A Review on Crop Yield Prediction using Deep Learning

The integration of cutting-edge technology into agricultural practices has catalyzed a transformative shift in global agriculture. This research study focuses on the revolutionary impact of deep learning approaches on crop yield prediction, a critical aspect of sustainable agriculture amid increasing global food demands. Traditional methods for crop yield estimation are examined initially, revealing limitations in accuracy and adaptability to diverse agroclimatic conditions. Subsequently, the rise of deep learning is explored, highlighting its ability to autonomously extract complex patterns from vast agricultural datasets. This research study provides a comprehensive classification and analysis of deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and attention mechanisms, in the context of agricultural yield prediction. This study discusses about the incorporation of sensor-based information, satellite imagery, and climate data into deep learning models, emphasizing the synergy among the data sources to enhance the prediction accuracy. Additionally, this research study addresses the challenges related to data quality, model interpretability, and scalability, proposing solutions and future research directions. By illuminating the transformative role of deep learning in crop yield prediction, this research study equips researchers, practitioners, and policymakers with valuable insights to advance sustainable agriculture and ensure global food security amidst changing climatic patterns and demographic trends.