Mechanical equipment is still lacking to clean the roofs of domestic Venlo-type greenhouses. The Manual cleaning can be time-consuming and labor-intensive so far. The existing solutions are relied excessively on imported equipment, resulting in high costs and maintenance expenses that significantly increased production costs. In this study, an electric roof cleaning machine was designed to automatically clean and switch the roofs of the Venlo-type greenhouses. A walking drive device was designed to ensure the consistent driving of the cleaning machine wheels, while reducing the wheel slippage on the damp tracks. A single motor was combined with a reducer and universal joint for the power transmission, in order to ensure the synchronous rotation of all four wheels. The resistance faced by the cleaning machine during movement was analyzed and calculated to determine the torque and power requirements for driving the motor. A uniform winding device was designed for uneven cable or water pipe winding during operation. The important parameters of the winding wheel were determined to accommodate the sufficient cable or water pipe length for each cleaning task. The winding and unwinding process of the uniform winding device was analyzed to clarify the impact of changes in the winding radius on the winding speed. The initial stage control of unwinding and the end stage of winding were optimized to prevent the excessive stretching of the cable or water pipe, thus avoiding a reduction in their lifespan. Reliable brushing was realized on the greenhouse roof using the cleaning roller brush. There was a contact process between the bristles and the roof surface. The roller brush fully met the design requirements to wash the roof surface under the conditions of 15 mm bristle deformation and a cleaning machine travel speed of 0.25 m/s. The torque resistance experienced by the roller brush was calculated to obtain the motor power required for the driving rotation of the roller brush. Some installation of switching tracks and positioning markers was carried out for Venlo-type connected greenhouses, in order to ensure the smooth progress of the switching operation. The rational design was achieved in the automatic water supply system for the auxiliary switching platform and the switching control system. A safety redundant control system was designed using multiple sensor perceptions for the cleaning machine. The various motor operations were controlled in an orderly manner, in order to collect the processing signals, such as the edge, departure detection, coiling, and skylight opening signals. A prototype was fabricated to conduct the test greenhouse, followed by performance testing of the prototype. The test results indicated that the cleaning machine achieved the maximum operating speed of 0.265 m/s, with a stop margin of 28.4 mm. The average switching time was 22.84 s, and the average alignment error between the platform and the roof track was 1.6 mm. An experiment was conducted to evaluate the cleaning effectiveness of the machine using the transmittance rate of the film as an indicator. The results showed that the transmittance rate increased to 86% after cleaning under the conditions of a travel speed of 0.25 m/s, roller brush speed of 120 r/min, and water pump flow rate of 34 L/min using a film with an initial transmittance rate of 68%. Better cleaning performance was achieved during operation. The cleaning machine can fully meet the cleaning requirements of Venlo-type greenhouse roofs. It is of significant importance in the indoor temperature and light environment, as well as the higher fruit yield and quality.

Chinese greenhouses have been widely used with planting areas exceeding 810,000 hm² in northern China, due to heat preservation, low investment, and energy saving. The plastic film of greenhouses cannot be washed by less rain in the northern region for an extended period. The accumulation of more dust can also reduce the light conditions in the shed, leading to adverse effects on the growth and development of crops. Consequently, it is very necessary for regular shed cleaning. In this study, a fully automatic cleaning machine of greenhouse film was proposed for the labor-saving and high efficiency. The machine included a cleaning host, floor and roof lateral shifting devices, as well as a control system. The cleaning host consisted of a bracket, brush, support wheel, climbing motor, brush motor, and water spray pipe. The roof lateral shifting device comprised a hanging rail, stainless steel pulley assembly, hanger, roof lateral shifting motor, polyurethane wheel, and control system. The ground lateral shifting device was composed of a flip frame, bearing bracket, rear axle assembly, water tank, and control system. A 433 MHz wireless module was selected to communicate between the floor and roof lateral shifting device conveyor over a distance of up to 200 m. A double worm gear motor was used to drive the brush and climbing shaft of the cleaning host, in order to reduce the weight of the entire machine for cost saving. A ground lateral shifting device was designed to carry the cleaning host along the length of the greenhouse. The roof lateral shifting device was designed for automatic line wrapping cleaning. The installation height of the roof lifting rope was determined to avoid interference during the lateral shifting operation, even damage to the film. Multi-sensor fusion and data verification were utilized for the real-time, consistency, and reliability of cooperation between the cleaning host, roof and ground lateral shifting device. A model greenhouse was then made to verify the rationality and feasibility of the design. The prototype parameters of span, ridge height, and shoulder height were used at the scaling ratio of 10:1. The prototype was then processed and cleaned, according to the external dimensions at 5:1. Alignment, tilt deviation, and cleaning tests were conducted to verify the performance. The results showed that the horizontal offset during the lifting and lowering of the cleaning host was within ±3°, the left and right offset was within ±7 mm, and there was no offset during the lateral shifting. There was approximately 1 mm in the single and cumulative lateral shifting errors of the floor and roof lateral shifting devices. The average light intensities were 107 232.38, 77 866.77, and 112 377.26 lx with/without the cleaning shed and the cleaning reference, respectively. The relative transmittance of the film increased from 69.3% to 95.4% after cleaning, indicating the well-designed brush material and key parameters of the machine. A remarkable cleaning performance was achieved on the surface of the film. The cleaning machine with the large weight in the early stage can also be updated for the high automation level, less film damage and cleaning blind zone. The findings can provide a strong reference for the design and development of film-cleaning equipment in the greenhouse.

