Machine Learning Based Crop Prediction on Region Wise Weather Data
Article Sidebar
Main Article Content
Abstract
Agriculture is a primordial occupation for human civilization, whereby farmers cultivate domesticated species of food. It refers to farming in general, which is an art and science that attempts to reform a component of the Earth's exterior through the cultivation of plants and other crops, as well as raising livestock for sustenance or other necessities for the soul and economic gain. As a result of the vital role that sustainable agriculture plays in the overall health of the nation, this sector of the economy has been the incubator for some of the most cutting-edge technological advances in recent history. Scientists and farmers have been working together to discover new methods that will allow them to increase crop production while simultaneously decreasing their water consumption and lessening their negative effects on the environment. Machine learning, deep learning, and a number of other methodologies are some examples of these approaches. A crop's expansion and maturation are both heavily influenced by the climate in which it is grown. The local climate, namely its wind speed, temperature, rainfall, and humidity, is the most exigent factor in determining the advancement or failure of crop production. If the weather is predicted prior to crop cultivation, it will be beneficial to the farmer. Machine learning is a new innovation that can solve people’s real-life problems. It is a technique where a machine can act like a human and learn through experiences and the use of different types of data. Now a day, Agriculture is one of the fields of machine learning where we use different types of machine learning algorithms to predict crop production based on climate data which can benefited farmers to increase the production of the crop. In these studies, we are going to predict crop yield using LSTM based on predicted weather data.
Article Details
References
Ms Kavita, Pratistha Mathur, “Crop Yield Estimation in India Using Machine Learning”, 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Galgotias University, Greater Noida, UP, India. Oct 30-31, 2020.
Dr. Y. Jeevan Nagendra Kumar, V. Spandana, V.S. Vaishnavi, K. Neha, V.G.R.R. Devi, “Supervised Machine learning Approach for Crop Yield Prediction in Agriculture Sector”, Proceedings of the Fifth International Conference on Communication and Electronics Systems (ICCES 2020) IEEE Conference Record # 48766; IEEE Xplore ISBN: 978-1-7281-5371-1.
Jarrett Booz, Wei Yu, Guobin Xu, David Griffith, Nada Golmie, “A Deep Learning-Based Weather Forecast System for Data Volume and Recency Analysis” , 2019 International Conference on Computing, Networking and Communications (ICNC): Green Computing, Networking, and Communications.
Anakha Venugopal, Aparna S, Jinsu Mani, Rima Mathew, Prof. Vinu Williams, “Crop Yield Prediction using Machine Learning Algorithms”, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Published by, www.ijert.org NCREIS - 2021 Conference Proceedings.
Shook J, Gangopadhyay T, Wu L,Ganapathysubramanian B, Sarkar S, Singh AK, “Crop yield prediction integrating genotype and weather variables using deep learning” , PLoSONE 16(6): e0252402. https://doi.org/10.1371/journal.pone.0252402, June 17, 2021.
Kazuo Yonekura, Hitoshi Hattori and Taiji Suzuki, “Short-term local weather forecast using dense weather station by deep neural network”, 2018 IEEE International Conference on Big Data (Big Data).
Khaki S and Wang L, “Crop Yield Prediction Using Deep Neural Networks”, Front. Plant Sci. 10:621.doi: 10.3389/fpls.2019.00621, 22 May 2019.
Sonal Agarwal, Sandhya Tarar, “ A Hybrid Approach for Crop Yield Prediction Using Machine Learning and Deep Learning Algorithms”, CONSILIO 2020 Journal of Physics: Conference Series , 1714 (2021) 012012 IOP Publishing doi:10.1088/1742-6596/1714/1/012012.
Rishi Gupta, Akhilesh Kumar Sharma, Oorja Garg, Krishna Modi , Shahreen Kasim, Zirawani Baharum, Hairulnizam Mahdin, Salama A. Mostafa, “WB-CPI: Weather Based Crop Prediction in India Using Big Data Analytics ”, VOLUME 9, IEEE Access, 2021.
Thayakaran Selvanayagam, Suganya S, Puvipavan Palendrarajah Mithun Paresith Manogarathash, Anjalie Gamage, Dharshana Kasthurirathna , “Agro-Genius: Crop Prediction using Machine Learning” , Volume 4, Issue 10, International Journal of Innovative Science and Research Technology, October 2019.
Sonal Jain, Dharavath Ramesh, “Machine Learning convergence for weather based crop selection” , IEEE International Students’ Conference on Electrical, Electronics and Computer Science, 2020.
Shubham Madan1, Praveen Kumar, Seema Rawat, Tanupriya Choudhury, “Analysis of Weather Prediction using Machine Learning & Big Data” , 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE-2018) Paris, France 22-23 June 2018.
Urmay Shah, Sanjay Garg, NehaSisodiya, Nitant Dube, Shashikant Sharma, “Rainfall Prediction: Accuracy Enhancement Using Machine Learning and Forecasting Techniques”, 5th IEEE International Conference on Parallel, Distributed and Grid Computing(PDGC-2018), 20-22 Dec, 2018, Solan, India.
Aruvansh Nigam, Saksham Garg, Archit Agrawal, Parul Agrawal, “Crop Yield Prediction Using Machine Learning Algorithms” , 2019 Fifth International Conference on Image Information Processing (ICIIP).
Online available: https://www.kaggle.com/datasets/
Online Available: https://pib.gov.in/
Online Available: https://www.kisaanhelpline.com
Online Available: https://agricoop.nic.in/#gsc.tab=0
Online Available: https://iasri.icar.gov.in/
Online Available: https://krishi.icar.gov.in/
Jitendra Kumara, Rimsha Goomer, Ashutosh Kumar Singh, “Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters”, Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting, 2017.