Deep learning technologies have enabled the development of a variety of deep learning models that can be used to detect plant leaf diseases. However, their use in the identification of soybean leaf diseases is currently limited and mostly based on machine learning methods. In this investigation an enhanced deep learning network model was developed to recognize soybean leaf diseases more accurately. The improved network model consists of three parts: feature extraction, attention calculation, and classification. The dataset used was first diversified through data augmentation operations such as random masking to enhance network robustness. An attention module was then used to generate feature maps at various depths. This increased the network’s focus on discriminative features, reduced background noise, and enabled the use of the LeakyReLu activation function in the attention module to prevent situations in which neurons fail to learn when the input is negative. Finally, the extracted features were then integrated using a fully connected layer, and the predicted disease category inferred to improve the classification accuracy of soybean leaf diseases. The average recognition accuracy of the improved network model for soybean leaf diseases was 85.42% both higher than the six deep learning comparison models (ConvNeXt (66.41%), ResNet50 (72.22%), Swin Transformer (77.00%), MobileNetV3 (67.27%), ShuffleNetV2 (59.89%), and SqueezeNet (72.92%)), thus proving the effectiveness of the improved method.The model proposed in this paper was also tested on the grapevine leaf dataset, and the performance ability of the improved network model remained due to other common network models, and overall the proposed network model was very effective in leaf disease identification.

Lead (Pb), a heavy metal environmental pollutant, poses a threat to the health of humans and birds. Inflammation is one of the most common pathological phenomena in the case of illness and poisoning. However, the underlying mechanisms of inflammation remain unclear. The cerebellum and the thalamus are important parts of the nervous system. To date, there have been no reports of Pb inducing inflammation in animal cerebellums or thalami. Selenium (Se) can relieve Pb poisoning. Therefore, we aimed to explore the mechanism by which Se alleviates Pb toxicity to the cerebellums and thalami of chickens by establishing a chicken Pb or/and Se treatment model. Our results demonstrated that exposure to Pb caused inflammatory damage in cerebellums and thalami, evidenced by the characteristics of inflammation, the decrease in anti-inflammatory factors (interleukin (IL)-2 and interferon-γ (INF-γ)), and the increase in pro-inflammatory factors (IL-4, IL-6, IL-12β, IL-17, and nitric oxide (NO)). Moreover, we found that the IL-2/IL-17–NO pathway took part in Pb-caused inflammatory injury. The above findings were reversed by the supplementation of dietary Se, meaning that Se relieved inflammatory damage caused by Pb via the IL-2/IL-17–NO pathway. In addition, an up-regulated oxidative index malondialdehyde (MDA) and two down-regulated antioxidant indices (glutathione (GSH) and total antioxidant capacity (TAC)) were recorded after the chickens received Pb stimulation, indicating that excess Pb caused an oxidant/antioxidant imbalance and oxidative stress, and the oxidative stress mediated inflammatory damage via the GSH–IL-2 axis. Interestingly, exposure to Pb inhibited four glutathione peroxidase (GPx) family members (GPx1, GPx2, GPx3, and GPx4), three deiodinase (Dio) family members (Dio1, Dio2, and Dio3), and fifteen other selenoproteins (selenophosphate synthetase 2 (SPS2), selenoprotein (Sel)H, SelI, SelK, SelM, SelO, SelP1, SelPb, SelS, SelT, SelU, and selenoprotein (Sep)n1, Sepw1, Sepx1, and Sep15), suggesting that Pb reduced antioxidant capacity and resulted in oxidative stress involving the SPS2–GPx1–GSH pathway. Se supplementation, as expected, reversed the changes mentioned above, indicating that Se supplementation improved antioxidant capacity and mitigated oxidative stress in chickens. For the first time, we discovered that the SPS2–GPx1–GSH–IL-2/IL-17–NO pathway is involved in the complex inflammatory damage mechanism caused by Pb in chickens. In conclusion, this study demonstrated that Se relieved Pb-induced oxidative stress and inflammatory damage via the SPS2–GPx1–GSH–IL-2/IL-17–NO pathway in the chicken nervous system. This study offers novel insights into environmental pollutant-caused animal poisoning and provides a novel theoretical basis for the detoxification effect of Se against oxidative stress and inflammation caused by toxic pollutants.

