Plant leaves can be used to effectively detect plant diseases. However, the number of images of unhealthy leaves collected from various plants is usually unbalanced. It is difficult to detect diseases using such an unbalanced dataset. We used DoubleGAN (a double generative adversarial network) to generate images of unhealthy plant leaves to balance such datasets. We proposed using DoubleGAN to generate high-resolution images of unhealthy leaves using fewer samples. DoubleGAN is divided into two stages. In stage 1, we used healthy leaves and unhealthy leaves as inputs. First, the healthy leaf images were used as inputs for the WGAN (Wasserstein generative adversarial network) to obtain the pretrained model. Then, unhealthy leaves were used for the pretrained model to generate 64*64 pixel images of unhealthy leaves. In stage 2, a superresolution generative adversarial network (SRGAN) was used to obtain corresponding 256*256 pixel images to expand the unbalanced dataset. Finally, compared with images generated by DCGAN (Deep convolution generative adversarial network). The dataset expanded with DoubleGAN, the generated images are clearer than DCGAN, and the accuracy of plant species and disease recognition reached 99.80% and 99.53%, respectively. The recognition results are better than those from the original dataset.

In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.

As an emerging energy carrier, hydrogen presents a potential opportunity to increase the flexibility and resiliency of the integrated power and transportation system having ultrahigh penetration of renewable generation. This article studies a conceptual integrated energy-mobility system (IEMS) based on renewable-to-hydrogen stations (RHSs) and tank truck fleets. In an RHS, the renewables power the grid while also charge a hydrogen energy storage unit that can perform power-to-hydrogen (P2H) and hydrogen-to-power (H2P) operations. Besides, using tank truck fleets, the RHS will distribute its surplus hydrogen products to local consumers, e.g., refueling stations serving hydrogen fuel cell vehicles. The operation framework of the IEMS is analyzed. And some preprocessing technologies are developed to reduce modeling complexity. Then, a joint optimal scheduling model, involving renewable generationcontract, P2H, and H2P of the RHS, hydrogen product demands of the local consumers, and vehicle routing problem (VRP) of the truck fleets, is established. Afterwards, the formulated problem is solved by a heuristic method based on the exhaustive search and genetic algorithm. Numerical results verify the effectiveness of the joint optimal scheduling model and illustrate that the IEMS has the potentials to promote hydrogen-integrated transportation and power systems.

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

The stomata of plants mainly regulate gas exchange and water dispersion between the interior and external environments of plants and play a major role in the plants’ health. The existing methods of stomata segmentation and measurement are mostly for specialized plants. The purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants. The proposed method utilizes level set theory and image processing technology and can outperform the existing stomata segmentation and measurement methods based on threshold and skeleton in terms of its versatility. The single stomata images of different plants were the input of the method and a level set based on the Chan-Vese model was used for stomatal segmentation. This allowed the morphological features of the stomata to be measured. Contrary to existing methods, the proposed segmentation method does not need any prior information about the stomata and is independent of the plant types. The segmentation results of 692 living stomata of black poplars show that the average measurement accuracies of the major and minor axes, area, eccentricity and opening degree are 95.68%, 95.53%, 93.04%, 99.46% and 94.32%, respectively. A segmentation test on dayflower (Commelina benghalensis) stomata data available in the literature was completed. The results show that the proposed method can effectively segment the stomata images (181 stomata) of dayflowers using bright-field microscopy. The fitted slope of the manually and automatically measured aperture is 0.993, and the R2 value is 0.9828, which slightly outperforms the segmentation results that are given in the literature. The proposed automated segmentation and measurement method for living stomata is superior to the existing methods based on the threshold and skeletonization in terms of versatility. The method does not need any prior information about the stomata. It is an unconstrained segmentation method, which can accurately segment and measure the stomata for different types of plants (woody or herbs). The method can automatically discriminate whether the pore region is independent or not and perform pore region extraction. In addition, the segmentation accuracy of the method is positively correlated with the stomata’s opening degree.

The temperature rise in wireless charging systems (WCS) limits their reliability and material lifespan, hindering the development of wireless charging technology. This article proposes a thermal design and optimization method for the magnetic coupler in WCSs. The proposed method uses electromagnetic–thermal simulation to predict the steady-state temperature of the magnetic coupler and employs multiobjective optimization to select suitable parameters. First, the losses in the WCS are analyzed, and a thermal model is established. The necessary electrical and thermal parameters for the modeling process are derived. Second, the nondominated sorting genetic algorithm II performs the multiobjective Pareto optimization on the established optimization model. Finally, a set of parameters with excellent performance is selected from the optimization results, and an experimental prototype is constructed to validate the proposed method's correctness.