Improving the Accuracy of Multi-Valued Datasets in Agriculture Using Logistic Regression and LSTM-RNN Method

Changes in environmental factors such as water quality, soil quality, and pollution factors lead to diseases in food producing plants. Identifying plant disease is a very difficult task in agriculture. Plant diseases are also mainly caused by many influences in agriculture which includes hybrid genetics, and the plant lifetime during the infection, environmental changes like climatic changes, soil, temperature, rain, wind, weather etc. The infections may be single or mixed, according to the infections the plants diseases spread. Early detection of plant diseases using recent technologies helps the plants growth. Therefore, Machine Learning techniques are used for early prediction of the diseases. This paper is used to improve the accuracy of detecting plant diseases using the prediction of the soil content in the field land. The techniques Nave Bayes (NB) and Neural Network (NN) were used in the existing system. The proposed system uses Logistic Regression method with Long Short-Term Memory (LSTM) in Neural Networks (NN) for predicting the soil content and also detects the plant diseases, improves the accuracy level in the plant growth.