Ideally, to achieve optimal production in agriculture, crop stress needs to be measured in real-time, and plant inputs managed in response. However, many important physiological responses like photosynthesis are difficult to measure, and current trade-offs between cost, robustness, and spatial measurement capacity of available plant sensors may prevent practical in-field application of most current sensing techniques. This paper investigates a novel application of laser speckle imaging of a plant leaf as a sensor with an aim, ultimately, to detect indicators of crop stress: changes to the dynamic properties of leaf topography on the scale of the wavelength of laser light. In our previous published work, an initial prototype of the laser speckle acquisition system specific for plant status measurements together with data processing algorithms were developed. In this paper, we report a new area based statistical method that improves robustness of the data processing against disturbances from various sources. Water and light responses of the laser speckle measurements from cabbage leaves taken by the developed apparatus are exhibited via growth chamber experiments. Experimental evidence indicates that the properties of the laser speckle patterns from a leaf are closely related to the physiological status of the leaf. This technology has the potential to be robust, cost effective, and relatively inexpensive to scale.