Machine Learning Technique for Crop Recommendation in Agriculture Sector
Nitin N. Patil1, Mohmmad Ali M. Saiyyad2
1Dr. Nitin N. Patil, Department of Computer Engineering, R. C. Patel Institute of Technology, Shirpur, India.
2Mr. Mohammad Ali M. Saiyyad, Department of Computer Engineering, R. C. Patel Institute of Technology, Shirpur, India.
Manuscript received on October 01, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 1359-1363 | Volume-9 Issue-1, October 2019. | Retrieval Number: A1171109119/2019©BEIESP | DOI: 10.35940/ijeat.A1171.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Numerous efforts have been demonstrated through various innovations to lead towards the betterment of agriculture sector till now. Effective and innovative use of Science and Technology can help to improve crop quality and production, yield prediction and crop disease analysis. Agriculture sector provides various productions such as food, raw material for industry and has a significant impact on economy and employment of a country. The agriculture sector contains huge data with respect to factors affecting its input and output. With advances in technology, various data mining techniques have been introduced accordingly. Also different analytical models like Decision Tree, Random Forest, Support Vector Machine and Bayesian Neural Network are available for required analysis. These data mining techniques can be used to analyze the multidimensional, time specific data of agriculture sector to produce effective knowledge in support of the efforts to boost the economy. These methods can be further used to analyze soil, climate, moisture, humidity, temperature. In this paper, we used Naive Bayesian Classification technique to identify the class of crop and used the food grain dataset to analyze the technique over different attribute. Further we used Machine Learning approach for the accurate crop recommendation. Crop recommendation will help for effective decision making to the end user to take proper decision according to the output of the system.
Keywords: Agriculture Sector, Data Mining, Multidimensional, Time Specific Data, Machine Learning.