iCrop: An Intelligent Crop Recommendation System for Agriculture 5.0

This article proposes a crop yield prediction and recommendation system for agriculture 5.0 based on edge computing, machine learning (ML), and steganography. In comparison with the existing crop yield prediction and recommendation frameworks, for the first time we are integrating steganography with edge computing and ML to provide a secure crop yield prediction and recommendation system. In the proposed system, an edge device is used for data preprocessing, and the private cloud server referred to as agri-server is maintained for data analysis and storage. For protecting data privacy during transmission, modified least significant bit-based image steganography is used. For data analysis, six ML approaches are used and compared based on their performance. The experimental results demonstrate that each ML approach achieves above 90% accuracy in crop yield prediction. The results also present that the proposed framework achieves highest prediction accuracy of 99.9% which is better than the existing crop yield prediction frameworks. The results also demonstrate that the proposed framework reduces the latency and energy consumption by $\sim$10% compared to the remote cloud-based crop yield prediction framework.