Search Results (708)

The health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

Intelligent monitoring and fault diagnosis of PV grid-connected inverters are crucial for the operation and maintenance of PV power plants. However, due to the significant influence of weather conditions on the operating status of PV inverters, the accuracy of traditional fault diagnosis methods faces challenges. To address the issue of open-circuit faults in power switching devices, this paper proposes a multi-dimensional feature perception network. This network captures multi-scale fault features under complex operating conditions through a multi-dimensional dilated convolution feature enhancement module and extracts non-causal relationships under different conditions using convolutional feature fusion with a Transformer. Experimental results show that the proposed network achieves fault diagnosis accuracies of 97.3% and 96.55% on the inverter dataset and the generalization performance dataset, respectively. Full article

Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused by Verticillium dahliae by integrating multispectral imaging with machine learning and deep learning techniques. Multispectral and chlorophyll fluorescence images were collected from leaves of the inbred eggplant line 11-435, including data on image texture, spectral reflectance, and chlorophyll fluorescence. Subsequently, we established a multispectral data model, fusion information model, and multispectral image–information fusion model. The multispectral image–information fusion model, integrated with a two-dimensional convolutional neural network (2D-CNN), demonstrated optimal performance in classifying early-stage Verticillium wilt infection, achieving a test accuracy of 99.37%. Additionally, transfer learning enabled us to diagnose early leaf wilt in another eggplant variety, the inbred line 14-345, with an accuracy of 84.54 ± 1.82%. Compared to traditional methods that rely on visible symptom observation and typically require about 10 days to confirm infection, this study achieved early detection of Verticillium wilt as soon as the third day post-inoculation. These findings underscore the potential of the fusion model as a valuable tool for the early detection of pre-symptomatic states in infected plants, thereby offering theoretical support for in-field detection of eggplant health. Full article

Efficient detection and localization of maize seedlings in complex field environments is essential for accurate plant segmentation and subsequent three-dimensional morphological reconstruction. To overcome the limited accuracy and high computational cost of existing models, we propose an enhanced architecture named MaizeStar-YOLO. The redesigned backbone integrates a novel C2F_StarsBlock to improve multi-scale feature fusion, while a PKIStage module is introduced to enhance feature representation under challenging field conditions. Evaluations on a diverse dataset of maize seedlings show that our model achieves a mean average precision (mAP) of 92.8%, surpassing the YOLOv8 baseline by 3.6 percentage points, while reducing computational complexity to 3.0 GFLOPs, representing a 63% decrease. This efficient and high-performing framework enables precise plant–background segmentation and robust three-dimensional feature extraction for morphological analysis. Additionally, it supports downstream applications such as pest and disease diagnosis and targeted agricultural interventions. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article

Timely and accurate identification of plant diseases is critical to mitigating crop losses and enhancing yield in precision agriculture. This paper proposes AgriFusionNet, a lightweight and efficient deep learning model designed to diagnose plant diseases using multimodal data sources. The framework integrates RGB and multispectral drone imagery with IoT-based environmental sensor data (e.g., temperature, humidity, soil moisture), recorded over six months across multiple agricultural zones. Built on the EfficientNetV2-B4 backbone, AgriFusionNet incorporates Fused-MBConv blocks and Swish activation to improve gradient flow, capture fine-grained disease patterns, and reduce inference latency. The model was evaluated using a comprehensive dataset composed of real-world and benchmarked samples, showing superior performance with 94.3% classification accuracy, 28.5 ms inference time, and a 30% reduction in model parameters compared to state-of-the-art models such as Vision Transformers and InceptionV4. Extensive comparisons with both traditional machine learning and advanced deep learning methods underscore its robustness, generalization, and suitability for deployment on edge devices. Ablation studies and confusion matrix analyses further confirm its diagnostic precision, even in visually ambiguous cases. The proposed framework offers a scalable, practical solution for real-time crop health monitoring, contributing toward smart and sustainable agricultural ecosystems. Full article

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article

Accurate and early detection of plant diseases is essential for effective management and the advancement of sustainable smart agriculture. However, building large annotated datasets for disease classification is often costly and time-consuming, requiring expert input. To address this challenge, this study explores the integration of few-shot learning with hyperspectral imaging to detect four major fungal diseases in tomato plants: Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum. Following inoculation, hyperspectral images were captured every other day from Day 1 to Day 7 post inoculation. The proposed hybrid method includes three main steps: (1) preprocessing of hyperspectral image cubes, (2) deep feature extraction using the EfficientNet model, and (3) classification using Manhattan distance within a few-shot learning framework. This combination leverages the strengths of both spectral imaging and deep learning for robust detection with minimal data. The few-shot learning approach achieved high detection accuracies of 85.73%, 80.05%, 90.33%, and 82.09% for A. alternata, A. solani, B. cinerea, and F. oxysporum, respectively, based on data collected on Day 7 post inoculation using only three training images per class. Accuracy improved over time, reflecting the progressive nature of symptom development and the model’s adaptability with limited data. Notably, A. alternata and B. cinerea were reliably detected by Day 3, while A. solani and F. oxysporum reached dependable detection levels by Day 5. Routine visual assessments showed that A. alternata and B. cinerea developed visible symptoms by Day 5, whereas A. solani and F. oxysporum remained asymptomatic until Day 7. The model’s ability to detect infections up to two days before visual symptoms emerged highlights its value for pre-symptomatic diagnosis. These findings support the use of few-shot learning and hyperspectral imaging for early, accurate disease detection, offering a practical solution for precision agriculture and timely intervention. Full article

