Crop Yield Prediction based on Indian Agriculture using Machine Learning
Crop Yield Prediction is an important feature of agriculture domain that helps farmers to make correct decision for huge losses for their crops. User have to provide the details of temperature (C), Humidity, Nitrogen, Phosphorous, Potassium from those inputs values model will predict the crop. It will predict and monitor crop and plot a graphs of individual values.
Implement machine learning model for crop yield prediction, a huge dataset of crop yield data is required. The machine learning algorithm is trained on dataset to learn the relationship between input and output. After the model trained it can be used to do prediction about crop yield. To implement machine learning model for crop yield prediction, a huge dataset of crop yield data is required. The machine learning algorithm is trained on dataset to learn the relationship between input and output. After the model trained it can be used to do prediction about crop yield.
Crop Yield Prediction with Neural Networks marks a significant stride in precision agriculture by harnessing the power of neural networks to predict crop yields. Analyzing diverse agricultural data, the model forecasts crop production based on factors such as weather conditions, soil quality, and historical yield patterns. This application empowers farmers with valuable insights, enabling better resource management and strategic decision-making for optimized crop yields.
Crop Yield Prediction using Deep Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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