Abstract
The Strategic decision to take machine learning and AI as a tool to enhance agriculture production is to enrich a farmer’s valuable time. It has an immense opportunity to make secured crops and healthy environment in due time. In extreme weather condition and drastic change in a situation many predictive and recognition techniques will improve not only the quality of crops but also their ability to producible. In this context, we have dedicated a proposed model which will predict for a real-time environment which crop will be benefitted on producing and for any particular area what should be the environmental condition for healthy productivity. This is done by using a machine learning algorithm in seasonal trend scenario with predictive analysis and applying a pattern recognition technique for discovering cropping pattern and their evaluation.
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Appendix 1: List of Abbreviation
Appendix 1: List of Abbreviation
Slno | Abbreviation | Meaning |
|---|---|---|
1 | CK | Chidamber and Kemerer object oriented metrics |
2 | MinTemp | Minimum Temperature |
3 | MaxTemp | Maximum Temperature |
4 | Snow | Constant graph |
5 | Precip | Precipitation |
6 | ML | Machine Learning |
7 | PA | Predictive Analysis |
8 | DL | Deep Learning |
9 | NB | Naive Bayes algorithm |
10 | FNB | Fuzzy Naive Bayes prediction model |
11 | MLR | Multiple Linear Regression |
12 | OO | Object-Oriented programs |
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Padhy, N., Satapathy, S.C. (2020). Digital Advancement in AgriTech by Using Evolutionary Techniques. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 160. Springer, Singapore. https://doi.org/10.1007/978-981-32-9690-9_37
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DOI: https://doi.org/10.1007/978-981-32-9690-9_37
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