Optical measuring system for water stress indication of tomato plants

An optical plant wellness and stress measuring system was developed based on the visual appearances of plants. Machine vision was used to quantify plants’ wellness from side projected canopy images. A developed image analysing program calculated plants’ leaf inclination state expressed in degrees, plant height and vertical centre of gravity at each hour during the experiments. To prevent stress and to ensure the wellness of the plants, a tolerable wilting rate can be set by the user. An irrigation pump was turned on automatically by the machine vision system to water the plants when the wilting rate was not tolerable. The image analyzing algorithm has several input files including preset parameters about the camera position, plants’ stem and leaf parameters. These values can be changed for different plants cameras or measurements. Leaf inclination was measured by calculating local orientations expressed in degrees at each point on the visible leaf’s edge lines. The leaves and stems on the image were separated according to the differences in their shape. Stems were defined as all the branches and petioles on the plant. ‘Leaf and stem’ direction was calculated from the angle of edge lines of all visible parts of the plant canopy, including leaves, petioles and branches. From the leaves’ edge point directions a general leaf inclination value was estimated. The leaf inclination is given in degrees between 0 to -90, as the angle between the horizontal direction and that of the leaf direction. The optical monitoring system measures other shape, colour and size parameters of plant growth in vivo and in situ; continuously calculating the desired graphs and triggers actions according to parameters set by the user. INTRODUCTION Greenhouse production can be very competent in warm and sunny areas, and some of these places are also dry and sufficient irrigation is not affordable. Irrigating the plants more than they need to maintain optimal physiological functions can result in economical and environmental difficulties. Wasting drink water and inefficient production can cause further problems in local communities. To make greenhouse production more sustainable in dry and semi-dry areas water management should be greatly improved. Maintaining optimal growth conditions at all times is important in preventing infection and loss in yield. Therefore it is important to improve irrigation scheduling. In the last two decades several research experiments investigated the relationship between plant water status and visual appearance. Soybean leaves were studied by Oosterhuis et al. (1985) and found that soybean leaf-stem angle became smaller as the leaf water potential and soil water potential declined. Seginer et al. (1992) pointed out the relationship among tomato leaf tips and water potential and reported that the movement can be used as indicator of incipient drought stress before the appearance of visual wilt symptoms. In the experiment they moved a camera in front of tomato plants to acquire images and analyze them. The machine vision system, with human help in finding the leaf tip, was capable of irrigating tomatoes. Kurata and Yan (1996) used the mean angle of lines extracted with shrinking algorithm from reflecting rachises of tomato canopy to estimate plant water status. Another machine vision system based water status estimation of plants was presented by Kuruta and Yan (1996). They took images not from a single plant but from the canopy of more plants, and analyzed the average water status with a Proc. IC on Greensys Eds.: G. van Straten et al. Acta Hort. 691, ISHS 2005 782 method based on reflecting inclinations of rachises of tomato plants. The camera was set slightly above the canopy. The average incline of the extracted lines correlated well with water potential of tomato plants. This algorithm can be implemented in an automated irrigation system and irresistible for shadows of structural members of a greenhouse. This work aims to present a machine vision based irrigation system that was developed for measuring wilting at the Faculty of Physics and Process Control, Szent István University, Gödöllő, Hungary. The machine vision system includes a video camera connected to a PC, and an image analyzing software. The side projected canopy images, displaying the plant conditions, are acquired by a digital camera at user defined time scales and the PC stores and analyses them. The image analyzing program measures in vivo and in situ the growth of the plant and continuously calculates the shape and size characteristic of the canopy, such as the leaf inclination. MATERIAL AND METHODS A model was created for estimating plant water status from side projected canopy images. The camera was set in front of the plants, where the subdued bottom leaves can be easily observed and analyzed. Besides other values, the developed algorithm calculates the following parameters (Figs. 1-3): 1. Plant height [mm] 2. Leaf and stem inclination [°] as the visual sign of wilting 3. Vertical centre of gravity of the plant [mm] In the tomato experiment a machine vision based irrigation method was applied to water the tomato plant in a 1m x 2m x 1.5m high indoor model greenhouse made of glass. During the experiment a photoperiod (light/dark) of 14/10 hours was applied and in the course of taking images in the night an additional light was switched on. Tomato plants were about 80 cm in height. A digital camera was set in front of the plants about 50 cm apart, so that the images did not contain the whole canopy of the tomato. In the paprika experiment 3 paprika plants were set in the same model greenhouse. Young paprika plants were about 17 cm in height. The camera was set in font of the plants about 50 cm apart, so that the images contained the whole canopy of paprika. The camera was connected to a PC where the images were stored and analysed by a self developed program compiled in C++. All of the input parameters of the image analysing program are set by the user, depending what kind of canopy is monitored. Canopy parameters, the scale of the image, the area of interest, the distance and the scanning time can be set in the input file of the image analysing program. The same input parameters were used in both experiments, except for the area of interest. After analysing the image, the program creates a result file which can be read by Excel or a self developed result viewer program. The image analysing algorithm is based on a local directionality estimating algorithm by Tamura et al. (1978). The algorithm looks for edge points on the image, calculates their direction and graylevel value. After thresholding the image with the average edge point graylevel value, the plant points are segmented and so the vertical centre of gravity and the maximum height can be counted. The stem and leaf inclination value is processed from the direction data of the Tamura method. The irrigation system used for the experiment can be seen in Figure 4. A relay board controlled by the PC printer port turned an irrigation pump on and off to water the plants. Paprika plants were irrigated with about 100 gram of water at the same time when they have not received any water in the past hour and the estimated leaf and stem inclination value of the selected canopy dropped below -51°. In the tomato experiment the amount of irrigation was set to 200 gram and the critical wilting rate -48°. After measuring the inclination value in the selected canopy area, a program triggered the water pump on if wilting occurred. Besides leaf inclination, plant height, vertical centre of gravity, the optical monitoring system measures other colour and morphological parameters of plant growth in vivo and in situ and continuously calculates the desired graphs.