EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment

There are a lot of flammable materials in the textile workshop, and once a fire occurs, it will cause property damage and casualties. At present, smoke detection in textile workshops mainly relies on temperature‐sensing smoke sensors with low detection rate and poor real‐time performance, which cannot meet the task of smoke detection in complex environments. Therefore, this paper proposes an improved mixed Gaussian and YOLOv5 smoke detection algorithm for textile workshops. In order to reduce the interference of static background in smoke detection, an improved gaussian mixture algorithm is used to extract suspected smoke areas in video by using the dynamic characteristics of smoke. Then, an adaptive attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi‐scale target recognition ability. In addition, the focal loss function is used to reduce the impact of background and foreground class imbalances on the detection results. The experimental results show that the detection accuracy of the proposed method is 94.7%, and the average detection speed is 66.7 FPS. By comparing with the existing state‐of‐the‐art algorithms, the detection capability of this method has been significantly improved. At the same time, it has high real‐time performance and detection accuracy in smoke detection in textile workshops.

Prohibited item detection is crucial for the safety of public places. Deep learning, one of the mainstream methods in prohibited item detection tasks, has shown superior performance far beyond traditional prohibited item detection methods. However, most neural network architectures in deep learning still lack sufficient local feature representation ability for overlapping and small targets, and ignore the problem of semantic conflicts caused by direct feature fusion. In this paper, we propose MFPIDet, a novel prohibited item detection neural network architecture based on improved YOLOV7 to achieve reliable prohibited item detection in complex environments. Specifically, a multi-scale attention module (MAM) backbone is proposed to filter the redundant information of target regions and further applied to enhance the local feature representation ability of overlapping objects. Here, to reduce the redundant information of target regions, a squeeze-excitation (SE) block is used to filter the background. Then, aiming at enhancing the feature expression ability of overlapping objects, a multi-scale feature extraction module (MFEM) is designed for local feature representation. In addition, to obtain richer context information, We design an adaptive fusion feature pyramid network (AF-FPN) to combine the adaptive context information fusion module (ACIFM) with the feature fusion module (FFM) to improve the neck structure of YOLOV7. The proposed method is validated on the PIDray dataset, and the tested results showed that our method obtained the highest mAP (68.7%), which is improved by 3.5% than YOLOV7 methods. Our approach provides a new design pattern for prohibited item detection in complex environments and shows the development potential of deep learning in related fields.

One of the fundamental operations in computer vision applications is change detection, in which moving foreground objects are segmented from a static background. A common approach for change detection is the comparison of an image frame with the stored background model using a matching algorithm, a process known as background subtraction. However, such techniques fail in environments with dynamic backgrounds, illumination changes, or shadow and camera jitters. This study focuses on effectively detecting scene changes in complex environments. To this end, we proposed a new colour descriptor named local colour difference pattern (LCDP) that is insusceptible to shadow and is able to capture both colour and texture features at a pixel location. Furthermore, a scene change detection framework was proposed to handle dynamic scenes based on sample consensus that integrates LCDP and a novel spatial model fusion mechanism. Experiments using the CDnet benchmark dataset demonstrated the effectiveness of the proposed approach to change detection in complex environments.

Red rot in sugarcane is a devastating fungal disease caused by Colletotrichum falcatum. It leads to wilting, red discoloration, and yield losses, which can impact sugar production globally. Red rot hampers sugarcane production, causing yield reductions, and economic losses, and affecting the nation’s sugar industry demanding stringent disease management strategies. There is a need of a system that detects the red rot disease at an early stage before it spreads. Transfer learning enhances agricultural models, leveraging pre-trained data for improved crop predictions, resource optimization, and sustainability. In this article, we have proposed a transfer learning-based VGG16 model for the detection of red rot disease in sugarcane on a large dataset. The model achieves a remarkable 98.85% accuracy and an F1 score of 0.93 in early red rot detection in sugarcane. This work underscores the potency of leveraging re-trained models for crop disease identification, offering a promising avenue for proactive disease management. This research marks a substantial step towards enhancing crop yield and sustainability, presenting an accessible and impactful technological solution for early disease detection in sugarcane, ultimately benefiting agricultural practices.