Chrysanthemum tea has been one of the most popular food products, due to the health benefits and high commercial value. The medicinal and economic chrysanthemum can greatly vary in the different flowering stages. Among them, the flowering stages of chrysanthemum can be categorized into the Jumi (flower buds), Taiju (flower buds just before blooming), and Duohua (fully bloomed flowers). At the same time, the chrysanthemum is required for the best picking time. However, manual picking cannot fully meet the requirement of large-scale production at the early flowering stage, due mainly to the labor-intensive and time-consuming. Untimely picking or picking errors at the different flowering stages can also lead to the waste of chrysanthemum, even the serious economic losses. Therefore, the picking robot can be expected to realize the accurate and rapid recognition of chrysanthemum in different flowering stages using lightweight model. In this study, an improved YOLOv8s model (YOLOv8s-RDL) was proposed for the object detection of chrysanthemum. Firstly, the C2f (faster implementation of CSP bottleneck with 2 convolutions) in Neck network was replaced by RCS-OSA (one-shot aggregation of reparameterized convolution using channel shuffle). The features were extracted to more efficiently fuse by the Backbone layer; Secondly, the decoupled head was replaced with the Dyhead (dynamic head), and then integrated into the DCNv3 (deformable convolutional networks v3). The multi-head self-attention mechanism was combined to strengthen the expression of the target detection head. Finally, the LAMP (layer-adaptive magnitude-based pruning) was used to reduce the number of parameters and the complexity of the model network. The amount of calculation was significantly reduced to maintain a high mean average precision level of the improved model. A comparison was also made to explore the effect of RCS-OSA in the different positions of the network. The performance of the model was depended mainly on the different pruning conditions and rates in the same pruning direction. The best improved model was obtained after network structure improvement and pruning. The best solution was attributed to the replacement of the C2f with the RCS-OSA only in the Neck part, particularly in the pruning condition of the adaptation model. The ablation experiments show that the mean average accuracies of the improved model for Duohua, Taiju and Jumi were 99.0%, 97.7% and 96.3%, respectively, in the classification and detection of flowering stage, which were 0.3, 1.9 and 3.8 percentage points higher than that of YOLOv8s. The mean average precision, precision and recall of the YOLOv8s-RDL for the Jumi, Taiju, and Duohua were 97.7%, 96.5%, and 95.2%, respectively, which were 1.9, 5.2 and 6.4 percentage points higher than YOLOv8s baseline model, respectively. The size of model weight also decreased by 6 MB. The improved model was greatly reduced the number of parameters and weight size, indicating the high detection accuracy. The mean average accuracies of YOLOv8S-RDL were 35.3, 2.9, 3.4, 1.9, 3.8 and 1.7 percentage points higher than those of SSD, YOLOv5s, YOLOv6s, YOLOv8s, Ginger-YOLOv5s and MSC-YOLOv8, respectively. At the same time, there were the smallest weight size and parameters of the improved model. In addition, the superiority of the algorithm was verified to introduce the TIDE (toolkit for identifying detection and segmentation errors) indicator. The detection errors of classification and background were reduced by 0.55 and 1.26, respectively, in the YOLOv8s-RDL, compared with the YOLOv8s. The better performance of detection and classification was achieved to reduce the influence of background and interference factors. The improved model was also fully met the requirements of accurately and rapidly distinguish the chrysanthemum flowering stages. This finding can also provide the theoretical reference and technical support to realize the automatic picking of chrysanthemum in the various flowering period.