Machine Learning in Agriculture Application: Algorithms and Techniques
Meeradevi1, Sindhu N2, Monica R Mundada3
1Meeradevi*, Dept. of CSE M S Ramaiah Institute of Technology Bangalore.
2Sindhu N, Dept. of CSE M S Ramaiah Institute of Technology Bangalore.
3Monica R Mundada, Dept. of CSE M S Ramaiah Institute of Technology Bangalore.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on April 10, 2020. | PP: 1140-1146 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3713049620/2020©BEIESP | DOI: 10.35940/ijitee.F3713.049620
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© 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: Machine learning techniques with high performance computing technologies can create various new opportunities in the agriculture domain. This paper does comprehensivere view of various papers which are concentrating on machine learning (ML) and deep learning application in agriculture. This paper is categorized into three sections a) Yield prediction using machine learning technique b) Price prediction c) Leaf disease detection using neural networks. In this paper we study the comparison of neural network models with existing models. The findings of this survey paper indicate Deep learning models give high accuracy and outperform traditional image processing technique and ML techniques outperforms various traditional techniques in prediction.
Keywords: Machine Learning Techniques, Application, Algorithms.
Scope of the Article: Machine Learning