Search Results (1,744)

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

Botryosphaeria dothidea is the causative agent of Chinese hickory trunk canker, which poses significant threat to the production of Chinese hickory (Carya cathayensis Sarg.). Previous studies reported that endophytic–pathogenic phase transition, also referred to as latent infection, plays an important role in the interaction of Botryosphaeria dothidea with various host plants, including Chinese hickory. However, the mechanism underlying this phase transition is not well understood. Here, we employed RNA-Seq to investigate transcriptional changes in B. dothidea during its phase transition upon interaction with Chinese hickory. A co-expression network was generated based on 6391 differentially expressed genes (DEGs) identified from different infection stages and temperature treatments. One co-expressed module was found that highly correlated with temperature treatments which simulated conditions of B. dothidea latent infection in the field. Subsequently, 53 hub genes were detected, and gene ontology (GO) enrichment analysis revealed three categories of enriched GO terms: transmembrane transport or activity, ion homeostasis or transport, and carbohydrate metabolism. One PacC transcriptional factor (BDLA_00001555, an ambient pH regulator), and one endo-β-1,3-glucanase (BDLA_00010249) were specifically upregulated under temperature treatments that corresponded with the activation stage of B. dothidea’s pathogenic state. The knockout mutant strain of BDLA_00001555 demonstrated defective capability upon the activation of the pathogenic state. This confirmed that BDLA_00001555, the PacC transcriptional factor, plays an important role in the latent infection phase of B. dothidea. Our findings provide insights into the pathogenic mechanism of Chinese hickory trunk canker disease. Full article

In recent years, research on plant disease detection has combined artificial intelligence, hyperspectral imaging, unmanned aerial vehicle remote sensing, and other technologies, promoting the transformation of pest and disease control in smart agriculture towards digitalization and artificial intelligence. This review systematically elaborates on the research status of non-destructive detection techniques used for plant disease identification and detection, mainly introducing the following two types of methods: spectral technology and imaging technology. It also elaborates, in detail, on the principles and application examples of each technology and summarizes the advantages and disadvantages of these technologies. This review clearly indicates that non-destructive detection techniques can achieve plant disease and pest detection quickly, accurately, and without damage. In the future, integrating multiple non-destructive detection technologies, developing portable detection devices, and combining more efficient data processing methods will become the core development directions of this field. Full article

Potatoes are one of the world’s most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

Although Brassica rapa (B. rapa) is vital in agricultural production and vulnerable to the pathogen Plasmodiophora, the intracellular water–nutrient metabolism and immunoregulation of Plasmodiophora infection in B. rapa leaves remain unclear. This study aimed to analyze the responsive mechanisms of Plasmodiophora-infected B. rapa using rapid detection technology. Six soil groups planted with Yangtze No. 5 B. rapa were inoculated with varying Plasmodiophora concentrations (from 0 to 10 × 10⁹ spores/mL). The results showed that at the highest infection concentration (PWB5, 10 × 10⁹ spores/mL) of B. rapa leaves, the plant electrophysiological parameters showed the intracellular water-holding capacity (IWHC), the intracellular water use efficiency (IWUE), and the intracellular water translocation rate (IWTR) declined by 41.99–68.86%. The unit for translocation of nutrients (UNF) increased by 52.83%, whereas the nutrient translocation rate (NTR), the nutrient translocation capacity (NTC), the nutrient active translocation (NAT) value, and the nutrient active translocation capacity (NAC) decreased by 52.40–77.68%. The cellular energy metabolism decreased with worsening Plasmodiophora infection, in which the units for cellular energy metabolism (∆G_E) and cellular energy metabolism (∆G) of the leaves decreased by 44.21% and 78.14% in PWB5, respectively. Typically, based on distribution of B-type dielectric substance transfer percentage (BPn), we found PWB4 (8 × 10⁹ spores/mL) was the maximal immune response concentration, as evidenced by a maximal BPn_R (B-type dielectric substance transfer percentage based on resistance), with increasing lignin and cork deposition to enhance immunity, and a minimum BPn_Xc (B-type dielectric substance transfer percentage based on capacitive reactance), with a decreasing quantity of surface proteins in the B. rapa leaves. This study suggests plant electrophysiological parameters could characterize intracellular water–nutrient metabolism and immunoregulation of B. rapa leaves under various Plasmodiophora infection concentrations, offering a dynamic detection method for agricultural disease management. Full article

Polyphenol oxidase (PPO) is a metalloproteinase widely present in plant organelles that plays crucial roles in photosynthesis, pest and disease resistance, growth and development, and flower color formation. Due to the high cost and reuse difficulties of plant PPO in applications, immobilization has emerged as a key technology to improve its stability, recyclability, and reusability. Immobilized plant PPO has been widely used in environmental and detection fields. This review examines different immobilization methods and carrier materials for plant PPO and summarizes its applications in wastewater treatment, biosensor detection, food preservation, and theaflavin synthesis. Finally, current challenges and future opportunities for immobilized plant PPO are discussed. Full article

SMoV and SVBV are two major viruses that pose significant threats to the global strawberry industry. Both are latent viruses, making early detection difficult due to their uneven distribution and low concentration in host tissues. Traditional RT-PCR techniques are insufficient for precise and quantitative detection. In this study, TaqMan RT-qPCR detection systems for SMoV and SVBV were established for application in practical production settings, enabling accurate, rapid, and efficient detection of strawberry viruses. When viral accumulation in plants is low, the highly sensitive TaqMan RT-qPCR technique allows for accurate quantification, facilitating the early identification of infected plants and preventing large-scale outbreaks in cultivation areas. The development of a duplex TaqMan RT-qPCR assay enables simultaneous quantification of SMoV and SVBV in a single reaction, improving detection efficiency and providing technical support for risk assessment and effective control of strawberry viral diseases. 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