The effects of K nutrition on polyamine content were analyzed in several parts of fruiting cuttings of Cabernet Sauvignon at different stages of development. Polyamines (especially conjugates and wall-bound forms) were strongly influenced by K nutrition in several organs before visual nutrient deficiency and excess symptoms appeared in leaves. These changes in polyamine composition and content induced by K nutrition differed according to tissue and stage of development. K deficiency and K excess (associated with Mg deprivation) had some opposite effects on polyamine metabolism, depending on tissue and stage of development. Accumulation of free putrescine in leaves coincided with the appearance of severe K deficiency symptoms. The visual appearance of Mg deficiency, induced by K excess, was associated with an accumulation of free spermidine, spermine, and diaminopropane in leaves. The results suggest that polyamines, especially conjugated and wall-bound polyamines, appear in some organs as good indicators of the K status of the grapevine and can be used as sensitive biochemical markers to distinguish the optimum K levels for grapevines before the appearance of K nutrient deficiency symptoms.

In the current agricultural landscape, a significant portion of tomato plants suffer from leaf diseases, posing a major challenge to manual detection due to the task's extensive scope. Existing detection algorithms struggle to balance speed with accuracy, especially when identifying small-scale leaf diseases across diverse settings. Addressing this need, this study presents FCHF-DETR (Faster-Cascaded-attention-High-feature-fusion-Focaler Detection-Transformer), an innovative, high-precision, and lightweight detection algorithm based on RT-DETR-R18 (Real-Time-Detection-Transformer-ResNet18). The algorithm was developed using a carefully curated dataset of 3147 RGB images, showcasing tomato leaf diseases across a range of scenes and resolutions. FasterNet replaces ResNet18 in the algorithm's backbone network, aimed at reducing the model's size and improving memory efficiency. Additionally, replacing the conventional AIFI (Attention-based Intra-scale Feature Interaction) module with Cascaded Group Attention and the original CCFM (CNN-based Cross-scale Feature-fusion Module) module with HSFPN (High-Level Screening-feature Fusion Pyramid Networks) in the Efficient Hybrid Encoder significantly enhanced detection accuracy without greatly affecting efficiency. To tackle the challenge of identifying challenging samples, the Focaler-CIoU loss function was incorporated, refining the model's performance throughout the dataset. Empirical results show that FCHF-DETR achieved 96.4% Precision, 96.7% Recall, 89.1% mAP (Mean Average Precision) 50-95 and 97.2% mAP50 on the test set, with a reduction of 9.2G in FLOPs (floating point of operations) and 3.6M in parameters. These findings clearly demonstrate that the proposed method improves detection accuracy and reduces computational complexity, addressing the dual challenges of precision and efficiency in tomato leaf disease detection.

Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

In dynamic and complex real-world environments, artificial intelligence (AI) vision systems continue to face significant challenges in accurately detecting and tracking small objects. The core difficulty lies in the fact that small targets usually exhibit limited spatial and textural features, while dynamic backgrounds often generate numerous misleading motion cues, thereby interfering with reliable discrimination between targets and backgrounds. Inspired by the remarkable capability of the insect brain in detecting small moving objects, this study proposes a visual neural network model enhanced by a feedback mechanism. By adaptively responding to temporal variations, the proposed model is able to more precisely distinguish small targets from background-induced false targets. The network architecture consists of two main pathways: a motion detection pathway that extracts motion-related features from minute targets, and a feedback attention pathway that enhances the focus on true targets by leveraging the feature differences between real and false motion signals. In addition, a global inhibition module is incorporated to reduce the false alarm rate by filtering out background-induced false positives, thereby improving the overall detection performance of the model. Experimental results demonstrate that the proposed model achieves a detection rate of 0.81 in complex visual scenarios, whereas the compared models all achieve detection rates below 0.59, indicating a significant improvement in detection performance. Meanwhile, in terms of Precision and F1-score, the proposed model achieves values of 0.0648 and 0.12, respectively, while the compared models obtain values lower than 0.0077 and 0.015, further validating the superiority of the proposed method in detection accuracy and robustness.

