Search Results (29)

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

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

Cotton is the most widely cultivated natural fiber crop worldwide, yet it is highly susceptible to various diseases and pests that significantly compromise both yield and quality. To enable rapid and accurate diagnosis of cotton diseases and pests—thus supporting the development of effective control strategies and facilitating genetic breeding research—we propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories. Built upon the MobileViTv2 backbone, RF-Cott-Net integrates an early exit mechanism and quantization-aware training (QAT) to enhance deployment efficiency without sacrificing accuracy. Experimental results on CCDPHD-11 demonstrate that RF-Cott-Net achieves an accuracy of 98.4%, an F1-score of 98.4%, a precision of 98.5%, and a recall of 98.3%. With only 4.9 M parameters, 310 M FLOPs, an inference time of 3.8 ms, and a storage footprint of just 4.8 MB, RF-Cott-Net delivers outstanding accuracy and real-time performance, making it highly suitable for deployment on agricultural edge devices and providing robust support for in-field automated detection of cotton diseases and pests. Full article

Modern embedded nanoelectronic devices, particularly in safety-critical sectors, require high dependability throughout their lifecycle. To address this, designers have started integrating extra circuitry for on-device self-testing, such as the Logic Built-In Self-Test (LBIST). However, while automatic test equipment (ATE) ensures exhaustive testing during manufacturing, in-field testing capabilities are limited. This study introduces a novel methodology for in-field data collection of failure information from LBIST engines and a subsequent logic diagnosis strategy to facilitate failure analysis of field returns. The information is collected from key-on and key-off self-tests, executed by central processing units (CPUs) with a fixed seed and different frequency configurations, primarily addressing transition delay (TRN) faults. The proposed approach capitalizes on the constrained in-field configurability of LBIST and does not require a custom architecture, making it highly practical and readily applicable to real-world devices. The logic diagnosis strategy significantly reduces the number of candidate faults by exploiting the first failing pattern index found during the in-field testing and data collection. Reducing fault candidates could enhance accuracy during failure analysis, especially when field return devices exhibit a “No Trouble Found” (NTF) behavior. The experimental results are reported for ITC’99 benchmarks and an industrial automotive system-on-chip (SoC) produced by STMicroelectronics, with about 20 million gates. Full article

Operational amplifiers (op amps) are fundamental blocks that find wide application both as stand-alone devices and as crucial blocks embedded in various Systems on Chips (SoCs). Achieving high defect coverage, as well as performing defect localization in these circuits, has proven to be a difficult/expensive task, even with sophisticated testing circuitry. The ISO 26262 standard for functional safety (FuSa) includes the stringent requirement that an automotive IC must have a very high defect coverage. This reinforces the need to ensure the functionality of analog and mixed (AMS) circuits, especially in mission critical applications. This paper presents an all-digital op amp defect detection, diagnosis, and localization method that can be used both for production and in-field tests and discusses various implementation of the proposed method. We validated our results using extensive transistor-level simulations of multiple op amp architectures using TSMC 180 nm technology. Across op amp architectures and multiple implementation approaches, we achieved a worst-case and best-case defect coverage of 94.5% and 99%, respectively. Furthermore, in this work, we also propose a defect diagnosis and localization strategy using recorded bit streams from states of digital injectors and detectors. Full article

With the global impetus for the elimination of canine-mediated human rabies, the need for robust rabies surveillance systems has become ever more important. Many countries are working to improve their rabies surveillance programs and, as a result, the reported use of lateral flow devices (LFDs) is increasing. Despite their known diagnostic limitations, previous studies have hypothesised that the benefits associated with LFDs could make them potentially quite useful towards improving the overall robustness of surveillance programs. To test this, a best practice standard operating procedure was developed which was used to guide the implementation of the ADTEC LFD as a diagnostic screening tool in Zanzibar. Over the course of the first 22 months of this investigation, 83 samples were subjected to in-field diagnostic screening, coupled with subsequent laboratory confirmation, and only one false-negative result was detected. Furthermore, the findings of our investigation indicated that the routine use of LFDs as a diagnostic screening tool resulted in a four-fold increase in the number of samples subjected to rabies diagnosis per month and a three-fold increase in the number of wards where samples were collected per year. Our findings suggest that LFDs could play a noteworthy role in improving the robustness of surveillance systems by increasing the number of samples tested and promoting diagnostic screening in areas distant from laboratories. Their implementation would, however, need to be carefully controlled through standardised protocols that align with the international best practices to ensure their judicious use. Full article

