Inflated Modified Kumaraswamy Regression Model for Invasive Plants Detection in NDVI Imagery

This study proposes the inflated modified Kumaraswamy (iMK) distribution, a flexible probability model defined on the unit interval [0,1]. It captures asymmetric behaviors while accommodating inflation at zero, one, or both boundaries, as commonly observed in normalized difference vegetation index (NDVI) data. Based on the iMK distribution, we develop a new regression model (iMKreg) suitable for double-bounded responses. From this model, we derive a detection tool for invasive plant species, particularly applicable to NDVI imagery. Model performance was evaluated using synthetic NDVI data, with further assessment of predictive accuracy and detection efficacy conducted on real-world measured NDVI image. The application to detecting black-grass(Alopecurus myosuroides)in wheat crops in southern Sweden shows that the iMKreg model outperforms both standard Gaussian-based linear regression and existing inflated Kumaraswamy regression models.