To monitor multiple environmental factors of henhouses in modern chicken farms, a henhouse online monitoring system based on wireless sensor network was developed using wireless sensor technology and computer network technology. Sensor data compensation and correction were designed to be achieved using software and data fitting methods, data reliable transmission achieved using a data loss recovery strategy, and data missing during monitoring addressed using a self-decision and online filling method. Operation test of the system showed that: The system was economic and reliable; it enabled wireless monitoring and Web display of the environmental factors of a henhouse; and the root mean square errors (RMSEs) between the estimated values from the self-decision and on-line filling method and experimental values of the four environmental factors were 0.1698, 3.0859, 77 and 0.094, respectively, indicative of high estimation accuracy. The system can provide support for modern management of henhouses and can be transplanted to related monitoring scenarios in the agricultural field.

Introduction: Drought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.Methods: We constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.Results: The experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.Discussion: Our model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea.

Pear trees must be artificially pollinated to ensure yield, and the efficiency of pollination and the quality of pollen germination affect the size, shape, taste, and nutritional value of the fruit. Detecting the pollen germination vigor of pear trees is important to improve the efficiency of artificial pollination and consequently the fruiting rate of pear trees. To overcome the limitations of traditional manual detection methods, such as low efficiency and accuracy and high cost, and to meet the requirements of screening high-quality pollen to promote the yield and production of fruit trees, we proposed a detection method for pear pollen germination vigor named YOLOv8-Pearpollen, an improved version of YOLOv8-n. A pear pollen germination dataset was constructed, and the image was enhanced using Blend Alpha to improve the robustness of the data. A combination of knowledge distillation and model pruning was used to reduce the complexity of the model and the difficulty of deployment in hardware facilities while ensuring that the model achieved or approached the detection effect of a large-volume model that can adapt to the actual requirements of agricultural production. Various ablation tests on knowledge distillation and model pruning were conducted to obtain a high-quality lightweighting method suitable for this model. Test results showed that the mAP of YOLOv8-Pearpollen reached 96.7%. The Params, FLOPs, and weights were only 1.5 M, 4.0 G, and 3.1 MB, respectively, and the detection speed was 147.1 FPS. A high degree of lightweighting and superior detection accuracy were simultaneously achieved.

As the application of mobile robots becomes increasingly widespread, many navigation applications now require more than just obstacle avoidance capabilities. There is a growing demand for robots to follow predetermined global paths. Inspired by stamping technology in the metalworking field, this paper proposes a two-stage local path planning algorithm based on sampling and optimization, termed the path stamping forming algorithm. In the exploration stage, the path stamping forming algorithm finds a preliminary path that avoids obstacles and runs parallel to the global path. The subsequent optimization stage determines the optimal local path that meets specific constraints. Finally, we compared the proposed algorithm with existing advanced navigation algorithms through simulations and experiments, demonstrating its superior performance. The results indicate that the proposed algorithm enables mobile robots to avoid obstacles and follow the global path without the need to re-plan the global path. Compared with the traditional local path planning algorithm, the performance of the proposed algorithm in following the global path is improved by up to 52.71%.

Automatic weight detection is an essential step in the factory production of Pleurotus eryngii. In this study, a data set containing 1154 Pleurotus eryngii images was created, and then machine vision technology was used to extract eight two-dimensional features from the images. Because the fruiting bodies of Pleurotus eryngii have different shapes, these features were less correlated with weight. This paper proposed a multidimensional feature derivation method and an Attention-Based CNN model to solve this problem. This study aimed to realize the traditional feature screening task by deep learning algorithms and built an estimation model. Compared with different regression algorithms, the R², RMSE, MAE, and MAPE of the Attention-Based CNN were 0.971, 7.77, 5.69, and 5.87%, respectively, and showed the best performance. Therefore, it can be used as an accurate, objective, and effective method for automatic weight measurements of Pleurotus eryngii.

Introduction: Accurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding. However, cotton seedling segmentation remains challenging due to fine-scale and complex organ morphology, uneven point density with noise, and the lack of high-quality annotated datasets. Methods: To address these issues, we propose DCSFormer, a tailored extension of Point Transformer V3 designed for cotton seedling point cloud segmentation. The model introduces the DCS Block, which leverages dynamic sparse expert routing and dual-channel attention to adaptively capture global semantic dependencies and subtle local geometric variations, thereby improving stem-leaf boundary discrimination. In addition, the proposed CLFSkip replaces traditional skip connections with a cross-layer fusion strategy, effectively integrating multi-scale features while preserving organ-level details. We also constructed an annotated cotton seedling dataset to support training and evaluation. Results and Discussion: Experimental results show that DCSFormer achieves 93.67% mIoU, 95.83% mPrec, 97.35% mRec, and 96.56% mF1, outperforming multiple comparison models. Furthermore, when evaluated against baseline models on two public datasets, Crops3D and Pheno4D, DCSFormer exceeds the baseline across all four metrics, further validating its effectiveness and generalizability. This work provides an effective solution for precise cotton seedling organ segmentation.