Study On The Method Of Monitoring Leaf Characteristic For LED Vegetable Growth Cabinet

With the continuous improvement of the quality of modern life and the consumptionlevels, consumers and producers are more and more requirements for the quality ofvegetables. Edible value of vegetables mainly manifests in leaf. Leaf is one of thelarger organs on the green plant especially on the leafy vegetables, so it has animportant role in the synthesis of organic compounds for the plant and in theregulation of water balance. Extraction of leaf characteristic parameters has importantsignificance for the rational adjust to the allocation of resources on growthenvironment, and promote rapid and healthy growth of vegetables. Development andmaturation of image technology created a powerful research conditions for the leaffeature extraction. Compared with traditional in vitro methods of measurement, it isspeed, flexibility and high accuracy on the non-destructive measurement. So, thispaper used the digital image processing method to start the relevant research aroundthe leaf characteristics extraction in LED vegetable growth cabinet.The main content of the subject is aimed at the research of leaf morphological featureextraction and maturity monitoring in LED vegetable growth cabinet by the imageprocessing method. In the aspect of morphological feature extraction, it first collectedthe leaf image in standard background plate with known dimensions, and then dividedthe image with threshold after the pretreatment of filtering. It used the method ofthreshold segmentation and mathematical morphology and the method of thresholdsegmentation and cuckoo algorithm to achieve effective segmentation of leaf for theeaves of different maturity and healthy state. Next, it got the boundary extraction andanalyzed the characteristics to measure leaf area, perimeter, length, width andother morphological character data by using the pixel statistics, chaincode scanning method and minimum circumscribed rectangle method. It can get realdata by proportional conversion of reference dimensions. In monitoring the maturity,it combined with the characteristics of root and leaf characteristics on the basis ofimage processing, and built neural network model using characteristic data of upperand lower parts, and made the information integration to improve maturity monitoringaccuracy. Finally, it designed the interface to monitor leaf characteristics of LEDvegetable growth cabinet by using GUI functionality of MATLAB software, whichcan realize the visual function of automatic measurement of leaf image characteristicsand enable managers to facilitate accurate extract leaf characteristics to master the growth of vegetables, and enable to create conditions of intelligent control for theLED vegetables growth cabinet.The test environment selected in the LED vegetable growth cabinet with independentresearch and development in the College of automation and electrical engineering,Tianjin university of technology and education. The results show that, using thedigital image processing method can accurately extract the leaf characteristics invegetable growth process of implementation. The method proposed in this paper tomonitor leaf characteristics and the user Interface have the advantages of highaccuracy, simple operation, convenient and flexible. It is convenient for the managersof LED vegetable growth cabinet to accurately judge the growth of vegetables.It has an important significance for reasonable adjust crop growth allocation ofenvironmental resources, the realization of scientific breeding and cultivation, diseaseprevention and effective pest control to ensure high quality and high yield of crops.