Abstract

Agriculture plays an important role in GDP Computation for our country. It fundamentally depends upon fair weather patterns, water for the water system, and circulated air through the soil, which are well-known facts for great farming according to our old framework. Yet, sadly, these variables are unforeseeable. Recent innovative promotion for better crop yielding is being integrated into Machine learning Algorithms with IoT platforms. Here, we will assess the similar investigation of Machine Learning for good crop producing with IoT and anticipating the yield of different crops, hence bringing about better return productivity. Swiss repliche rolex official fake rolex watches UK for sale are cheap for men and women.
The discounted online store provides wholesale rolex replica watches UK with Swiss movements.
Place an order for UK cheap replica rolex watches online is convenient and guaranteed.

References

1. Archit Agrawal, Aruvansh Nigam; Parul Agrawa, Saksham Garg, 'Crop Yield Prediction Using Machine Learning Algorithms', 2019 IEEE International conference
2. Adnan Abid, Farooq, Shamyla Riaz, Muhammad Shoaib, Tariq Umer and Yousaf Bin Zikria, 'Role of iot Technology in Agriculture: A Systematic Literature Review', Electronics 2020, 9(2), 319; https://doi.org/10.3390/electronics9020319.
3. Archit Agrawal, Aruvansh Nigam, Parul Agrawal, Saksham Garg, ‘Crop Yield Prediction Using Machine Learning Algorithms’, 2019 Fifth International Conference on Image Information Processing (ICIIP)
4. Archana Praveen Kumar, Radhika Sreedharan,’Analysis And Prediction of Smart Data Using Machine Learning’ AIP Conference Proceedings 2240, 140001 (2020); https://doi.org/10.1063/5.0011064
5. Bollu Lakshmi Avinash, Potnuru Sai Nishant, Pinapa Sai Venkat, B. Jabber 'Crop Yield Prediction based on Indian Agriculture using Machine Learning', 2020International Conference for Emerging Technology (INCET) Belgaum, India. Jun 5-7.
6. Sai Sowmya, Divya Sri, Jyothi, Meghana, Pavani Latha,’Machine Learning Approach Forforecasting Crop Yield Based on Climatic Parameters’, JAC: A Journal Of Composition Theory Volume XIII, Issue V, May 2020 ISSN: 0731-6755
7. Chellammal Surianarayanan, Kodimalar Palanivel 'An Approach For Prediction of Crop Yield Using Machine Learning And Big Data Techniques' International Journal of Computer Engineering and Technology (IJCET) Volume 10, Issue 03, May-June 2019, pp. 110-118.
8. Guiping Hu, Isaiah Huber & Sotirios V. Archontoulis, Mohsen Shahhosseini, 'Coupling machine learning and crop MODELING IMPROVES crop yield prediction in the US Corn Belt', www.nature.com
9. https://www.scribd.com/document/397375927/5
10. https://www.doc.ic.ac.uk/~jce317/history-machine-learning.html

Download PDF
Back