Unmanned aerial vehicle (UAV) imagery provides an efficient approach for monitoring cotton defoliation and boll-opening rates. Deep learning, particularly convolutional neural networks (CNNs), has been widely applied in image processing and agricultural monitoring, achieving strong performance in tasks such as disease detection, weed recognition, and yield prediction. However, existing models often suffer from heavy computational costs and slow inference speed, limiting their real-time deployment in agricultural fields. To address this challenge, we propose a lightweight cotton maturity recognition model, RTCMNet (Real-time Cotton Monitoring Network). By incorporating a multi-scale convolutional attention (MSCA) module and an efficient feature fusion strategy, RTCMNet achieves high accuracy with substantially reduced computational complexity. A UAV dataset was constructed using images collected in Xinjiang, and the proposed model was benchmarked against several state-of-the-art networks. Experimental results demonstrate that RTCMNet achieves 0.96 and 0.92 accuracy on defoliation rate and boll-opening rate classification tasks, respectively. Meanwhile, it contains only 0.35 M parameters—94% fewer than DenseNet121—and only requires an inference time of 33 ms, representing a 97% reduction compared to DenseNet121. Field tests further confirm its real-time performance and robustness on UAV platforms. Overall, RTCMNet provides an efficient and low-cost solution for UAV-based cotton maturity monitoring, supporting the advancement of precision agriculture.

Aiming at the problems of low operating efficiency and the unclear mechanisms in the bundling process of existing residual film recycling machines, this paper designs a chain network-type residual film bundling device and analyzes the motion characteristics of the film bundling process using high-speed camera technology. A mechanical analysis of the bundling process was conducted, and a test rig for the chain network residual film bundling device was built. The bundling process was studied via a high-speed camera. Field tests were carried out with the density of the film bale as the evaluation indicator and the forward speed of the machine, the rotational speed of the active film-removing roller, and the rotational speed of the film-rolling support roller as influencing factors. A Box–Behnken experimental design was used to optimize the working parameters of the device. The results show that when the machine’s forward speed is 5.8 km/h, the active stripping roller rotates at 170 rpm, the roll support roller operates at 210 rpm, and the film bale density reaches 124.44 kg/m³, with a relative error of only 1.34 kg/m³ compared to the predicted value. This verifies the effectiveness of the device and demonstrates that it can meet the requirements of mechanized residual film recycling.

The aim of this study is to improve the film removal rate of a film removal device on a residual film recovery machine and mitigate the soil compaction caused by film removal operations during the residual film recovery process. We designed a bionic film-lifting shovel by applying the contour curve of the first claw of the North China mole cricket’s front foot to the soil-penetrating portion of the film-lifting tines. Based on agronomic requirements and mechanical analysis of the operation process, the biomimetic blade was developed to break up soil more effectively and lift the residual film more efficiently. The contour features were obtained using high-definition cameras, with the fitting equation guiding the design of the soil-penetrating structure. A three-dimensional model was constructed using SolidWorks. Tensile tests provided the physical parameters of the autumn residual film, enabling the creation of a finite element model using the Mohr–Coulomb yield criterion. Simulation comparisons showed that the biomimetic shovel teeth reduced the operating resistance by 9.3% compared to conventional teeth. Soil trench experiments validated these results, demonstrating a 4.24% higher film-lifting rate and average resistance of 411.49 N for the bionic shovel versus 454.70 N for the conventional one. The close match between the experimental and simulation results confirms the effectiveness of the bionic design in meeting the resistance reduction requirements.

The compression deformation of seed cotton has been identified as a key factor affecting the working reliability of the baling device and the quality of bale molding. However, due to the complex working conditions of seed cotton in the continuous compression process in a confined space, it has proven to be difficult to study the compression molding mechanism of machine-harvested seed cotton in the baling process. The present study employs a universal testing machine to compress the seed cotton. In addition, pressure sensors are utilised to ascertain the internal axial load transfer law of the seed cotton. Furthermore, the internal density distribution equation of the seed cotton is established. Moreover, the Fiber model is employed to establish a spatial helix structure model of the cotton fibre. Finally, the compression simulation test is conducted to calibrate its material parameters. The results of the study indicate that seed cotton exhibits hysteresis in its internal stress–strain transfer. Through the polynomial fitting of the compression–displacement curve, it has been demonstrated that as the seed cotton approaches the compressed side, the rate of change in compression increases. The internal density distribution of the seed cotton must be calculated when it is compressed to a density of 220 kg·m⁻³. It is found that the density of the upper layer of the seed cotton is slightly greater than that of the lower layer of the seed cotton. The density distribution equation must then be obtained through regression fitting. The parameters of the compression model must be calibrated by means of uniaxial compression tests. Finally, the density distribution equation of the cotton fibre must be obtained through the compression test. The parameters of the simulation model, as determined by the uniaxial compression test calibration, are of significant importance. This is particularly evident in the context of the Poisson’s ratio of cotton fibre and the cotton fibre elastic modulus under pressure. The regression equation was obtained through analysis of variance, and the simulation of contact parameter optimisation. The optimal parameter combination was determined to be 0.466, and the pressure at this time. The relative error was found to be 2.96%, and the compression of specific performance was determined to be 10.14%. These findings serve to validate the simulation model. The findings of this study have the potential to provide a theoretical foundation and simulation assistance for the design and optimisation of cotton picker baling devices.