Smart farming is evolving rapidly thanks to the integration of machine learning, blockchain, and the Internet of Things (IoT) technologies. Machine learning enables the use of agricultural data to improve crop monitoring, disease detection, yield forecasting, and resource management. Blockchain adds a complementary dimension by enhancing data security, transparency, and traceability.
The article “Review of Machine Learning and Blockchain Integration for Crop Monitoring and Management in Smart Farming” provides a systematic review of this emerging field. Using a methodology based on the PRISMA principles, 127 scientific publications were analyzed to identify the main applications, technological approaches, benefits, limitations, and research prospects.
The results show that combining machine learning and blockchain technology can foster the development of smarter, more reliable, and more transparent agricultural systems. Integrating these technologies with the Internet of Things (IoT), edge computing, and cloud computing also paves the way for more connected agricultural architectures tailored to on-the-ground needs.
However, several challenges remain, particularly regarding data quality, interoperability, scalability, privacy, and energy efficiency. Future research should therefore focus on integrated and robust solutions that can be applied in real-world agricultural conditions, in order to contribute to a more sustainable, resilient, and resource-efficient agricultural sector.