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.

Estimation of blight and spot diseases severity in Ciherang and Ciliwung rice varieties based on vegetation index algorithms

“…NDVI serves as a valuable tool for estimating the size of plant area experiencing health problems. This method has proven useful for early disease detection and assessment of plant disease severity (Hasan et al 2021;Taufik et al 2023a,b). However, in this study, it is crucial to acknowledge the limitations of NDVI in precisely identifying factors disrupting plant health.…”

“…Rice field images were recorded based on the methodology by Hasan et al (2021) with slight modifications. Recordings were taken from 09:00 am UTC+8 under clear sky conditions, using Canon EOS 750D camera with a lens featuring an On-Lens IR-Ultra Blue filter (www.spencercamera.com).…”

Vegetation index value on chili leaves with symptoms of geminivirus disease (case study in Konda district, Konawe Selatan regency, Southeast Sulawesi)

“…Two ROIs for each image with dimensions of 180 × 180 pixels each, the total ROI for each symptom variation group is 6 ROIs (ROI focused on the leaf area of the plant image). Furthermore, each ROI image is converted to NDVI image using the Photo Monitoring plugin [13] as in previous studies [10]. 2.3.3.…”

“…One method for assessing the severity of disease symptoms caused by viruses in chili plants based on digital image processing that is worth trying is NDVI image processing (normalize difference vegetation index). The NDVI method has been reported to be able to distinguish chili IOP Publishing doi:10.1088/1755-1315/1182/1/012004 2 plants infected with Tobacco mosaic virus (TMV) from healthy chili plants and nutrient-deficient chili plants before visual symptoms become visible [10]. However, the magnitude of the NDVI value in each variation of Geminivirus symptoms in chili plants has not been reported.…”

Characteristics of Virus Symptoms in Chili Plants (Capsicum frutescens) Based on RGB Image Analysis

“…Camera sensors with the ability to record changes in leaf spectral reflectance due to disease infection can be used as a non-destructive method for the early detection of plant diseases (Cordon, Andrade, Barbara, & Romero, 2022;Kuska et al, 2022;Nguyen et al, 2021). Hasan, Widodo, Mutaqin, Taufik, & Hidayat (2021) reported that using cameras and digital image analysis techniques, namely SI-NDVI (Single Image-Normalized Difference Vegetation Index), diseased plants can be recognized at 13 days before the symptoms are visible. Furthermore, Dey, Sharma, & Meshram (2016) explained that digital image analysis techniques based on trichromatic colors, namely red, green, and blue (RGB), can be used to measure leaf chlorophyll as an important indicator of plant health index.…”