’ in Proc. 19th Annu. Comput. Commun. Workshop Conf. (CCWC), Las Vegas, NV, USA, 2019, pp. 227–232.
Ricci, A., Jankowski, M., Pedersen, A., Sánchez, F., & Oliveira, F. Predicting Engineering Student Success using Machine Learning Algorithms. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/118
Shivnath and S. Koley, ‘‘Machine learning for soil fertility and plant nutrient management using back propagation neural networks,’’ Int. J. Recent Innov. Trends Comput. Commun., vol. 2, no. 2, pp. 292–297, 2014.
M. Paul, S. K. Vishwakarma, and A. Verma, ‘‘Analysis of soil behaviour and prediction of crop yield using data mining approach,’’ in Proc. Int. Conf. Comput. Intell. Commun. Netw. (CICN), Dec. pp. 766–771, 2015.
S. Pudumalar, E. Ramanujam, R. R. Harine, C. Kavya, T. Kiruthika, and J. Nisha, ‘‘Crop recommendation system for precision agriculture,’’ in Proc. 8th Int. Conf. Adv. Comput. (ICoAC) , pp. 32–36, 2017.
Mariammal, A. Suruliandi, S. P. Raja, and E. Poongothai, ‘‘Prediction of land suitability for crop cultivation based on soil and environmental characteristics using modified recursive feature elimination technique with various classifiers,’’ IEEE Trans. Computat. Social Syst., vol. 8, no. 5, pp. 1132–1142, Oct. 2021.
A. Liaw and M. Wiener, ‘‘Classification and regression by random forest,’’ R News, vol. 2, pp. 18–22, Apr. 2002.
Datalink: Kaggle datasets download -d atharvaingle/crop-recommendation-dataset