Creating a suitable growing environment is necessary to ensure good plant growth in a plant factory, which requires wireless sensor networks (WSNs) to monitor the environment in real time. However, existing WSN clustered routing methods hardly take into account the network unreliability caused by varying link quality among nodes, resulting in reduced stability and accuracy of environmental monitoring. This study proposes a wireless sensor network system strategy for improving network reliability in large-scale reliable wireless sensor networks suitable for plant factory scenarios. Firstly, a hybrid wireless sensor network was designed and built based on Wi-Fi and ZigBee communication protocols. Secondly, a nonlinear link quality prediction model for plant factory scenarios was developed using a function fitting method, taking into account the interference and attenuation caused by the dense concentration of agricultural facilities and plants in plant factories on the wireless signal propagation. Finally, a network coverage optimization scheme was designed by combining a particle swarm optimization (PSO) algorithm and link quality prediction model, and a reliable cluster routing protocol was designed by combining K-means algorithm. The results indicated that the coefficient of determination (R²) for the prediction model was 0.9962. The impact of agricultural facilities and vegetation on link quality was most significant when the node height was 0.7 m. Under the optimal node deployment, the number of nodes was 33, and the network coverage rate (CR) reached 97.512%. Compared with the traditional clustered routing method, the wireless sensor network designed in this study is more applicable to the field of plant factories; it further enhances data transmission effectiveness and link quality, improves the reliability of the network, and realizes the load balancing of the internal transmission of the network, which in turn ensures the accuracy of environmental monitoring and the stability of the system.

This study investigated the effects of far-red (FR, 740 nm) and ultraviolet-A (UV-A, 370 nm) supplementation on the growth and nutritional quality of pak choi (Brassica rapa ssp. chinensis ‘Xiawang’) under different light intensities. Plants were grown under a white LED background light (6500 K) at two light intensities (100 and 400 μmol·m⁻²·s⁻¹) either without (T100 and T400) or with an additional 40 μmol·m⁻²·s⁻¹ of FR or UV-A radiation (T100FR, T100UV-A, T400FR, and T400UV-A). The results demonstrated a significant interactive effect between light intensity and FR/UV-A supplementation. Under low light intensity (T100), both FR and UV-A significantly enhanced biomass accumulation, the net photosynthetic rate, and the content of soluble proteins, soluble sugars, and cellulose. Under a high light intensity (T400), FR and UV-A primarily promoted morphological development (leaf expansion and thickening), photosynthetic performance, and carbohydrate accumulation. Specifically, FR notably promoted petiole elongation while reducing photosynthetic pigment content, whereas UV-A markedly increased the vitamin C content while decreasing nitrate accumulation. In conclusion, both FR and UV-A supplementation effectively improve the growth and quality of pak choi, but, through light-intensity-dependent mechanisms. FR acts mainly via morphological and photosynthetic optimization, whereas UV-A acts as a physiological and nutritional regulator. These findings highlight the complementary roles of FR and UV-A, providing practical guidance for optimizing spectral strategies in controlled environment agriculture (CEA).