Tomatoes are a critical economic crop. The realization of tomato harvesting automation is of great significance in solving the labor shortage and improving the efficiency of the current harvesting operation. Accurate recognition of fruits is the key to realizing automated harvesting. Harvesting fruit at optimum ripeness ensures the highest nutrient content, flavor and market value levels, thus maximizing economic benefits. Owing to foliage and non-target fruits obstructing target fruits, as well as the alteration in color due to light, there is currently a low recognition rate and missed detection. We take the greenhouse tomato as the object of research. This paper proposes a tomato recognition model based on the improved YOLOv8 architecture to adapt to detecting tomato fruits in complex situations. First, to improve the model’s sensitivity to local features, we introduced an LSKA (Large Separable Kernel Attention) attention mechanism to aggregate feature information from different locations for better feature extraction. Secondly, to provide a higher quality upsampling effect, the ultra-lightweight and efficient dynamic upsampler Dysample (an ultra-lightweight and efficient dynamic upsampler) replaced the traditional nearest neighbor interpolation methods, which improves the overall performance of YOLOv8. Subsequently, the Inner-IoU function replaced the original CIoU loss function to hasten bounding box regression and raise model detection performance. Finally, the model test comparison was conducted on the self-built dataset, and the test results show that the mAP0.5 of the YOLOv8-Tomato model reached 99.4% and the recall rate reached 99.0%, which exceeds the original YOLOv8 model detection effect. Compared with faster R-CNN, SSD, YOLOv3-tiny, YOLOv5, and YOLOv8 models, the average accuracy is 7.5%, 11.6%, 8.6%, 3.3%, and 0.6% higher, respectively. This study demonstrates the model’s capacity to efficiently and accurately recognize tomatoes in unstructured growing environments, providing a technical reference for automated tomato harvesting.

Intelligent agricultural machinery refers to machinery that can independently complete tasks in the field, which has great significance for the transformation of agricultural modernization. However, most of the existing research on intelligent agricultural machinery is limited to unilateral research on positioning, planning, and control, and has not organically combined the three to form a fully functional intelligent agricultural machinery system. Based on this, this article has developed an intelligent agricultural machinery system that integrates positioning, planning, and control. In response to the problem of large positioning errors in the large range of plane anchoring longitude and latitude, this article integrates geographic factors such as ellipsoid ratio, long and short axis radius, and altitude into coordinate transformation, and combines RTK/INS integrated inertial navigation to achieve precise positioning of the entire vehicle over a large range. In response to the problem that existing full-coverage path planning algorithms only focus on job coverage as the optimization objective and cannot achieve path optimization, this paper proposes a multi-objective function-coupled full-coverage path planning algorithm that integrates three optimization objectives: job coverage, job path length, and job path quantity. This algorithm achieves optimal path planning while ensuring job coverage. As the existing pure pursuit algorithm is not suitable for the motion control of tracked mobile machinery, this paper reconstructs the existing pure pursuit algorithm based on the Kinematics characteristics of tracked mobile machinery, and adds a linear interpolation module, so that the actual tracking path points of motion control are always ideal tracking path points, effectively improving the motion control accuracy and control stability. Finally, the feasibility of the intelligent agricultural machinery system was demonstrated through corresponding simulation and actual vehicle experiments. This intelligent agricultural machinery system can cooperate with various operating tools and independently complete the vast majority of agricultural production activities.

Energy conservation and emission reduction of construction machinery are the focus of current research. The traditional excavator, whose hydraulic pump is driven by the engine, has high fuel consumption and emissions. Furthermore, it is difficult to match the working point of the engine to that of the hydraulic pump. Current pure electric drive technology has the advantages of zero pollution and low noise, and the motor used has the advantages of fast response and a wide speed range. Based on the characteristics of the pure electric drive technology, a positive flow system based on variable speed constant displacement instead of a variable displacement pump for pure electric construction machinery is put forward to realize the flow-matching of the whole machine. The basic structure and working principle were introduced. The control process was analyzed. The controllability and energy saving of the proposed system were tested through simulation and experimental analysis. The research results showed that the controllability of the proposed positive flow system was comparable to that of the traditional throttling speed-regulating control system. The energy-saving efficiency of the proposed positive flow system is increased by 35.2% compared to that of the tradition control system. To further exploit the strong overload capacity of electric motors of electric construction machinery and solve the insufficient power under sudden load, research on constant power control will be carried out in the future.

