An Improved Normalized Difference Vegetation Index (NDVI) Estimation Using Grounded Dino and Segment Anything Model for Plant Health Classification

Ensuring sustainable and profitable agriculture is critical for addressing global food security challenges. This has resulted in the need for automation in plant health identification. However, this objective is hampered by the lack of efficient image-processing methods and the need for extensive datasets for training deep learning models for plant disease diagnosis. To overcome the need for extensive training data, the proposed Localized Normalized Difference Vegetation Index (LNDVI) uses zero-shot plant detection models such as Grounded Dino and state-of-the-art methods for image segmentation such as Segment Anything Model (SAM) are leveraged. This also expands the capabilities of the system to diagnose plant health beyond known plant species available as part of training set. The proposed system uses synthetic Normalized Difference Vegetation Index (NDVI) to estimate the chlorophyll content of the plant through RGB images alone instead of using the combination of RGB and near Infra-red (nIR) bands used in contemporary works. Since NDVI value is greatly affected by the amount of light present while the image is captured, we also present an irradiation estimation metric that uses CIE XYZ (Tristimulus values), Hue, Saturation and Value (HSV) and CIE LAB color spaces as well as correlated color temperatures, which automatically normalizes the NDVI threshold for health classification of the image, enabling a more precise analysis of plant health. Using the Grounding Dino provided an accuracy of 99.994% in terms of detecting plants from the phenotyping dataset. The segmentation of plant region in images is reported using Intersection over Union (IoU). While using the Segment Anything Model (SAM), an average accuracy of 95.884% was obtained for clustered plants while the average accuracy was even better at 97.031% for individual plants. Significant differences were observed for plant health classification while using Localized Normalized Difference Vegetation Index (LNDVI) approach when compared to NDVI.