The vector-transmitted Citrus Greening (CG) disease, also called Huanglongbing, is one of the most destructive diseases of citrus. Since no measures for directly controlling this disease are available at present, current disease management integrates several measures, such as vector control, the use of disease-free trees, the removal of diseased trees, etc. The most essential issue in integrated management is how CG-infected trees can be detected efficiently. For CG detection, digital image analyses using deep learning algorithms have attracted much interest from both researchers and growers. Models using transfer learning with the Faster R-CNN architecture were constructed and compared with two pre-trained Convolutional Neural Network (CNN) models, VGGNet and ResNet. Their efficiency was examined by integrating their feature extraction capabilities into the Convolution Block Attention Module (CBAM) to create VGGNet+CBAM and ResNet+CBAM variants. ResNet models performed best. Moreover, the integration of CBAM notably improved CG disease detection precision and the overall performance of the models. Efficient models with transfer learning using Faster R-CNN were loaded on web applications to facilitate access for real-time diagnosis by farmers via the deployment of in-field images. The practical ability of the applications to detect CG disease is discussed. Full article

Degradation is an inevitable companion in the operation of solid oxide fuel cell (SOFC) systems since it directly deteriorates the reliability of the system’s operation and the system’s durability. Both are seen as barriers that limit the extensive commercial use of SOFC systems. Therefore, diagnosis and prognosis are valuable tools that can contribute to raising the reliability of the system operation, efficient health management, increased durability and implementation of predictive maintenance techniques. Remaining useful life (RUL) prediction has been extensively studied in many areas like batteries and proton-exchange membrane fuel cell (PEM) systems, and a range of different approaches has been proposed. On the other hand, results available in the domain of SOFC systems are still relatively limited. Moreover, methods relying on detailed process models and models of degradation turned out to have limited applicability for in-field applications. Therefore, in this paper, we propose an effective, data-driven approach to predicting RUL where the trend of the health index is modeled by an adaptive linear model, which is updated at all times during the system operation. This allows for a closed-form solution of the probability distribution of the RUL, which is the main novelty of this paper. Such a solution requires no computational load and is as such very convenient for the application in ordinary low-cost control systems. The performance of the approach is demonstrated first on the simulated case studies and then on the data obtained from a long-term experiment on a laboratory SOFC system. From the tests conducted so far, it turns out that the quality of the RUL prediction is usually rather low at the beginning of the system operation, but then gradually improves while the system is approaching the end-of-life (EOL), making it a viable tool for prognosis. Full article

Introduction: The rate of isolated locoregional recurrence after surgery for pancreatic adenocarcinoma (PDAC) approaches 25%. Ablative radiation therapy (A-RT) has improved outcomes for locally advanced disease in the primary setting. We sought to evaluate the outcomes of salvage A-RT for isolated locoregional recurrence and examine the relationship between subsequent patterns of failure, radiation dose, and treatment volume. Methods: We conducted a retrospective analysis of all consecutive participants who underwent A-RT for an isolated locoregional recurrence of PDAC after prior surgery at our institution between 2016 and 2021. Treatment consisted of ablative dose (BED10 98–100 Gy) to the gross disease with an additional prophylactic low dose (BED10 < 50 Gy), with the elective volume covering a 1.5 cm isotropic expansion around the gross disease and the circumference of the involved vessels. Local and locoregional failure (LF and LRF, respectively) estimated by the cumulative incidence function with competing risks, distant metastasis-free and overall survival (DMFS and OS, respectively) estimated by the Kaplan–Meier method, and toxicities scored by CTCAE v5.0 are reported. Location of recurrence was mapped to the dose region on the initial radiation plan. Results: Among 65 participants (of whom two had two A-RT courses), the median age was 67 (range 37–87) years, 36 (55%) were male, and 53 (82%) had undergone pancreaticoduodenectomy with a median disease-free interval to locoregional recurrence of 16 (range, 6–71) months. Twenty-seven participants (42%) received chemotherapy prior to A-RT. With a median follow-up of 35 months (95%CI, 26–56 months) from diagnosis of recurrence, 24-month OS and DMFS were 57% (95%CI, 46–72%) and 22% (95%CI, 14–37%), respectively, while 24-month cumulative incidence of in-field LF and total LRF were 28% (95%CI, 17–40%) and 36% (95%CI 24–48%), respectively. First failure after A-RT was distant in 35 patients (53.8%), locoregional in 12 patients (18.5%), and synchronous distant and locoregional in 10 patients (15.4%). Most locoregional failures occurred in elective low-dose volumes. Acute and chronic grade 3–4 toxicities were noted in 1 (1.5%) and 5 patients (7.5%), respectively. Conclusions: Salvage A-RT achieves favorable OS and local control outcomes in participants with an isolated locoregional recurrence of PDAC after surgical resection. Consideration should be given to extending high-dose fields to include adjacent segments of at-risk vessels beyond direct contact with the gross disease. Full article

