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Controlled environment agriculture offers a sustainable and efficient solution to meeting humanity’s growing demand for food and agricultural products [...]

The labor shortage and rising costs in the greenhouse industry have driven the development of automation, with the core of autonomous operations being positioning and navigation technology. However, precise positioning in complex greenhouse environments and narrow aisles poses challenges to localization technologies. This study proposes a multi-sensor fusion positioning and navigation robot based on ultra-wideband (UWB), an inertial measurement unit (IMU), odometry (ODOM), and a laser rangefinder (RF). The system introduces a confidence optimization algorithm based on weakening non-line-of-sight (NLOS) for UWB positioning, obtaining calibrated UWB positioning results, which are then used as a baseline to correct the positioning errors generated by the IMU and ODOM. The extended Kalman filter (EKF) algorithm is employed to fuse multi-sensor data. To validate the feasibility of the system, experiments were conducted in a Chinese solar greenhouse. The results show that the proposed NLOS confidence optimization algorithm significantly improves UWB positioning accuracy by 60.05%. At a speed of 0.1 m/s, the root mean square error (RMSE) for lateral deviation is 0.038 m and for course deviation is 4.030°. This study provides a new approach for greenhouse positioning and navigation technology, achieving precise positioning and navigation in complex commercial greenhouse environments and narrow aisles, thereby laying a foundation for the intelligent development of greenhouses.

As a promising environmental protection technology, the application of ultraviolet B irradiation in vegetable production has been widely considered. However, the effect of UVB irradiation varies with different plant varieties. In this study, we investigated the effects of two UVB intensities (0.7, 1.4 W m−2) on the accumulation of phenolics and flavonoids, and antioxidant activity of green-leaf and red-leaf lettuce (Lactuca sativa L.) 7 days prior to harvest. The results indicated that short-term (within 2 days) UVB treatment could promote the increase in total chlorophyll content of red-leaf lettuce and green-leaf lettuce, which increased by 49.8% and 20.6% compared with day zero, respectively, and was beneficial to the synthesis of carotenoids of red-leaf lettuce. Extending UVB exposure time significantly decreased chlorophyll a/b value of green-leaf lettuce from 0.92 to 0.63, and simultaneously increased the accumulation of antioxidant substances such as flavonoids, which were increased by 90.0% and 183.4% compared with day zero for UVB-0.7 and UVB-1.4 treatments of red-leaf lettuce, 84.1% and 110.9% of green-leaf lettuce. In contrast, red-leaf lettuce had a higher accumulation level of secondary metabolites, faster scavenging rate of free radicals, and stronger ability to resist UVB stress. Our results suggest that supplementation of low-dose UVB radiance prior to harvest can improve the secondary metabolite content and antioxidant activity of the two kinds of lettuce. This research provided a theoretical basis for improving lettuce quality by pre-harvest UVB treatment in controlled environmental agriculture.

Deciphering the various types of interactions between plants and their microbiomes is a hot topic for research in ecology as well as in plant sciences and agronomy. To analyse and compare the differences in microbial communities in different compartments of Chinese chives, high-throughput sequencing technology was employed to amplify and sequence the V5-V6 region of the 16S rDNA of microorganisms in the leaves, phylloplanes, stems, roots and rhizospheres of Chinese chives. The sequences were clustered by operational taxonomic units (OTUs), and the community composition of bacteria between the endosphere (inner tissues) and ectosphere (outer surfaces) of Chinese chives was analysed based on the OTU. Overall, the results indicated that the endophytic bacteria in Chinese chives mainly include Proteobacteria, Actinobacteria, and Actinomycetes. Alpha diversity index analysis and OTU number analysis showed that the bacterial diversity and richness of the underground plant compartments were higher than those of the above-ground parts. PCoA based on the OTU level showed that the vertical stratification structure of plants and compartments had significant effects on the bacterial community structure. The richness of endophytic bacteria also varied greatly among the different varieties of Chinese chive. A considerable number of endophytic bacteria form symbiotic and mutually beneficial relationships with host plants, which play an important role in regulating host growth, metabolism and stress resistance. Further investigations are needed to uncover the evolution of interactions between plants and endophytes.

Transplanting is a critical step in vegetable production, and the application of automatic transplanters can significantly reduce labor intensity, improve production efficiency, and enhance the precision and consistency of operations. However, automatic transplanters are structurally complex, with diverse components, each design and function offering its own advantages and limitations. To assist industry professionals in quickly understanding and selecting transplanters suited to specific crops and environments, this paper reviews three key technologies in current vegetable transplanters: planting mechanisms, automated seedling picking and placing, and tray conveyance. Each technology is classified, compared, and analyzed to evaluate its applicability. Based on the current state of technology, the paper identifies major challenges in the development of vegetable transplanters in China, including insufficient integration of machinery and agronomy, high demands for equipment adaptability, lack of standardized systems, and delays in the development of core technologies for fully automated transplanting. Solutions are proposed for each of these issues. Finally, the paper discusses future directions for the development of automatic transplanters, including enhancing transplanting efficiency, achieving autonomous navigation, digitalizing operations, developing supporting systems for transplanting, and unmanned transplanting.

