Coconut leaf nitrogen measurement using different vegetative indices and multispectral images

To ensure sustainable agricultural practices, it is crucial to monitor the health of coconut trees. Managing the shape and size of their canopies effectively poses significant challenges due to the absence of advanced technological solutions. This industry and economy can benefit greatly from the integration of multispectral imaging, which offers innovative ways to assess and improve tree health and productivity. By deploying Unmanned Aerial Vehicles (UAV), critical vegetation indexes such as Normalized Difference Vegetative Index (NDVI), Normalized Difference Red Edge (NDRE), Chlorophyll Index (CI) green, and Chlorophyll Index red edge derived from multispectral images are cost-effective, high-resolution, and facilitating continuous plant monitoring. This study is aimed to monitor leaf nitrogen content in coconut plantation using multispectral UAV-based approach in Farm at Faculty of Agriculture, Mapalana, Kamburupitiya. The correlations were identified between UAV captured images and SPAD values, as well as soil nitrogen percentage using ground truth data which were gathered through targeted explorations aimed to optimizing data acquisition efficiency. Data processing revealed strong correlations between crop and soil variables and specific vegetation index, including NDVI, NDRE CI green and CI red edge. The NDVI demonstrated strong association with soil nitrogen content, while NDRE and CI green showed significant correlations with SPAD value with R-squared values of 71.3 and 73.4 %, respectively. The R-Squared values for the soil nitrogen content with NDVI was 57.2 %. These findings highlight that NDRE and CI green are more effective indicators for measuring nitrogen concentration of coconut leaves. Monitoring of vegetation indices using Utilizing UAV-based monitoring of vegetation indices can greatly enhance site specific management in coconut plantation, promoting sustainable agricultural practices.