Artificial Intelligence and Agriculture 5. 0
Rajkumar Murugesan1, S. K. Sudarsanam2, Malathi. G3, V. Vijayakumar4, Neelanarayanan .V5, Venugopal. R6, D. Rekha7, Sumit Saha8, Rahul Bajaj9, Atishi Miral10, Malolan V11
1Rajkumar Murugesan, VIT Business School, Vellore Institute of Technology, Chennai, India
2Dr. Sudarsanam, Kidambi, VIT Business School, Vellore Institute of Technology, Chennai, India
3Dr. Malathi.G, Associate Professor, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai Campus.
4V. Vijayakumar, VIT Business School, Vellore Institute of Technology, Chennai, India.
5Neelanarayanan.V, VIT Business School, Vellore Institute of Technology, Chennai, India
6Venugopal. R, VIT Business School, Vellore Institute of Technology, Chennai, India.
7D. Rekha, VIT Business School, Vellore Institute of Technology, Chennai, India.
8Sumit Saha, VIT Business School, Vellore Institute of Technology, Chennai, India.
9Rahul Bajaj, VIT Business School, Vellore Institute of Technology, Chennai, India.
10Atishi Miral, VIT Business School, Vellore Institute of Technology, Chennai, India.
11Malolan V, VIT Business School, Vellore Institute of Technology, Chennai, India.
Manuscript received on 02 March 2019 | Revised Manuscript received on 06 March 2019 | Manuscript published on 30 July 2019 | PP: 1870-1877 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1510078219/19©BEIESP | DOI: 10.35940/ijrte.B1510.078219
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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: Use of Artificial Intelligence and Robotics in agriculture is called as Agriculture 5.0 Disruptive technology should help in solving the social needs . Rogers suggest to develop human centric “ Ubiquitous Computing ” solution, for specific domain (agriculture production). Crop yield prediction (CYP) is vital to address the ever growing demand of food requirements of burgeoning world population and to prevent starvation. Artificial Intelligence can offer effective and practical solution for the problem. Machine Learning (ML) and Deep learning (DL) have been evaluated. Machine Learning models (using python, R, Seaborn) have been experimented in this paper. Data of crop yield is used for model evaluation, which includes horticultural product (Banana), cash crop (Sugarcane), food crop (Rice), for kharif, rabi season (Dataset of Tamil Nadu and US region); Future research could combine remote sensing data and machine learning to predict the yield ( using google earth engine). Better accuracy in crop prediction is possible when vital data like soil moisture content ( ground level, root level and extreme ends of the field), 14 micro nutrients of soil is made available, for many seasons.
Index Terms: CYP, AI 2. 0, Machine Learning ( Python, R, Seaborn Plotting (Python Visualization Package)), LSTM, SVM, Cyber Physical System (CPS), Drones (UAV), Floccinaucinihilipilification, STARMAC Quadrator Helicopter UAV, Trans Disciplinary Approach
Scope of the Article: Machine Learning