Data Analysis of Agriculture using Data Mining Techniques

Data mining is a method of uncovering hidden patterns in vast, complex databases. It's crucial while dealing with complex agricultural challenges. To detect trends in the influence of a range of factors on crop yield, data visualization is necessary. The planned prediction system is critical in addressing India's food security challenges, as well as advising government agencies on how to deal with over-or under-production situations. In nonlinear complicated settings, deep learning techniques are crucial for properly predicting the agricultural output. The state-of-the-art pattern recognition of time-sequence data has been achieved using the recurrent Neural Network (RNN) technique. Long Short–Term Memory is the most extensively utilized strategy in RNN models (LSTM). In order to forecast crop production in India, the suggested approach is used to extract information from previous agricultural data collections. Python Jupiter is used to carrying out the simulation. The RMSE, MAE, and correlation coefficient are the performance measures employed. Long Short–Term Memory (LSTM), Agriculture Data Mining, and Deep Learning are all used to analyze the data.