Utilizing Drones and Satellite Imagery in Crop Monitoring and Precision Farming An Application of the WASPAS Method

Drones and satellite imagery have revolutionized crop monitoring and precision farming, offering innovative solutions for enhanced agricultural productivity. These technologies enable farmers to collect high-resolution data on crop health, soil conditions, and resource management. Drones provide real-time, localized insights, allowing for precise interventions, while satellite imagery offers extensive coverage and long-term trend analysis. By integrating these advanced technologies, farmers can monitor their fields more efficiently, reduce costs, and contribute to sustainable farming practices, ultimately ensuring food security in an increasingly challenging agricultural landscape. The significance of drones and satellite imagery in crop monitoring and precision farming lies in their potential to enhance agricultural efficiency and sustainability. They facilitate early detection of issues such as pests, diseases, and nutrient deficiencies, reducing waste and minimizing environmental impact. Furthermore, the integration of these tools supports precision agriculture practices, which are crucial for adapting to climate change and meeting the growing global food demand. Ultimately, their application promotes sustainable farming and contributes to food security. The methodology for utilizing drones and satellite imagery in crop monitoring and precision farming involves several key steps. First, high-resolution satellite images are obtained to assess the overall health and variability of crops across large areas. Drones are deployed for targeted monitoring, capturing detailed images and data at a finer scale. Data processing and analysis use advanced algorithms to extract insights, such as NDVI (Normalized Difference Vegetation Index) for assessing plant health. The findings inform precision interventions, such as variable rate irrigation or fertilization. Continuous monitoring allows for adaptive management, ensuring timely responses to emerging issues and optimizing agricultural practices. Alternative taken as Drone A, Drone B, Satelite A, Satelite B, Drone+Satelite. Evaluation preference taken as Cost Efficiency (USD/ha), Data Accuracy (%), Timeliness of Data, Coverage Area (ha). Drone A getting first place of the table and satellite B is getting last place of the table.