Enhancing Crop Recommendation Systems Through Advanced Deep Learning Techniques

Author(s)	Ms. Ekta Patel, Prof. Dr. Hiren jayantilal dand, Dr. neelam labhade
Country	India
Abstract	Economic development is greatly aided by the agricultural sector, particularly in developing countries. However, farmers face challenges related to crop yield decisions, soil management, and climate change interventions. In order to address these issues and provide a comprehensive strategy that uses a variety of Deep Learning (DL) techniques for crop selection based on soil condition, climate, and historical agricultural data, this study turns to DL. Predicted models and techniques include Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Transformers; precision agriculture through crop prediction; data-driven resource allocation decision-making to increase farm income; and soil management through soil pH prediction and suggested crops. The purpose of this action plan is to assist farmers in choosing the best course of action to improve farming outcomes.
Keywords	Agricultural, Deep Learning (DL), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Transformers, crop recommendation.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-18

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