Lijie Zhang’s research while affiliated with Hebei Agricultural University and other places

Compared to existing distributed flowshop scheduling problems (DFSPs), this paper addresses a more realistic DFSP, which integrates intermachine blocking constraints and two customized processing stages of assembly and differentiation. The manufacturing process includes job fabrication in distributed blocking flowshops, job-to-product assembly on an assembling machine, and product fine-processing on differentiation machines. A novel evolutionary framework consisting of continuous space exploration, discrete space exploitation, and dual space knowledge migration is devised. This framework has advanced features of distribution, memetic evolution, and dual-space coevolution, and can serve as a unified model to construct algorithms for different optimization problems. Based on this evolutionary framework, a matrix and learning co-aided distributed dual-space memetic algorithm (DDMA) is proposed to address the studied problem. In DDMA, exploratory population is represented as a real matrix, where individuals have different identities that will dynamically adjust with evolution. In accordance with identity differences, exploratory population is heterogeneously evolved in the continuous search space by a matrix-aided evolutionary optimizer. The exploitative population consists of elite individuals, which are represented as discrete permutations. It is evolved in parallel with exploratory population and in the discrete search space by a learning-aided evolutionary optimizer, including a reinforcement learning-based multi-neighborhood local search and a statistical learning-based enhanced local search. To communicate the superior evolutionary information obtained by exploration and exploitation, an adaptive knowledge migration across continuous and discrete search spaces is proposed based on the impact of migration on the population diversity. The computational results demonstrate the superiority of DDMA over state-of-the-art algorithms.

Urban tree species identification is crucial for forest management and ecosystem assessment. Mobile Laser Scanning (MLS) provides significant advantages for this task through its flexibility in navigating complex urban environments with spatial constraints. However, MLS-based classification faces challenges such as intricate canopy structures, incomplete point clouds from urban occlusions, and intra-species variations. This study presents Tree Morphology Multi-Representation Fusion Network (TM $^{2}$ F), integrating 3D point cloud data with 2D projections for enhanced tree species classification. The framework employs a backpack-mounted MLS system to capture high-quality point cloud data. The core architecture features an Adaptive Hierarchical Sampling (AHS) module extracting multi-scale geometric features, followed by a Cross-View Fusion (CVF) module that implements stage- wise fusion of 3D structural information with 2D representations. This fusion strategy not only leverages established 2D feature extraction pipelines, but also addresses sparsity issues in point cloud projections. The method was validated on a diverse dataset of eight urban tree species (seven broadleaf and one coniferous species). Quantitative assessment yielded 98.57% F1-score and 98.77% Overall Accuracy with moderate computational resources (2.25M parameters, 1.11G FLOPs), demonstrating significant improvements over existing methods. The proposed workflow achieves a balance between classification accuracy and processing efficiency, making it suitable for large-scale urban tree inventory applications.

Winter wheat is one of the major crops for global food security. Accurate statistics of its planting area play a crucial role in agricultural policy formulation and resource management. However, the existing semantic segmentation methods for remote sensing images are subjected to limitations in dealing with noise, ambiguity, and intra-class heterogeneity, posing a negative impact on the segmentation performance of the spatial distribution and area of winter wheat fields in practical applications. In response to the above challenges, we proposed an improved HRNet-based semantic segmentation model in this paper. First, this model incorporates a semantic domain module (SDM), which improves the model’s precision of pixel-level semantic parsing and reduces the interference from noise through multi-confidence scale class representation. Second, a nested attention module (NAM) is embedded, which enhances the model’s capability of recognizing correct correlations in pixel classes. The experimental results show that the proposed model achieved a mean intersection over union (mIoU) of 80.51%, a precision of 88.64%, a recall of 89.14%, an overall accuracy (OA) of 90.12%, and an F1-score of 88.89% on the testing set. Compared to traditional methods, our model demonstrated better segmentation performance in winter wheat semantic segmentation tasks. The achievements of this study not only provide an effective tool and technical support for accurately measuring the area of winter wheat fields, but also have important practical value and profound strategic significance for optimizing agricultural resource allocation and achieving precision agriculture.

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model’s representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model’s generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model’s extraction accuracy. (3) The model’s input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model).