Apple leaf disease image recognition based on a modified rime optimization algorithm and ConvNeXt network

Early and accurate diagnosis of apple leaf disease is a prerequisite for maintaining crop health and for enhancing agricultural productivity. Conventional methods, which largely relied on human inspection or naive machine learning algorithms, were not capable of handling the complexity of patterns, the class imbalance, and the real-world challenges such as conflated symptoms or poor lighting. The present study develops a completely new model design by integrating a ConvNeXt model along with a modified rime optimization algorithm (MRIME) used for hyperparameter tuning as well as complementing through the Convolutional Block Attention Module (CBAM) to ensure better feature extraction. CBAM extends the power of the model in focusing on critical discriminative regions, while MRIME gives optimal values for relevant hyperparameters for generalization while avoiding overfitting. Evaluated by the Apple Leaf Disease Symptoms Dataset, the proposed approach attained an accuracy of 92.7%, precision of 92.5%, recall of 92.6%, F1-score of 92.5%, and mAP of 92.3%, surpassing most baselines including ResNet50 and EfficientNet-B0. Compared to the aforementioned baselines, ablation experiments demonstrated that CBAM led to about 1.5% enhancement in accuracy, while MRIME could boost performance by another 1.2% via hyperparameter tuning. These results confirm the complementary benefit of attention mechanisms and metaheuristic optimization in producing state-of-the-art results

Cotton pest and disease diagnosis via YOLOv11-based deep learning and knowledge graphs: a real-time voice-enabled edge solution

IntroductionHigh labor costs, limited expert availability, and slow response hinder cotton pest and disease management. We propose a real-time, voice-enabled edge solution that integrates deep learning–based detection with a domain knowledge graph to deliver accessible, field-ready decision support.MethodsWe construct a cotton pest–disease knowledge graph with over 3,000 triples spanning seven major categories by fusing expert-curated and web-sourced knowledge. For image recognition, we develop an enhanced YOLOv11 detector compressed via LAMP pruning and a teacher–assistant–student distillation strategy for lightweight, high-performance deployment on Jetson Xavier NX. Detected objects are semantically aligned to graph entities to generate context-aware recommendations, which are delivered through Bluetooth voice feedback for hands-free use.ResultsThe optimized model has 0.3M parameters and achieves mAP50 = 0.835 at 52 FPS on the edge device, enabling stable real-time inference in field conditions while preserving detection accuracy.DiscussionCoupling a compact detector with a structured knowledge graph and voice interaction reduces dependence on expert labor and speeds response in non-expert settings, demonstrating a practical pathway to scalable, intelligent cotton pest and disease management at the edge

Optical Extinctions of Inter-Arm Molecular Clouds in M31: A Pilot Study for the Upcoming CSST Observations

Recent sub-millimeter dust thermal emission observations have unveiled a significant number of inter-arm massive molecular clouds in M31.However,the effectiveness of this technique is limited to its sensitivity,making it challenging to study more distant galaxies.This study introduces an alternative approach,utilizing optical extinctions derived from space-based telescopes,with a focus on the forthcoming China Space Station Telescope(CSST).We first demonstrate the capability of this method by constructing dust extinction maps for 17 inter-arm massive molecular clouds in M31 using the Panchromatic Hubble Andromeda Treasury(PHAT) data.Our analysis reveals that inter-arm massive molecular clouds with an optical extinction(AV) greater than 1.6 mag exhibit a notable AV excess,facilitating their identification.The majority of these inter-arm massive molecular clouds show an AV around 1 mag,aligning with measurements from our JCMT data.Further validation using a mock CSST RGB star catalog confirms the method's effectiveness.We show that the derived AV values using CSST z and y photometries align more closely with the input values.Molecular clouds with AV>1.6 mag can also be identified using the CSST mock data.We thus claim that future CSST observation could provide an effective way for the detection of inter-arm massive molecular clouds with significant optical extinction in nearby galaxies.Comment: 15 pages,9 figure

A smart chicken farming platform for chicken behavior identification and feed residual estimation

