In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images can be transformed into diseased crop images. To improve the accuracy of the generated data, the category activation mapping attention mechanism is integrated into the original CycleGAN generator and discriminator, and a feature recombination loss function is constructed in the discriminator. In addition, the minimum absolute error is used to calculate the differences between the hidden layer feature representations, and backpropagation is employed to enhance the contour information of the generated images. To demonstrate the effectiveness of this method, the improved CycleGAN algorithm is used to transform healthy maize leaf images. Evaluation metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Fréchet inception distance (FID), and grayscale histogram can prove that the obtained maize leaf disease images perform better in terms of background and detail preservation. Furthermore, using this method, the original CycleGAN method, and the Pix2Pix method, the dataset is expanded, and a recognition network is used to perform classification tasks on different datasets. The dataset generated by this method achieves the best performance in the classification tasks, with an average accuracy rate of over $91\%$. These experiments indicate the feasibility of this model in generating high-quality maize disease leaf images. It not only addresses the limitation of existing maize disease datasets but also improves the accuracy of maize disease recognition in small-sample maize leaf disease classification tasks.

To improve its cleaning performance, the peanut sheller’s air–screen cleaning device was used as the research object, and a numerical simulation was carried out. A scheme was proposed to optimize the installation angle of the screen surface of the air–screen cleaning device, and the influence of the airflow velocity of the sieve surface before and after the structural improvement of the machine on the airflow distribution of the whole chamber was evaluated. At the same time, the computational fluid dynamics–discrete element method (CFD–DEM) of coupling simulation was used to carry out single-factor simulation tests on the improved air–screen cleaning device, analyze the movement trajectory, velocity, and displacement of the extrudates in the cleaning chamber, characterize the separation law of extrudates, and determine the parameter ranges for each factor. In addition, based on the central composite Box–Behnken design, an orthogonal simulation test was carried out with three factors, including fan speed, amplitude, and vibration frequency, and the influence of each factor on the kernel loss rate and impurity rate was investigated. Furthermore, the influencing factors were optimized, and the optimal parameter combination was obtained; when the fan speed was 1682.72 r/min, the amplitude was 3.74 mm, and the vibration frequency was 492.86 Hz, it was more conducive to the cleaning of kernels. Finally, the accuracy of the simulation and optimization methods was verified via the machine test, and the optimal parameter combination was obtained: at a fan speed of 1680 r/min, an amplitude of 3.7 mm and a vibration frequency of 490 Hz were achieved, and the kernel loss rate and impurity rate were 2.01% and 2.42%, 3.71 and 4.42 percentage points lower than those before optimization, respectively.