The objective of this research was to experimentally determine whether signs of water deficit stress in plants can be detected from changes in the absorbance spectra based on the relationships between absorbance spectra and physiological parameters.Absorbance of tomato leaves in the near-infrared (NIR) wavelengths (1 100 nm to 2 500 nm) was measured simultaneously with photosynthesis and stomatal conductance during water stress. To observe small changes in absorbance which arise from water stress, a leaf in non-stressed conditions was used as a reference for the absorbance measurements.In general, under the effects of water stress, peaks formed in the absorbance spectra at water absorbance bands near 1 440 nm and 1 940 nm, and absorbance decreased near 1 600 nm forming a valley.Although the relationship between the changes in absorbance and physiological parameters varied depending on the individual plants tested, consistent overall trends were observed, demonstrating there is potential to nondestructively detect changes in plant condition resulting from water stress by measuring NIR absorbance.

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A method is needed to detect water stress in greenhouse grown muskmelon plants (Cucumis melo L.) for irrigation management.Expert growers observe indicators such as color changes of the youngest leaf to judge the optimum level of stress ; i.e., time for irrigation.In this research, color changes in leaves of muskmelon plants under water stress were analyzed by machine vision to determine indicators of stress. Color images of the youngest leaf were taken under diffuse sunlight in 30-min intervals throughout the day with a still-video camera and then digitized into RGB images. The average green color normalized for luminance (termed chromaticity, g) was found for each leaf image, and two methods of analysis were considered. First, the green chromaticity of the youngest leaf for five plants was averaged and time course changes were examined. Second, the relationship between initial leaf color under non-stress conditions and the leaf color at the stress point judged by the expert grower was examined. Based on this relationship, a machine vision algorithm was developed to estimate the stress time. The second method showed greater potential for practical application.The difference in stress time judged by the expert and machine vision was approximately 30 min to 1 h for plants which reached stress point within 1 d.

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