基于MOPSO和TOPSIS的多目标优化温室黄瓜光环境调控模型

针对在现有温室光环境调控模型中，未考虑光质-光强的协同影响以及净光合速率与光能利用率双优化的问题。该研究面向温室黄瓜的高效补光，提出一种基于多目标优化思想的光质-光强协同调控方法。通过设计多因子耦合的净光合速率试验，获取叶片的净光合速率数据，建立净光合速率模型，并计算叶片尺度的光能利用率；构建光能利用率与净光合速率双优化的多目标优化模型，利用多目标粒子群算法获取非劣解集，基于理想解逼近算法得到光质-光强的调控单点，从而建立设施黄瓜红、蓝光模型。理论验证试验表明，与光合最大补光法相比，净光合速率降低21.39%，需光量降低59.40%；与固定光质0.5和0.8相比，在相同光强下净光合速率依次提升3.66%和9.69%。在此基础上开展实际验证试验，结果表明与固定光质法相比，在耗电量相近的前提下，生理指标均优于固定光质补光法，且在茎粗、干质量以及壮苗指数上存在显著差异；与光合最大补光法相比，生理指标不存在显著差异，且耗电量节省27.43%，表明本研究方法在保证生理指标高水平的前提下，有效节省了光电资源的消耗。本研究方法为设施农业调控提供了新型补光策略，保障了农业生产资源的高效利用。

Multi-objective optimization of the light environment regulation model for greenhouse cucumber using MOPSO and TOPSIS

Automated and intelligent light supplement systems have been widely applied in greenhouses in recent years. Among them, the light environment regulation model can be the core content of the system. However, the existing models cannot consider the comprehensive influence of light quality and light intensity, as well as the double optimization of net photosynthetic rate and light use efficiency. In this study, a collaborative control method was proposed for the light quality and light intensity using multi-objective optimization, particularly for the efficient supplemental illumination of cucumbers in greenhouses. Firstly, a multi-factor coupled photosynthetic experiment was designed to obtain the net photosynthetic rate of cucumber leaves. The model of net photosynthetic rate was then established using support vector regression with temperature, carbon dioxide concentration, photosynthetic photon flux density and light quality ratio as the input, while the net photosynthetic rate as the output. Furthermore, the light use efficiency was calculated at the leaf scale, according to the definition. Secondly, a multi-objective optimization model was constructed with the light use efficiency and net photosynthetic rate as optimization targets, while the light quality and light intensity as control variables. The non-inferior solution set was solved using the multi-objective particle swarm optimization. Technique for order preference by similarity to ideal solution was used to select the control single point of light quality and light intensity, in order to narrow the regulation interval for the less subjectivity of manual selection. Finally, the red and blue light demand were calculated according to the multiple relationship of light quality and light intensity. And then the red and blue light models were fitted by support vector regression with the temperature and carbon dioxide concentration as the input. The control experiments were carried out to compare with the fixed light quality supplement and the photosynthetic maximum supplement, in order to verify the superiority. The theoretical verification experiment showed that the net photosynthetic rate decreased by 21.39%, whereas, the light demand decreased by 59.40%, compared with the photosynthetic maximum supplement. The net photosynthetic rate increased by 3.66% and 9.69%, respectively, compared with the fixed light quality of 0.5 and 0.8. The practical verification experiment was also carried out to further verify the energy efficiency. The results showed that the physiological indicators were better than the fixed light quality supplement under similar power consumption, indicating significant differences in the stem diameter, dry weight and strong seedling index. There was no significant difference in physiological indicators, but the power consumption decreased by 27.43%, compared with the photosynthetic maximum supplement. The consumption of light and electrical energy resources was effectively saved to keep the physiological indicators almost unchanged. The model construction can be expected to serve as the new perspective for the greenhouse light supplement. This study can provide a new light supplement strategy for the facility''s agricultural regulation and the efficient utilization of agricultural production resources.