Fecha de publicación: 18/11/2021 Fuente: Wipo "precision agriculture" Due to the inherent agrarian aspect of the economy, the agricultural sector holds paramount importance in many countries. Some countries have their GDP dependent on agriculture, but they rely on manual crop monitoring, which is a system that is labor intensive and ineffective. In comparison to this, in developing countries, many cutting-edge technology based technologies are being used to increase crop yield with optimum resource utilization. To this end, this invention suggested an integrated approach using IoT, machine learning and drone technology for monitoring crop health. The incorporation of these sensing modalities produces heterogeneous data which is not only differing in absorbed parameter also in the temporal fidelity. The spatial resolution of these approaches is also different, so the proposed scheme suggests the optimum integration of these sensing modalities and their implementation in practice. The proposed work is essentially an indigenous, technology-based agricultural solution capable of providing important insights into crop health by extracting additional features from the multi-modal data set and minimizing the effort to survey crops, particularly useful when the agricultural land is large.