Metode single image-NDVI untuk deteksi dini gejala mosaik pada Capsicum annuum

Abstract

Single image-NDVI method for early detection of mosaic symptoms in Capsicum annuum
 Mosaics are a symptom of a disease often found in red chilies (Capsicum annuum) and is generally caused by viral infections such as the Tobacco mosaic virus. Severe infection can cause stunting and significant yield loss. Serological and molecular detection is a common detection method for plant viruses although they are time-consuming, relatively inefficient for large samples, and are destructive to plants. On the other hand, direct symptoms observation is hampered by human visual abilities and latent symptoms in virus infection. Therefore, detection method based on the plant’s ability to absorb and reflect various spectrums of sunlight, such as the normalized difference vegetation index (NDVI), has the potential to be developed. This study aims to evaluate the potential of a single image-NDVI as an NDVI variant for the early detection of mosaic symptoms in red chilies. The main activity involved image recording of chili plants that were not inoculated (V0) and inoculated (V1) by the virus, and given minimal nutrients (M) using an unmodified RGB camera and lens filter to capture blue and Near-Infrared light reflection. Furthermore, image processing is carried out using the Photo Monitoring plugin on the Fiji-ImageJ application. The recording was done one day after inoculation (dai) until the symptoms were visible. The results showed that there was an increasing trend in the integrated NDVI value in all treatments. Howewer, the increasing trend in V1 was not significant compared to V0 and M. The difference in the mean value of integrated NDVI between V1 was very significant compared to V0 (at 5 dai) and M (at 1 dai). This method’s level of sensitivity, specificity, and accuracy ranges from 80–90% at 5 dai.