Crop Recommendation System for Best Farming Practices Using Various Deep Learning Algorithms

Crop prediction and recommendation plays an important role in the field agriculture. The proposed work explores the application of machine learning (ML) and deep learning (DL) techniques in predicting crop yields, a critical aspect of modern agriculture. The comprehensive and systematic analysis on various algorithms and methodologies employed in crop yield prediction are examined. The study investigates the challenges and advancements in utilizing ML and DL models, emphasizing the importance of considering environmental factors as well as remote sensing data and satellite imagery. A novel model is also proposed and evaluated amongst two state of the art deep learning algorithms, showcasing promising results with a higher correlation coefficient despite a seemingly higher value in Root Mean Squared Error (RMSE). The findings underscore the potential of ML and DL techniques in enhancing agricultural productivity and sustainability through informed decision- making. This paper contributes to the ongoing discourse on precision agriculture, highlighting the integrated significance of technologies to address the complexities of modern farming practices.