Artificial intelligence (AI) is revolutionizing approaches in plant disease management and phytopathological research. This review analyzes current applications and future directions of AI in addressing evolving agricultural challenges. Plant diseases annually cause 10–16% yield losses in major crops, prompting urgent innovations. Artificial intelligence (AI) shows an aptitude for automated disease detection and diagnosis utilizing image recognition techniques, with reported accuracies exceeding 95% and surpassing human visual assessment. Forecasting models integrating weather, soil, and crop data enable preemptive interventions by predicting spatial-temporal outbreak risks weeks in advance at 81–95% precision, minimizing pesticide usage. Precision agriculture powered by AI optimizes data-driven, tailored crop protection strategies boosting resilience. Real-time monitoring leveraging AI discerns pre-symptomatic anomalies from plant and environmental data for early alerts. These applications highlight AI’s proficiency in illuminating opaque disease patterns within increasingly complex agricultural data. Machine learning techniques overcome human cognitive constraints by discovering multivariate correlations unnoticed before. AI is poised to transform in-field decision-making around disease prevention and precision management. Overall, AI constitutes a strategic innovation pathway to strengthen ecological plant health management amidst climate change, globalization, and agricultural intensification pressures. With prudent and ethical implementation, AI-enabled tools promise to enable next-generation phytopathology, enhancing crop resilience worldwide. Full article

The fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), is native to tropical and subtropical regions of the Western Hemisphere, but is now regularly appearing in crop fields across South Korea, particularly in corn fields. Therefore, it is crucial to promptly and accurately identify the presence of FAW in crop fields to effectively eradicate it as a regulated quarantine species. We developed a loop-mediated isothermal amplification (LAMP) assay, which allows for rapid in-filed identification. To develop the LAMP assay, we selected FAW-specific genomic regions from the whole-genome sequences of one FAW and 13 other lepidopteran species and validated five primer sets that consistently produced positive reactions in ten FAW samples collected from eight different locations in four countries. The assay successfully identified FAW in a maximum of 45 min, starting from crude DNA extraction (~15 min) to diagnosis (30 min) from the following samples, which were deposited outdoors for 30 days: a 1st-instar larva, an adult leg, an adult antenna, and 1/16 and 1/8 of an adult thorax. The five assays can be used selectively or in combination to cross-check and provide further confidence in the in-field diagnosis of FAW. Full article

Stewart’s vascular wilt and leaf blight of sweet corn is caused by the Gram-negative enteric bacterium Pantoea stewartii subsp. stewartii. Stewart’s wilt results in substantial yield losses worldwide warranting rapid and accurate disease diagnosis. Recombinase polymerase amplification (RPA) is an isothermal technique that is tolerant to host plant-derived inhibitors and is, therefore, ideally suited for rapid in-field detection vis-à-vis traditional polymerase chain reaction-based molecular assays. An RPA assay coupled with a Lateral Flow Device (LFD) was developed for rapid, accurate, and sensitive real-time detection of P. stewartii subsp. stewartii directly from the infected host offering in-field pathogen detection, timely disease management, and satisfying quarantine and phytosanitary requirements. Twelve novel primer sets were designed against conserved genomic regions of P. stewartii subsp. Stewartii; however, only the primers for amplification of the intergenic spacer region between capsular polysaccharide genes cpsA and cpsB were discernibly unique and adequate for unambiguous identification of P. stewartii subsp. stewartii. The P. stewartii subsp. stewartii-specific primers were further validated in a simplex RPA assay for specificity against twenty-six bacterial species representing several Pantoea and other closely related bacterial species/subspecies/strains found in the same niche, and naturally or artificially infected plant samples. The integrated RPA/LFD assay was also optimized for rapid and sensitive on-site detection of P. stewartii subsp. stewartii with an empirical detection limit of 0.0005 pg μL⁻¹ bacterial DNA and 1 × 10² CFU mL⁻¹ (app. two bacterial cells used per RPA reaction) in minimally processed samples for accurate, low-cost, and point-of-need diagnosis of the quarantine pathogen P. stewartii subsp. stewartii. Full article