Insulated plastic greenhouses (IPG) were a new type of facility emerging in production in China. The mechanism of construct parameters on the indoor solar radiation deserves further study. A mathematical model that can well describe the spatial and temporal distribution of solar radiation in the greenhouse was established. Based on this model, the effects of multiple construct parameters, such as insulation blanket shading, height–span ratio, greenhouse azimuth, and geographical latitude, on the indoor solar radiation of IPG were quantitatively specified. The results showed that the spatial and temporal distribution of inside solar radiation was highly variable along the span direction of IPG. And by analyzing the simulation data, it was found that for different heights of greenhouses, the corresponding optimal span is different. Therefore, in the construction of greenhouses, the appropriate height–span ratio should be selected according to the planting demand. And by simulating the inside solar radiation under different greenhouse azimuth angles, it was found that accumulated daily solar radiation in the greenhouse reaches the optimum value when the greenhouse azimuth angle is in the range of 0–20°. This paper can provide theoretical guidance for the design and optimization of structure parameters of IPG in China.

Due to the heavy computation load of closed-loop simulations, optimal control of greenhouse climate is usually simulated in an open-loop form to produce control strategies and profit indicators. Open-loop simulations assume the model, measurements, and predictions to be perfect, resulting in too-idealistic indicators. The method of two-time-scale decomposition reduces the computation load, thus facilitating the online implementation of optimal control algorithms. However, the computation time of nonlinear dynamic programming is seldom considered in closed-loop simulations. This paper develops a two-time-scale decomposed closed-loop optimal control algorithm that involves the computation time. The obtained simulation results are closer to reality since it considers the time delay in the implementation. With this algorithm, optimal control of Venlo greenhouse lettuce cultivation is investigated in Lhasa. Results show that compared with open-loop simulations, the corrections in yield and profit indicators can be up to 2.38 kg m−2 and 11.01 CNY m−2, respectively, through closed-loop simulations without considering the computation time. When involving the time delay caused by the computation time, further corrections in yield and profit indicators can be up to 0.1 kg m−2 and 0.87 CNY m−2, respectively. These conservative indicators help investors make wiser decisions before cultivation. Moreover, control inputs and greenhouse climate states are within their bounds most of the time during closed-loop simulations. This verifies that the developed algorithm can be implemented in real time.

Supplying homogenous and suitable airflow schemes were explored in Chinese solar greenhouses, which had a positive impact on the crop yield and quality. This paper provided a multifunctional fan–coil unit system (FCU) to assist in circulating air. This system could collect the surplus heat of daytime air and release it to heat the greenhouse at nighttime. However, the main problem to be faced was the nonuniform airflow distributions. Thus, this paper aimed to optimize and analyze the placement strategy of the FCU system for a Chinese solar greenhouse using the numerical methodology. The computational fluid dynamics model was constructed to evaluate the effect of the FCU system on the airflow field and to uphold its validation. The complex structure of the FCU system was simplified to a fan model by fitting the pressure jump and the air velocity to enhance the practicality of the simulation model. Finally, the coefficient of variation was used to optimize four parameters: the tilt angle, swing angle, height above the ground, and shape of the outlet baffle. The effective disturbance velocity percentage was proposed as the evaluation index to improve the turbulence characteristics. The mean absolute error (MAE) between the measured and simulated values of the air velocity for the two planes was 0.06 m/s and 0.09 m/s, and the root mean square error (RMSE) was 0.08 m/s and 0.11 m/s. The simulated results showed that the coefficient of variation before optimization was 0.76, and the effective disturbance velocity percentages of the planes at 0.7 m and 1.0 m from the ground were 42.73% and 41.02%, respectively. After optimization, the coefficient of variation was reduced to 0.33, and the effective disturbance velocity percentages of the two planes increased to 58.68% and 43.73%, respectively. These results significantly improved the uniformity of the interior airflow field. This paper provides a reference for the design and installation of the FCU system.

Smart agricultural machinery is built upon traditional agricultural equipment, further integrating modern information technologies to achieve automation, precision, and intelligence in agricultural production. Currently, significant progress has been made in the autonomous operation and monitoring technologies of smart agricultural machinery in China. However, challenges remain, including poor adaptability to complex environments, high equipment costs, and issues with system implementation and standardization integration. To help industry professionals quickly understand the current state and promote the rapid development of smart agricultural machinery, this paper provides an overview of the key technologies related to autonomous operation and monitoring in China’s smart agricultural equipment. These technologies include environmental perception, positioning and navigation, autonomous operation and path planning, agricultural machinery status monitoring and fault diagnosis, and field operation monitoring. Each of these key technologies is discussed in depth with examples and analyses. On this basis, the paper analyzes the main challenges faced by the development of autonomous operation and monitoring technologies in China’s smart agricultural machinery sector. Furthermore, it explores the future directions for the development of autonomous operation and monitoring technologies in smart agricultural machinery. This research is of great importance for promoting the transition of China’s agricultural production towards automation and intelligence, improving agricultural production efficiency, and reducing reliance on human labor.