When utilizing convolutional neural networks for wheat disease identification, the training phase typically requires a substantial amount of labeled data. However, labeling data is both complex and costly. Additionally, the model’s recognition performance is often disrupted by complex factors in natural environments. To address these issues, this paper proposes a wheat disease identification model, SC-ConvNeXt, which integrates the SimCLR pre-training framework and an improved CBAM attention mechanism. The model employs ConvNeXt-T as the feature extraction network. Initially, it uses the self-supervised SimCLR pre-training framework to learn inter-class similarities, reducing the reliance on labeled data during training. Subsequently, the CBAM attention module is integrated into ConvNeXt-T to enhance the model’s feature extraction and generalization capabilities in complex backgrounds, and each attention module’s loss function is improved with a LeakyReLU activation function to prevent neuron deactivation when inputs are negative. Furthermore, by introducing the Focal Loss function, the model addresses the imbalance in the quantity of easy and difficult-to-classify samples. The dataset used in this study comes from the ‘Smart Agriculture’ platform of Jilin Agricultural Science and Technology College, including images of three wheat diseases and healthy wheat. After expanding the dataset with various data augmentation methods, the effectiveness of adding SimCLR and the attention mechanism was sequentially verified. Comparative experiments were also conducted against four classic classification models. The experimental results show that the proposed SC-ConvNeXt model achieves an average classification accuracy of 88.05% on the test set, the highest among all comparative models. The model does not require additional labeled data during training, demonstrating its effectiveness in enhancing wheat disease recognition performance under natural environmental conditions without the need for extra labeled data training.

A dynamic path-planning algorithm based on a general constrained optimization problem (GCOP) model and a sequential quadratic programming (SQP) method with sensor input is proposed in this paper. In an unknown underwater space, the turtle-inspired amphibious spherical robot (ASR) can realise the path-planning control movement and achieve collision avoidance. Due to the special underwater environments, thrusters and diamond parallel legs (DPLs) are installed in the lower hemisphere to realise accurate motion control. A propulsion model for a novel water-jet thruster based on experimental analysis and a modified Denavit–Hartenberg (MDH) algorithm are developed for multiple degrees of freedom (MDOF) to realize high-precision and high-speed motion control. Simulations and experiments verify that the effectiveness of the GCOP and SQP algorithms can realize reasonable path planning and make it possible to improve the flexibility of underwater movement with a small estimation error.

It is challenging to achieve high-speed and accurate multicooperation of turtle-inspired amphibious spherical robots (ASRs) in turbid water and confined spaces when the robots are underwater movement with multiple degrees of freedom (MDOF). This paper innovatively proposes a control strategy for modelling and experimental platforms that can communicate and cooperate between multiple robots. First, a novel underwater kinematic model using the unit quaternion (UQ) algorithm is proposed based on attitude interpolation to realize MDOF movement. Then, the ASRs use a camera acquisition compartment to realize underwater target recognition and tracking by adjusting their motion trajectory. Finally, multirobot cooperation and three-dimensional (3-D) movement experiments using ASRs verifies the effectiveness of the proposed cooperation mode and 3-D underwater movement according to the control strategy implemented. The control strategy and experimental results presented in this paper can inspire the efficient communication and cooperation of multiple bionic robots, which are currently popular research topics.

Ammonia (NH₃) and lipopolysaccharide (LPS), common pollutants in poultry farming environments, pose significant health risks by disrupting cellular processes. Although previous studies have demonstrated the individual effect of NH₃ or LPS on human and animal health, the mechanisms underlying their combined impact on chicken heart tissue remain poorly understood. In this study, we established a chicken cardiotoxicity model to investigate the effects of NH₃ and/or LPS exposure on energy metabolism, autophagy, endoplasmic reticulum (ER) stress, and apoptosis in cardiomyocytes. Our findings indicated that exposure to NH₃ or/and LPS reduced ATPase activity and ATP content, led to the downregulation of HK2, PK, PDHX, and SDH, and upregulation of AMPK, resulting in impaired energy metabolism in chicken cardiomyocytes. Additionally, we found the gga-miR-1599/HK2 axis as a key regulator involved in NH₃ or/and LPS-induced energy metabolism impairment. The impairment in energy metabolism activated the AMPK/mTOR pathway, which subsequently triggered autophagy, evidenced by the upregulation of Beclin, LC3-I, and LC3-II. Furthermore, decreased mTOR expression induced ER stress, as indicated by the upregulation of key markers such as ATF6, GRP78, IRE1, and PERK. ER stress, in turn, increased CHOP expression, which downregulated Bcl-2 and upregulated Bim, resulting in elevated levels of Bax, caspase-9, and caspase-3, ultimately triggering apoptosis. This study provides valuable insights into the mechanisms of NH₃ and LPS co-exposure on poultry heart tissue and identifies potential molecular targets for mitigating these adverse effects.