Small modular reactors are progressing towards greater levels of automation and intelligence, with intelligent control emerging as a pivotal trend in SMR development. When contrasted with traditional commercial nuclear power plants, SMR display substantial disparities in design parameters and the designs of safety auxiliary systems. As a result, fault diagnosis systems tailored for commercial nuclear power plants are ill-equipped for SMRs. This study utilizes the PCTRAN-SMR V1.0 software to develop an intelligent fault diagnosis system for the SMART small modular reactor based on an attention mechanism. By comparing different network models, it is demonstrated that the CNN–LSTM–Attention model with an attention mechanism significantly outperforms CNN, LSTM, and CNN–LSTM models, achieving up to a 7% improvement in prediction accuracy. These results clearly indicate that incorporating an attention mechanism can effectively enhance the performance of deep learning models in nuclear power plant fault diagnosis. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

The genus Tegonotus Nalepa (Acariformes: Eriophyidae: Phyllocoptinae) is recorded for the first time from Saudi Arabia with the description of a new species, T. saudiensis sp. nov., collected from the inner fronds of Phoenix dactylifera L. (Arecaceae), described and illustrated based on females. The individuals of the new species were vagrant on the abaxial leaf surface, causing no apparent damage to the host plant. The taxonomic status of the genus and its species was thoroughly assessed through the literature-based analysis of morphological characters. Consequently, the diagnosis of the genus Tegonotus is updated, and a key to 47 valid species is provided. Eight Tegonotus species are suggested to be transferred to three different genera within the tribe. A brief discussion on the taxonomic status of these species is provided. The position of scapular tubercles and setae (sc), and shape of the dorsal pedipalp genu seta (d), were found to be significant for the generic designation. Full article

The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [$R^2$] = 0.75) and potassium (K) ($R^2$ = 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) ($R^2$ = 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture. Full article

Non-steroidal anti-inflammatory drugs (NSAIDs) can cause hypersensitivity reactions and lead to anaphylactic shock. These drugs also act as cofactors in allergic reactions. Lipid transfer proteins (LTPs), found in plants, represent a unique group of allergens in which cofactors play a crucial role. This case report describes a 26-year-old female who developed anaphylactic symptoms after ingesting grapes and taking ketoprofen. The patient experienced swelling of the lips, tongue, and throat, as well as shortness of breath, dizziness, and loss of consciousness, after consuming grapes and taking ketoprofen. She had previously used ketoprofen and acetylsalicylic acid without issues but had developed urticaria on several occasions after consuming multi-ingredient dishes. Skin prick tests showed positive results for peanut and orange allergens. Further testing using the ALEX multiparametric test detected antibodies to several LTP allergens. Intradermal tests with ketoprofen yielded a positive result, although irritant reactions could not be ruled out. A provocation test with acetylsalicylic acid (ASA) showed no adverse reactions. Skin tests with ibuprofen were negative, and provocation tests confirmed its tolerance. A diagnosis of LTP allergy and selective ketoprofen allergy was made, with the recommendation to avoid ketoprofen and follow a diet excluding foods from the LTP group. Full article

Sustainability can be achieved through the widespread adoption of electrification across multiple sectors of activity, which would thereby enable increased operational efficiency and reduce the environmental impact. The attainment of this purpose relies on electrical circuits that convert electrical energy from renewable power plants into forms that are compatible with the specific requirements of the load. Failure of the aforementioned circuits, denominated as power converters, can lead to financial losses resulting from unexpected shutdowns and, in critical systems, can pose significant risks to human life. This article focuses on the topic of fault diagnosis in power converters. Some of the most vulnerable components of these converters are the capacitors used in the DC-link, whose failure evolves gradually. When the capacitor internal resistance (ESR) or the capacitor capacitance (C) exceeds a certain threshold value, it is advisable to propose a system shutdown, as soon as possible, to replace the capacitor. The solution presented in this article combines signal processing techniques (SPTs) with a machine learning (ML) algorithm to determine the optimal time for capacitor replacement. The ML algorithm employed herein was a logistic regression (LR) algorithm which classified the capacitor into one of two states: normal operation (0) or failure (1). To train and evaluate the LR model, two different datasets were created using various electrical quantities that can be measured non-invasively. The model demonstrated excellent performance, achieving an accuracy, precision, recall, and F1 score above 0.99. Full article