ISHS Acta Horticulturae 399: Greenhouse Environment Control and Automation
PHYSIOLOGICAL DIAGNOSIS OF TOMATO PLANTS GROWN IN HYDROPONIC CULTURE BY USING IMAGE ANALYSIS |
| Authors: | K. Hatou, H. Nonami, T. Fukuyama, Y. Hashimoto |
| Keywords: | artificial intelligence, computer network, diagnosis, hydroponic culture, image analysis, plant factory, tomato |
| Abstract:
Growth inhibition and physiological disorder of crops frequently appear in plants cultivated under hydroponic conditions. In order to automate a cultivation system, it is essential to develop a diagnosis system for detecting the physiological status of crops in artificial intelligence by using computers. Image analysis was incorporated into a program for physiological diagnosis. The program contains an expert system that was developed from routine cultivation practices and a physiological diagnosis system that exerts physiological reasoning by using image analysis. The program was tested in the cultivation of tomato plants (Lycopersicon esculentum Mill.) by using hydroponic culture in greenhouses. Images of fruits of tomato plants were taken by using a video camera connected to a computer. Growth rates, shapes and colors were analyzed from the images by the computer. Growth inhibition in leaves and stems was identified, and blossom-end rot in fruits was detected from the image analysis. When the physiological diagnosis system was combined with the expert system in artificial intelligence, growth inhibition in stems and blossom-end rot in fruits could be avoided in hydroponic culture. | |
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