Improvement of Agriculture Yield Rate of Crops and Decision Making Using Machine Learning Techniques
Abstract
India being an
agricultural country, its economy mainly depends on agriculture yield growth
and allied agro-industry products. In India agriculture is largely influenced
by rain water which is highly unpredictable. Agriculture growth depends on
diverse soil parameters like nitrogen, phosphorous, potassium, crop rotation,
soil moisture, surface temperature. It also depends on whether aspects which
include temperature, rainfall etc. Agriculture is one of the major fields in
our country and also plays a major role in our country’s economy. India is the
second largest producer of agriculture crops and agriculture is one of the
major and least paid occupations in India. Variability in seasonal climate
conditions can have harmful effects, with incidents of drought reducing
production. Developing better techniques to predict crop productivity in
various climatic conditions can help farmer and other stakeholders in their decision
making in terms of agronomy and crop choice.
Country : India
1 Sheetal Kulkarni
Associate Professor, Department of Computer Science and Engineering, Malla Reddy College of Engineering for Women, Hyderabad -500100, Telangana, India
Gour Hari Santra, Debahuti Mishra and
Subhadra Mishra, Applications of Machine Learning Techniques in Agricultural
Crop Production, Indian Journal of Science and Technology, October 2016.
Karan deep Kauri, Machine Learning:
Applications in Indian Agriculture, International Journal of Advanced Research
in Computer and Communication Engineering, April 2016.
S. Djodiltachoumy, A Model for
Prediction of Crop Yield, International Journal of Computational Intelligence
and Informatics, March 2017.
Nishit Jain, Amit Kumar, SahilGarud,
Vishal Pradhan, Prajakta Kulkarni, Crop Selection Method Based on Various
Environmental Factors Using Machine Learning, Feb -2017.
Niketa Gandhi, OwaizPetkar, Leisa J
Armstrong “Rice crop yield prediction using Support Vector Machines” 2016 IEEE
Technological Innovations in ICT for Agriculture and Rural Development.
J.P. Singh, M.P. Singh, Rakesh Kumar
and Prabhat Kumar “Crop Selection Method to Maximize Crop Yield Rate using
Machine Learning Technique”, International Journal on Engineering Technology,
May 2015.
Prof. D. S. Zingade, OmkarBuchade,
NileshMehra, ShubhamGhodekar, ChandanMehta “Crop Prediction system using
machine Learning”.
Ramesh Medar, Vijay S.Rajpurohit,
Shweta “Crop yield Prediction using Machine Learning Techniques” 2019 IEEE 5th
International Conference for Convergence in Technology (I2CT)
S. S. Kale and P. S. Patil, "A
Machine Learning Approach to Predict Crop Yield and Success Rate," 2019
IEEE Pune Section International Conference (PuneCon), Pune, India, 2019.