In complex environments, the clutter statistical characteristics of synthetic aperture radar (SAR) are inconstant, and the constant detection performance of a false alarm rate (CFAR) detector based on a clutter statistical model is also hard to achieve. As a result, the overestimated threshold leads to a degradation in detection probability. To this end, this paper proposes a SAR ship detector different from CFAR detectors, which is independent of traditional clutter statistical distribution models and the probability of a false alarm (PFA). The proposed detector aims to raise the ship detection probability and alleviate interference from complex environments such as multi-target areas, shores, and breakwaters. It estimates clutter-truncated thresholds based on clutter intensity statistics (CIS). Firstly, three statistical parameters, including the mean, standard deviation, and maximum intensity of background clutter contaminated by outliers, are calculated; secondly, these parameters are utilized to estimate the clutter-truncated threshold using the novel CIS; and finally, the pixel under test is determined according to the CIS detection rule. Compared with CFAR-based algorithms, CIS obtains a high probability of detection in complex environments. As for other aspects, the CIS detector is insensitive to the structure of the detection window, as well as the size. It is also computationally efficient due to its simple calculations. The superiority of the CIS detector is validated on scene-differed SAR images from the DSSDD dataset.

The earliest detection of plant disease is the primary concern of the farming community. The availability of advanced digital cameras and smartphones with improved image acquisition modes and deep learning methods like convolutional neural networks (CNN) can detect plant disease with high accuracy. This study used 2259 smartphone images of various rice (Oryza sativa) plant parts under various classes and 250 real-time validation images for classifying 12 rice diseases and nutrient deficiency symptoms. Different image segmentation techniques like foreground extraction were used to segment affected portions. Additionally, optimization of models and procedures to use them in smartphones with offline working capabilities have been described. Furthermore, a dynamic framework has been developed and demonstrated where the server trains on unseen image data to improve the classification performance and updates the model on falling below a certain threshold level. A comparison was made across different models used for image classification with many supporting metrics to select the best model for transfer learning. The top four performing models were DenseNet201, Xception, MobileNetV2, and ResNet50, with validation accuracies of 0.9803, 0.9778, 0.9756, and 0.9718, respectively. The ResNet50 model was found to best among all for cloud architectures, while MobileNetV2 appeared as the best model for the smartphone application. Finally, the android application, “Rice Disease Detector” compiled with the MobileNetV2 model was tested for multiple disease occurrences in a single capture. More research is warranted to test the application for smartphones with variable configurations.

The agriculture sector is undergoing a transformative evolution driven by the integration of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision (CV). These advancements are not only addressing long-standing challenges but also unlocking new possibilities for enhancing productivity, sustainability, and decision-making in agricultural practices. In this context, BotaniCare emerges as a comprehensive AI-based intelligent agricultural assistant, designed to support both farmers and agricultural researchers through a suite of innovative features and services. BotaniCare delivers end-to-end solutions encompassing crop cultivation guidance, early disease detection and prevention, interactive chatbot assistance, and analysis of the medicinal value of plants. By employing Computer Vision, the system is capable of real-time monitoring and assessment of plant health, identifying symptoms of disease, nutrient deficiencies, and pest infestations through image-based diagnostics. This empowers users to take timely and informed actions, minimizing crop loss and reducing dependency on manual inspections. At the core of BotaniCare is a robust Machine Learning framework that leverages vast datasets comprising geographical information, climate conditions, and soil profiles to recognize patterns and generate tailored recommendations. These predictive insights assist in optimizing planting schedules, irrigation plans, fertilizer usage, and crop selection strategies, ultimately improving yield and resource efficiency. Furthermore, BotaniCare integrates Generative AI technologies to provide personalized and context-aware conversational support. The embedded intelligent chatbot serves as a 24/7 virtual advisor, offering dynamic assistance in local languages and dialects, answering queries, delivering updates on weather or market trends, and guiding users through complex agricultural practices with ease. The architecture of the system combines cloud-based services, edge computing, and mobile accessibility to ensure scalability, low-latency interactions, and real-time decision support in both connected and remote environments. This paper explores the design and implementation methodology of BotaniCare, detailing the technical stack, data pipelines, training strategies, and deployment mechanisms involved in building the system. Keywords: AI in Agriculture, Crop Recommendation System, Plant Disease Detection, Computer Vision, Generative AI, Medicinal Plants, Smart Farming.

