The primary goal of this paper is to develop Hyperspectral algorithms for early detection of a readout system used in conjunction with plants designed to de-green or discolor after detection of explosives, harmful chemicals, and environmental pollutants. Work in progress is aimed to develop a new class of biosensors or Plant Sentinels that can serve as inexpensive plant-based biological early-warning systems capable of detecting substances that are harmful to human or the environment [LoHe03]. The de-greening circuits in the laboratory plant, Arabidopsis, have been shown to induce rapid chlorophyll loss, thereby change color under the influence of synthetic estrogens. However, as of now, the bio de-greening phenomenon is detectable by human eyes or with a system (chlorophyll fluorescence) that works best in laboratory conditions. In order to make the plant sentinel system practically viable, we have developed automated monitoring scheme for early detection of the de-greening phenomenon. The automated detection capability would lead to practical applicability and wider usage. This paper presents novel and effective HSI-based algorithms for early detection of de-greening of plants and vegetation due to explosives or chemical agents. The image processing based automated degreening detector, presented in this paper will be capable of 24/7 monitoring of the plant sentinels and to detect minutest possible discoloration of the plant-sensors to serve as an early-warning system. We also present preliminary results on estimating the length of time that the explosive or chemical agent has been present.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.