Artificial Intelligence Based Recommendation System for Farmers

International Journal of Computer Trends and Technology (IJCTT)          
© 2019 by IJCTT Journal
Volume-67 Issue-3
Year of Publication : 2019
Authors : G.Ramyalakshmi, A.Deeksha, M.Sumana.M.E
DOI :  10.14445/22312803/IJCTT-V67I3P106


MLA Style: G.Ramyalakshmi, A.Deeksha, M.Sumana.M.E, "Artificial Intelligence Based Recommendation System for Farmers" International Journal of Computer Trends and Technology 67.3 (2019): 22-25.

APA Style:G.Ramyalakshmi, A.Deeksha, M.Sumana.M.E, (2019). Artificial Intelligence Based Recommendation System for Farmers. International Journal of Computer Trends and Technology, 67(3), 22-25.

In an agro-based country, the main goal of agricultural planning is to achieve maximum yield rate of crop using land resource. Selection of crop yield is maximized by considering the proportion of nutrients present in the soil. In this paper, the farmer / beginner will predict the crop cultivation based on their weather, monsoon and soil type along with their pH level.For the chosen crop, the cultivation process is recommended in the form of audio, video and text in three languages namely Hindi, English and Tamil. During the cultivation of crops,the amount of fertilizers, insecticides and fungicides are analyzed using Machine Learning Technique. Here, K-Nearest Neighbor Algorithm is used for recommending nearby shops using adjacent maps for buying and selling products to the farmers. Finally, using this system the farmers will have a well guided approach to begin with farming.

[1] Junyan Ma, Xingshe Zhou, Shining Li, Zhigang Li “Connecting Agriculture to the Internet of Things throughSensor Network” 2011 IEEE International Conferences on Internet of Things, and Cyber, Physical and Social Computing
[2] Andreas Kamilaris, FengGaoy, Francesc X. Prenafeta-Bold´u , Muhammad IntizarAliy “Agri-IoT: A Semantic Framework for Internet of Things-enabled Smart Farming Applications” 2016 European Union
[3] KarandeepKaur “Machine Learning Applications: Indian Agriculture” International Journal of Advanced Research inComputer and Communication Engineering Vol. 5, Issue 4, April 2016
[4] Rakesh Kumar, M.P. Singh, Prabhat Kumar and J.P. Singh “ Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique” 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Vel Tech RangarajanDr.Sagunthala R&D Institute of Science and Technology, Chennai, T.N., India. 6 - 8 May 2015. pp.138-145.
[5] S.Sannakki, V.S. Rajpurohit, F. Sumira, H.Venkatesh “A Neural Network approach for Disease Forecasting in Grapes using Weather Parameters” 4th ICCCNT 2013
[6] Jay Gholap “Performance Tuning Of J48 Algorithm for Prediction of Soil Fertility”
[7] Heamin Lee “Disease and Pest Prediction IoT System in Orchard: A Preliminary Study” ICUFN 2017
[8] K.A.Patil, N.R.Kale “A model for smart agriculture using IoT” 2016, International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)
[9] A.Nithya, Dr. V. Sundaram, “Classification rules for Indian Rice Diseases”. International journal of Computer Science Issues, Vol.8, Issue 1, ISSN (Online):1694-0814, January 2011.
[10] P.Revathi, R.Revathi, Dr.M.Hemalatha, “Comparative Study of Knowledge in Crop Diseases Using Machine Learning Techniques”. International Journal of Computer Science and Information Technologies (IJCSIT), Vol.2 (5), 2180-2182, 2011.
[11] Yudong Zhang, LenanWu., “Crop Classifcation By Forward Neural Network With Adaptive Chaotic Particle Swarm Optimization”. Sensors, 4721-4743; doi:10.3390/s110504721, 2011.
[12] Seen, R., 2001. “Plant Disease Forecasting in the era of Information Technology”. In:plant disease forecast : Information Technology In Plant Pathology, Kyongju, Republic of Korea.
[13] Jabrzemski, R., Sutherland, A., 2006 “An innovative approach to weather based decision supports for agriculture models”. In:22nd International Conference On Interactive Information Processing Systems For Meteorology, Oceanography and Hydrology. American Meteorological Society, Washington, DC, USA.
[14] Bannayan, M., Hoogenboom, G. “Daily weather sequence prediction using the non-parametric nearest neighbour resampling technique”. International Journal of Climatology, In press.

ML as Machine Learning, AI as Artificial Intelligence, RFA as Random Forest Algorithm, KNN as K-Nearest Neighbor Algorithm, DB as Data Base, ED as Euclidean Distance, EF as Euclidean Function.