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- W4361732090 abstract "Indian subcontinent in the South Asian Region is the most productive and committing region of the world. Seeing the Food crisis in the world, there is heavy need felt to develop the devices based on IoT and Cyber Physical systems which can help farmers technically to take decision at the time of need. It may be forecasting about the yield or it may be moisture related info or weather forecasting based analysis. The present manuscript is an effort to develop a novel approach to use AI and ML in crop and yield productivity so that we can bridge the increasing gap among demand and supply of edibles for the world populations.The Python and Ml algorithms have been used on the available dataset and accuracy has been tried to be increased with better learning algorithms" @default.
- W4361732090 created "2023-04-04" @default.
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- W4361732090 date "2022-12-16" @default.
- W4361732090 modified "2023-10-14" @default.
- W4361732090 title "Crop and Yield Prediction Through Machine Learning Techniques to Maximize Production: 21st Century Sustainable Approach for Smart Cities 5.0" @default.
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- W4361732090 doi "https://doi.org/10.1109/icac3n56670.2022.10074244" @default.
- W4361732090 hasPublicationYear "2022" @default.
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