The potential of hyperspectral UV–VIS–NIR reflectance for the in-field, non-destructive discrimination of bacterial canker on kiwi leaves caused by Pseudomonas syringae pv. actinidiae (Psa) was analyzed. Spectral data (325–1075 nm) of twenty kiwi plants were obtained in vivo and in situ with a handheld spectroradiometer in two commercial kiwi orchards in northern Portugal over 15 weeks, resulting in 504 spectral measurements. The suitability of different vegetation indexes (VIs) and applied predictive models (based on supervised machine learning algorithms) for classifying non-symptomatic and symptomatic kiwi leaves was evaluated. Eight distinct types of VIs were identified as relevant for disease diagnosis, highlighting the relevance of the Green, Red, Red-Edge, and NIR spectral features. The class prediction was achieved with good model metrics, achieving an accuracy of 0.71, kappa of 0.42, sensitivity of 0.67, specificity of 0.75, and F1 of 0.67. Thus, the present findings demonstrated the potential of hyperspectral UV–VIS–NIR reflectance for the non-destructive discrimination of bacterial canker on kiwi leaves. Full article

Arboviruses are a diverse family of vector-borne pathogens that include members of the Flaviviridae, Togaviridae, Phenuviridae, Peribunyaviridae, Reoviridae, Asfarviridae, Rhabdoviridae, Orthomyxoviridae and Poxviridae families. It is thought that new world arboviruses such as yellow fever virus emerged in the 16th century due to the slave trade from Africa to America. Severe disease-causing viruses in humans include Japanese encephalitis virus (JEV), yellow fever virus (YFV), dengue virus (DENV), West Nile virus (WNV), Zika virus (ZIKV), Crimean–Congo hemorrhagic fever virus (CCHFV), severe fever with thrombocytopenia syndrome virus (SFTSV) and Rift Valley fever virus (RVFV). Numerous methods have been developed to detect the presence of these pathogens in clinical samples, including enzyme-linked immunosorbent assays (ELISAs), lateral flow assays (LFAs) and reverse transcriptase–polymerase chain reaction (RT-PCR). Most of these assays are performed in centralized laboratories due to the need for specialized equipment, such as PCR thermal cyclers and dedicated infrastructure. More recently, molecular methods have been developed which can be performed at a constant temperature, termed isothermal amplification, negating the need for expensive thermal cycling equipment. In most cases, isothermal amplification can now be carried out in as little as 5–20 min. These methods can potentially be used as inexpensive point of care (POC) tests and in-field deployable applications, thus decentralizing the molecular diagnosis of arboviral disease. This review focuses on the latest developments in isothermal amplification technology and detection techniques that have been applied to arboviral diagnostics and highlights future applications of these new technologies. Full article

Introduction: We aimed to identify the role of radiotherapy (RT) in the treatment of thymic carcinoma as well as the optimal RT target volume. Materials and Methods: This single-institution retrospective study included 116 patients diagnosed with thymic carcinoma between November 2006 and December 2021 who received multimodal treatment including RT with or without surgery or chemotherapy. Seventy-nine patients (68.1%) were treated with postoperative RT, 17 patients (14.7%) with preoperative RT, 11 patients (9.5%) with definitive RT, and nine patients (7.8%) with palliative RT. The target volume was defined as the tumor bed or gross tumor with margin, and selective irradiation of the regional nodal area was performed when involved. Results: With a median follow-up of 37.0 (range, 6.7–174.3) months, the 5-year overall survival (OS), progression-free survival, and local recurrence-free survival rates were 75.2%, 47.7% and 94.7%, respectively. The 5-year OS was 51.9% in patients with unresectable disease. Overall, 53 recurrences were observed, of which distant metastasis was the most common pattern of failure (n = 32, 60.4%) after RT. No isolated infield or marginal failures were observed. Thirty patients (25.8%) who had lymph node metastases at the initial diagnosis had regional nodal areas irradiated. There was no lymph node failure inside the RT field. A tumor dimension of ≥5.7 cm (hazard ratio [HR] 3.01; 95% confidence interval [CI] 1.25–7.26; p = 0.030) and postoperative RT (HR 0.20; 95% CI 0.08–0.52; p = 0.001) were independently associated with OS. Intensity-modulated-RT-treated patients developed less overall toxicity (p < 0.001) and esophagitis (p < 0.021) than three-dimensional-conformal-RT-treated patients. Conclusions: A high local control rate was achieved with RT in the primary tumor sites and involved lymph node area in the treatment of thymic carcinoma. A target volume confined to the tumor bed or gross tumor plus margin with the involved lymph node stations seems reasonable. The advanced RT techniques with intensity-modulated RT have led to reduced RT-related toxicity. Full article