Study On The Technology For Testing The Soil Moisture Content Of Cucumber Seedlings In Greenhouse Based On Computer Image Processing

As industrialized agriculture technology progress rapidly, the automatization for cropraising is becoming more important. The nondestructive measurement and testing is avery hot agricultural production technology. Choosing greenhouse cucumber as theresearch object, cucumber leaves image feature was obtained using computer imageprocessing technology under natural illumination, and the growth information ofcucumber was analyzed, then realizing the moisture content of soil was tested, theresults could be used as a reference for precision irrigation, and provide scientific basisfor the latter part of the irrigation.Based on above goals, the following work was finished in this study:(1) Managing the plant according to conventional methods, the soil moisture contentwas set as five levels: 90%,80%,70%,60% and 50%. The amount of water wascontrolled with the±5% of soil moisture content. The images of the No.3 leaf wereacquired using digital camera.(2) The leaf images were used to pass through histogram correcting, smoothing andsharpening, and then segmented they were leaves and background. A suitable method,super-green, for segmenting leaves and complex background, was advanced undernatural illumination, and the maximal variance ratio subtract method which could chooseautomatically threshold was used to compute the threshold segmentation T, which waseffective by experiment.(3) Using computer image processing technology, color feature of leaf image wasobtained. There are both a high correlation between soil moisture content and colorfeatures r, H, by analyzing the linear correlation between soil moisture content and colorfeatures, which achieved confidence level (p=0.05), and then, there is a well relationshipbetween the range of r, H and soil moisture content. It is feasible that color feature r, Hwere selected as testing parameters of the testing system.(4) The three tiers BP neural network was designed between the testing parametersand soil moisture content. The network was trained by a reverse transmission algorithmof self adaptive learning rate with gradient reduction, after training the network, thecorrect recognition rate is 100% for training samples, it is 92.5%, 97.5%, 100%, 97.5%and 100% for treatment level 50% to 90% of the tested samples. The testing system wasprogrammed with the development platform of VC++.This study may provide a significant help to the greenhouse crop production bycomputer image processing technology, and ultimately to improve the intelligentmanagement level of industrialized agriculture.