Recent developments in water status measurement techniques using the psychrometer, the pressure probe, the osmometer and pressure chamber are reviewed, and the process of cell elongation from the viewpoint of plant-water relations is discussed for plants subjected to various environmental stress conditions. Under water-deficient conditions, cell elongation of higher plants can be inhibited by interruption of water flow from the xylem to the surrounding elongating cells. The process of growth inhibition at low water potentials could be reversed by increasing the xylem water potential by means of pressure application in the root region, allowing water to flow from the xylem to the surrounding cells. This finding confirmed that a water potential field associated with growth process,i.e., the growth-induced water potential, is an important regulating factor for cell elongation other than metabolic factors. The concept of the growth-induced water potential was found to be applicable for growth retardation caused by cold stress, heat stress, nutrient deficiency and salinity stress conditions. In the present review, the fact that the cell elongation rate is primarily associated with how much water can be absorbed by elongating cells under water-deficiency, nutrient deficiency, salt stress, cold stress and heat stress conditions is suggested.

Insulator defect detection is important in ensuring power systems’ safety and stable operation. To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extraction from high-entropy details to low-entropy semantics, an improved YOLOv8 target detection network for insulator defects based on bidirectional weighted feature fusion was proposed. A C2f_DSC feature extraction module was designed to identify more insulator tube features, an EMA (encoder–modulator–attention) mechanism and a BiFPN (bidirectional weighted feature pyramid network) fusion layer in the backbone network were introduced to extract different features in complex environments, and EIOU (efficient intersection over union) as the model’s loss function was used to accelerate model convergence. The CPLID (China Power Line Insulator Dataset) was tested to verify the effectiveness of the proposed algorithm. The results show its model size is only 6.40 M, and the mean accuracy on the CPLID dataset reaches 98.6%, 0.8% higher than that of the YOLOv8n. Compared with other lightweight models, such as YOLOv8s, YOLOv6, YOLOv5s, and YOLOv3Tiny, not only is the model size reduced, but also the accuracy is effectively improved with the proposed algorithm, demonstrating excellent practicality and feasibility for edge devices.

In situ surface‐enhanced Raman scattering (SERS) enables the monitoring of dynamic processes in chemistry and biology. However, highly sensitive in situ SERS detection remains a challenge in complex environments. In this study, a magnetoelectric composite membrane with SERS properties that can be actively tuned by remotely applied magnetic fields is presented. Owing to the magnetoelectric coupling effect, the applied magnetic field can change the surface potential of the composite membrane, thus modulating the local electric field strength of the silver nanoparticles on the membrane surface. Further enhancement of the SERS signals of rhodamine 6G, crystal violet (CV), malachite green, and 4‐nitrothiophenol is achieved in the presence of a magnetic field. Furthermore, experiments show that the in situ SERS signals of dopamine in aqueous solution, CV on shrimp surfaces, and human‐secreted sweat can be successfully enhanced by applying an external magnetic field. The remote SERS enhancement technique proposed in this study provides an innovative approach for highly sensitive in situ detection in complex environments.

Target-free recognition of cable vibration in complex backgrounds based on computer vision