Is it possible to obtain NDVI from RGB images?
Author(s)
Date Issued
2025
Type
conferenceObject
Start Page
215
End Page
222
Journal
Abstract
The normalized difference vegetation index (NDVI) is the most widely used
metric for assessing the density and health of vegetation in terms of water stress,
nutritional deficiency, and plant disease occurrence for precision agriculture
purposes. Usually, it is extracted from images acquired by proximal and/or remote
sensing using hyperspectral and multispectral optical sensors which can often
represent an unaffordable expense for smallholders. NDVI is calculated as the
normalized difference between reflectance values at visible red and near-infrared
bands, thereby ranges from -1 to 1. In this study, a model based on a shallow-regressive
neural network (SNN) to predict NDVI from sRGB images was developed. The model
was trained on images of different plant species at different growing vegetation stages
acquired by a snapshot hyperspectral camera (Specim IQ), captured in natural
environmental light conditions, and then calibrated. The model performs a pixel-topixel regression and shows the calibrated RGB values have a strong correlation with
the NDVI, for the validation data set (r=0.91). The same shallow-regressive neural
network was tested on a set of data acquired with a different non-co-registered sensor.
Specifically, the application of the model to around 1000 drone images acquired by a
6X Sentera sensor still has good performances (r=0.71). To demonstrate the reliability
of the proposed method, k-means clustering (k=2) was applied to NDVI images to make
a comparison between observed and predicted results evaluating r2, SSIM, and
accuracy mean values. Therefore, this work shows the efficiency of the AI approach to
estimate vegetation index: for the first time crop conditions could be monitored by
computing NDVI using data from a low-cost RGB device. The application of this method
can be an important resource for small-sized farms and a first step to making this type
of technology accessible to improve production, reduce costs, and minimize
environmental impact.
Conference(s)
IV Int. Organic Fruit Symposium and II Int. Organic Vegetable Symposium