<p>Agriculture is the main economic growth and development on the earth's surface for human survival. The quality of the crop is affected by natural conditions and is the most significant factor for producing crop productivity. A Representational Alignment Net-based Long Short-Term Memory (REALNET–LSTM) model for crop recommendation system technique has been created to increase crop productivity in the agricultural fields using several processing techniques. Normalizing the data, encoding the categorical data, and imputed missing data were introduced in this research. Features such as periodicity, self-similarity, and mutual information used to create the informative feature vector along with that deep feature are used for the recommendation, and in addition, statistical features are performed to reduce the amount of redundant data from the data set. The crop recommendation system is based on a hybrid deep learner to suggest crops for agricultural fields. The proposed REALNET–LSTM model was evaluated with the crop recommendation dataset and intelligent crop prediction dataset, with 80% of the training percentage at 100 epochs to enhance better achievements. It attained 97.45% accuracy, 96.03% precision, and 98.86% of recall on the crop recommendation dataset and 96.05% of accuracy, 94.66% of precision, and 97.44% of recall on the intelligent crop prediction dataset. Similarly, for K-fold the values achieved on the intelligent crop prediction dataset with 100 epochs with 10 folds, achieved an accuracy of 95.23%, recall of 96.64%, and precision of 93.82%. Beyond, the values demonstrate for the crop recommendation dataset as 98.86% recall, 93.82% precision, and 95.23% accuracy respectively.</p>