Constructing Stereoscopic Light Requirement Model Of Cucumber Under The Constraint Of Non-uniform Photosynthetic Characteristics

The yield and quality of crops are directly influenced by photosynthetic efficiency,and the intensity of which is closely related to light intensity,temperature and CO₂,as well as the photosynthetic characteristics of different leaf positions.Therefore,how to achieve photosynthetic optimal regulation of crops under multi-environmental interactions becomes the key to improve the photosynthetic capacity of crops.Artificial light-filling technology can effectively improve the light environment requirements for the growth of facility crops,thereby improving the photosynthetic capacity of crops.However,the traditional artificial light-filling technique takes the functional leaf position photosynthetic ability as the reference to make the canopy supplementing light,which leads to too much light supplement of the top new leaves,and finally causes photoinhibition or even photodamage.Functional leaf position between plants is insufficient for supplemental light due to cross-blocking of leaves and vertical attenuation of natural light,which can affect the yield and quality of crops.Thus,this paper takes cucumber as research object to study the method of obtaining the target light intensity of the top leaf photoinhibition point,of obtaining the target value of the suitable leaf position range between plants,and of the verification of the crop stereo environment optimization control system,so as to provide new ideas and new methods for optimizing the regulation of crop light environment.?1?To study the method for obtaining target value of light environment regulation of top new leaves based on photosynthesis and fluorescence characteristics.Taking the facility cucumber as the test object,the photosynthesis rate of different leaf positions,light intensity,temperature,carbon dioxide concentration and other multi-environment factors as the experimental samples,and BP neural network optimized by particle swarm optimization was used to construct a photosynthetic rate prediction model for cucumber.Based on the photosynthetic rate prediction model,we used the particle swarm optimization model and support vector regression?PSO-SVR?to get the top new leaf light requirement model.The different calibration analysis shows that the coefficient of determination of the top new leaf light requirement model is 0.9993,the root mean square error is 1.614?mol·m^-2·s^-1,the slope of the line is 0.9982,and the intercept is 1.2,which prove that the prediction model has a good performance.Based on the light model,the reversible inactivation fluorescence experiment was designed to obtain the target light intensity of the photoinhibition point,and the threshold control model of the photoinhibition point shows that the coefficient of determination was0.9999,the root mean square error is 11.03?mol·m^-2·s^-1.It provides an efficient physiological model for the precise regulation of the crop top leaf light environment on demand.?2?To study the acquisition method of target value of light environment regulation in the suitable range between plants based on the difference of light demand of different leaf positions.Based on the difference of photosynthesis characteristics between different crop leaves,the photosynthetic rate prediction model is used as the fitness function,and the response curve between the leaf position and the maximum photosynthetic rate is obtained by using a multi-population genetic algorithm?MPGA?.Besides which,the author designs chlorophyll content measurement test at different leaf positions to obtain the response curve between leaf position and chlorophyll.Based on the above response curve analysis,the suitable leaf position range between the plants should be between 5 and 7.Relied on this range,the PSO-SVR algorithm is used to obtain the optimal environmental control model of the suitable leaf position range between the plants,and the different calibration analysis of the model yields a coefficient of determination of 0.9998,the root mean square error of 1.369?mol·m^-2·s^-1,and the slope of the line of 0.9954.?3?The stereoscopic light-requirement model verification of the facility cucumber based on the stereo light environment optimization control system.Based on the wireless sensor network,the stereo light-filling control system is used as the carrier,and the machine learning modeling method was adopted to realize the high-precision transplantation of the light-requirement model.The author designed stereo light-filling,canopy light-filling and natural control treatment verification test with a 90-day light-filling effect verification to verify photosynthetic capacity,chlorophyll content of different leaf positions,and crop height,diameter,flower and fruit,yield and quality.The results showed that the stereo light-filling was compared with the natural one:the photosynthetic capacity increased by 31.1%,the crop height increased by 27%,the crop diameter increased by 36%,the flower and fruit count increased by 95.4%,the chlorophyll content increased by 7.5%,and the average yield increased by 38.5%.The soluble solids increased by about 15.2%,the soluble sugar increased by about 20.4%,and the vitamin C increased by about 5.63%.Therefore,the precise construction of the stereo light-requirement model is of great significance for achieving high quantity and quality of facility crops.