The loader has a lot of recoverable braking energy due to its larger mass and frequent starts/stops. For a 5-ton pure electric drive loader, an emergency braking intention recognition strategy based on hydraulic braking pressure was proposed. The braking intention recognition strategy of an acceleration pedal and brake pedal was used to distinguish different braking intentions, and the hydraulic braking system pressure was used as a feedback parameter for emergency braking intention recognition to improve braking safety. Aiming at electro-hydraulic composite braking mode switching, a collaborative control strategy of walking regenerative braking and mechanical braking is proposed. Simulation analysis by AMESim and vehicle test results show that the proposed control strategy can realize driver braking intention recognition and electro-hydraulic braking force distribution under different working conditions and improve braking smoothness. According to the calculation of the energy recovery effect evaluation index, the energy recovery efficiency is up to 71.64%, the braking recovery rate is above 42.50%, and the maximum energy saving for the whole vehicle is 7.58% under one cycle condition. The proposed strategy has a good energy-saving effect.

Heavy forklifts that are widely used in ports and stations have large gravitational potential energy at the lowering of the boom. As concerning the large rated power level, the engine is still the main power source for the heavy forklifts. With the increasingly stringent emissions regulations, the electric heavy forklift becomes an important choice. The structure and the working mode of an electric heavy forklift are introduced. Additionally the schematic of double hydraulic motor-generators is adopted to regenerate the potential energy when the boom is descending. The judge rule of the working mode and control strategy are analyzed. A test rig of a prototype electric heavy forklift is established. Control mode discrimination of potential energy regeneration, the control performance and the influence factors on regeneration efficiency are tested based on the test rig. The experimental results show that the discrimination method of the working mode of the proposed double hydraulic motor-generators with a potential energy regeneration system for potential energy is feasible. The descending of the lifting cylinder is consistent with the handle. The forklift can obtain the good following ability. Although the lifting cylinder descends at low velocity, the velocity is stable and the fluctuation of the rodless chamber pressure is within 0.1 MPa. With the increase of the load mass and descending velocity, the regeneration efficiency increases accordingly. The maximum efficiency is up to 74%. Hence, the proposed potential energy regeneration system is feasible and potential energy regeneration system does not affect the control performance of the boom.

As a fast, high-accuracy and non-contact method, chromatic confocal microscopy is widely used in micro dimensional measurement. In this area, thickness measurement for transparent specimen is one of the typical applications. In conventional coaxial illumination mode, both the illumination and imaging axes are perpendicular to the test specimen. At the same time, there are also geometric measurement limitations in conventional mode. When measuring high-transparency specimen, the energy efficiency will be quite low, and the reflection will be very weak. This limitation will significantly affect the signal-to-noise ratio. The inclined illumination mode is a good solution to overcome this bottleneck, but the thickness results may vary at different axial positions of the sample. In this paper, an error correction method for thickness measurement of transparent samples is proposed. In the authors’ work, the error correction model was analyzed and simulated, and the influence caused by the different axial positions of sample could be theoretically eliminated. The experimental results showed that the thickness measurement of the samples was practically usable, and the measurement errors were significantly reduced by less than 2.12%, as compared to the uncorrected system. With this error correction model, the standard deviation had decreased significantly, and the axial measurement accuracy of the system can reach the micron level. Additionally, this model has the same correction effect on the samples with different refractive indexes. Therefore, the system can realize the requirement of measurement at different axial positions.