Cap-DiBiL: an automated model for crop water requirement prediction and suitable crop recommendation in agriculture

Abstract

In this technological era, several approaches used to provide the information about suitable crop recommendation means which is crop is suitable for soil. Some of approaches depends on the IoT smart agricultural-devices to gather information from surrounding area. However, several collection of data are used to predict the crops details but it not efficient to provide better performance. Therefore, the proposed model uses various techniques to improve the performance efficiently. Some steps involved in the proposed model as data pre-processing, feature extraction, feature selection, water requirement prediction and recommendation. Initially, the collected IoT data from dataset are pre-processed using data normalization, missing value imputation and one-hot encoding. Then, extract the features from pre-processed data using Gated Residual autoencoder (GRA) model, whereas optimal features are selected using Chaotic Northern Goshawk Optimization (ChaNgo) algorithm. Based on the farmland details, the crop water requirement prediction and suitable crop recommendation due to the market price are carried out using a novel hybrid deep learning model called Channel capsule-assisted stacked dilated Bi-LSTM (Cap-DiBiL). The channel capsule network predicts the crop water requirement and stacked dilated Bi-LSTM is used for suitable crop recommendations such as millets, rice and other crops. Then the proposed model analyses the performance and compares it with several existing techniques to prove the proposed model’s enhancement. The proposed model improved the accuracy as 98.18% for predicting the crop water requirement and crop recommendation. The performance of proposed model for Precision, Recall and F1 score also enhanced as 98.31%, 98.18% and 98.20%.

Keywords

• value imputation
• one-hot encoding
• residual autoencoder
• chaotic northern goshawk
• channel capsule
• stacked Bi-LST0M