It is very potential to develop digital villages for promoting smart agriculture. As one of the important research fields of smart agriculture, smart chicken farms encounter management problems such as difficulties in quickly and accurately warning of sick and dead chickens and estimating feed residuals. Therefore, this study not only respectively proposed CKTrack and FRCM to detect sick and dead chickens and estimate feed residuals, but also developed a smart chicken farming platform for automagical management. Our main results include (1) the proposed CKTrack method can effectively identify sick and dead chickens under the condition of limited data volume and computing capacity; (2) the proposed FRCM method can accurately estimate the feed residuals; and (3) the smart chicken farming platform developed can provide farmers with functions such as early warning of sick and dead chickens, visualization of the chicken quantity inventory, and feed residual estimation.<br/

Change in sensory integration and regularity of postural sway with the suspensory strategy during static standing balance

Background and aimThe suspensory strategy, a method for controlling postural balance in the vertical direction of the center of mass (COM), is considered by the elderly as a means of balance control. The vertical COM control might alter the sensory integration and regularity of postural sway, which in turn impacts balance. However, to date, this was not confirmed. Thus, this study aimed at investigating the influence of the suspensory strategy achieved through knee flexion on the static standing balance.MethodsNineteen participants were monitored at knee flexion angles of 0°, 15°, and 65°. Time-frequency analysis and sample entropy were employed to analyze the COM data. Time-frequency analysis was utilized to assess the energy content across various frequency bands and corresponding percentage of energy within each frequency band. The outcomes of time-frequency are hypothesized to reflect the balance-related sensory input and sensory weights. Sample entropy was applied to evaluate the regularity of the COM displacement patterns.ResultsKnee flexion led to a decreased COM height. The highest energy content was observed at 65° knee flexion, in contrast with the lowest energy observed at 0° in both the anterior–posterior (AP) and medial-lateral (ML) directions. Additionally, the ultra-low-frequency band was more pronounced at 65° than that at 0° or 15° in the ML direction. Furthermore, the COM amplitudes were notably higher at 65° than those at 0° and 15° in the AP and ML directions, respectively. The sample entropy values were lower at 65° and 15° than those at 0° in the ML direction, with the lowest value observed at 65° in the vertical direction.ConclusionThe suspensory strategy could enhance the sensory input and cause sensory reweighting, culminating in a more regular balance control. Such suspensory strategy-induced postural control modifications may potentially provide balance benefits for people with declining balance-related sensory, central processing, and musculoskeletal system functions

Enhanced-RICAP: a novel data augmentation strategy for improved deep learning-based plant disease identification and mobile diagnosis

IntroductionPlant diseases pose a significant threat to global food security and agricultural productivity, making accurate and timely disease identification essential for effective crop management and minimizing economic losses. Although data augmentation techniques such as RICAP improve model robustness, their reliance on randomly extracted image regions can introduce label noise, potentially misleading the training of deep learning models.MethodsThis study introduces Enhanced-RICAP, an advanced data augmentation technique designed to improve the accuracy of deep learning models for plant disease detection. Enhanced-RICAP replaces random patch selection with an attention module guided by class activation maps, focusing on discriminative regions, Enhanced-RICAP reduces label noise and improves model accuracy for plant disease detection, addressing a key limitation of traditional augmentation methods. The method was evaluated using several deep learning architectures, such as ResNet18, ResNet34, ResNet50, EfficientNet-b, and Xception, on the cassava leaf disease and PlantVillage tomato leaf disease datasets.ResultsThe experimental results demonstrate that Enhanced-RICAP consistently outperforms existing augmentation methods, including CutMix, MixUp, CutOut, Hide-and-Seek, and RICAP, across key evaluation metrics: accuracy, precision, recall, and F1-score. The ResNet18+Enhanced-RICAP configuration achieved 99.86% accuracy on the tomato leaf disease dataset, whereas the Xception+Enhanced-RICAP model attained 96.64% accuracy in classifying four cassava leaf disease categories.Discussion and ConclusionTo bridge the gap between research and practical application, the ResNet18+Enhanced-RICAP model was deployed in PlantDisease, a mobile application that enables real-time disease identification and management recommendations. This approach supports sustainable agriculture and strengthens food security by providing farmers with accessible and reliable diagnostic tools