mksami22 / Crop-Yield-Prediction

Crop Yield Prediction

Description:

Agriculture is an important part of India's economy and at present it is among the top two farm producers in the world. This sector provides approximately 52 percent of the total number of jobs available in India and contributes around 18.1 percent to the GDP. But recently farmers have been facing huge losses in the agriculture field due to lack of empowerment and applications of IT. I have tried to overcome this problem by applying various machine learning techniques to predict the crop yield by taking various factors into consideration like temprature and rainfall, area, etc.

Algorithms Used:

1. LSTM : LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series.
2. Simple RNN : A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behaviour for a time sequence.
3. Linear Regression : Forecasting by minimizing the errors in prediction in past.

Final Dataset:

Regression Analysis and Classification Analysis were both applied on this data set.

1. KNN Regressor and classification
2. Artificial Neural Net
3. Linear Regression
4. SGD Regressor and classification
5. Random Forest Regressor and classification

Comprehensive Analysis of Algorithms Applied:

1. Rainfall Prediction:

a. Simple RNN:

b. LSTM:

c. Comparision of Actual Distribution With Predicted:

2. Temprature Prediction:

a. Simple RNN:

b. LSTM:

c. Comparision of Actual Distribution With Predicted:

3. Yield Prediction:

a. Artificial Neural Network:

b. Linear Regression:

c. Random Forest Regressor:

d. KNN Regressor:

4. Comparision of various techniques:

Contributor:

Sami MK Radhika Gupta