The spoilage of dairy products and foodborne diseases caused by Bacillus spp. are important public concerns. The objective of this study was to estimate the global prevalence of Bacillus spp. in a range of milk and dairy products by using a meta-analysis of literature data published between 2001 and 2023. A total of 3624 publications were collected from Web of Science and PubMed databases. Following the principles of systematic review, 417 sets of prevalence data were extracted from 142 eligible publications. Estimated by the random-effects model, the overall prevalence of Bacillus spp. in milk and dairy products was 11.8% (95% CI: 10.1–13.7%), with highly severe heterogeneity (94.8%). Subgroup analyses revealed substantial heterogeneity in Bacillus spp. prevalence according to geographical continents, sources of sampling, types of dairy products, microbial species, and detection methods. The prevalence of Bacillus spp. was highest in Asia (15.4%, 95% CI: 12.3–19.1%), lowest in Oceania (3.5%, 95% CI: 3.3–3.7%) and generally higher in developing versus developed countries. The prevalence of Bacillus spp. isolated from retail markets (16.1%, 95% CI: 13.0–19.7%) was higher than from farms (10.3%, 95% CI: 6.9–15.0%) or dairy plants (9.2%, 95% CI: 7.1–12.0%). This finding is likely attributable to its inherent characteristic of the resistant endospores and ubiquitous presence in the environment—Bacillus spp. can potentially cyclically contaminate farms, dairy products and human markets. Regarding the species distribution, Bacillus cereus presented a cosmopolitan distribution across all continents. The epidemic patterns of different Bacillus species vary depending on the sample sources. In addition, the detection method utilized also affected the reported prevalence of Bacillus spp. It is recommended to use molecular-based rapid detection methods to obtain a more accurate prevalence of Bacillus contamination. Therefore, a better understanding of variations in Bacillus spp. prevalence across different factors will enable competent authorities, industries, and other relevant stakeholders to tailor their interventions for effectively controlling Bacillus spp. in milk and dairy products. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

This study identifies and classifies resistance gene analogues (RGAs) in the genomes of Brassica nigra, Sinapis arvensis and Sinapis alba using the RGAugury pipeline. RGAs were categorised into four main classes: receptor-like kinases (RLKs), receptor-like proteins (RLPs), nucleotide-binding leucine-rich repeat (NLR) proteins and transmembrane-coiled-coil (TM-CC) genes. A total of 4499 candidate RGAs were detected, with species-specific proportions. RLKs were the most abundant across all genomes, followed by TM-CCs and RLPs. The sub-classification of RLKs and RLPs identified LRR-RLKs, LRR-RLPs, LysM-RLKs, and LysM-RLPs. Atypical NLRs were more frequent than typical ones in all species. Atypical NLRs were more frequent than typical ones in all species. We explored the relationship between chromosome size and RGA count using regression analysis. In B. nigra and S. arvensis, larger chromosomes generally harboured more RGAs, while S. alba displayed the opposite trend. Exceptions were observed in all species, where some larger chromosomes contained fewer RGAs in B. nigra and S. arvensis, or more RGAs in S. alba. The distribution and density of RGAs across chromosomes were examined. RGA distribution was skewed towards chromosomal ends, with patterns differing across RGA types. Sequence hierarchical pairwise similarity analysis revealed distinct gene clusters, suggesting evolutionary relationships. The study also identified homologous genes among RGAs and non-RGAs in each species, providing insights into disease resistance mechanisms. Finally, RLKs and RLPs were co-localised with reported disease resistance loci in Brassica, indicating significant associations. Phylogenetic analysis of cloned RGAs and QTL-mapped RLKs and RLPs identified distinct clusters, enhancing our understanding of their evolutionary trajectories. These findings provide a comprehensive view of RGA diversity and genomics in these Brassicaceae species, providing valuable insights for future research in plant disease resistance and crop improvement. Full article

Several species of Fusarium are reported to infect inflorescences of high-THC-containing cannabis (Cannabis sativa L.) plants grown in greenhouses in Canada. These include F. graminearum, F. sporotrichiodes, F. proliferatum, and, to a lesser extent, F. oxysporum and F. solani. The greatest concern surrounding the infection of cannabis by these Fusarium species, which cause symptoms of bud rot, is the potential for the accumulation of mycotoxins that may go undetected. In the present study, both naturally infected and artificially infected inflorescence tissues were tested for the presence of fungal-derived toxins using HPLC-MS/MS analysis. Naturally infected cannabis tissues were confirmed to be infected by both F. avenaceum and F. graminearum using PCR. Pure cultures of these two species and F. sporotrichiodes were inoculated onto detached inflorescences of two cannabis genotypes, and after 7 days, they were dried and assayed for mycotoxin presence. In these assays, all Fusarium species grew prolifically over the tissue surface. Tissues infected by F. graminearum contained 3-acetyl DON, DON, and zearalenone in the ranges of 0.13–0.40, 1.18–1.91, and 31.8 to 56.2 μg/g, respectively, depending on the cannabis genotype. In F. sporotrichiodes-infected samples, HT2 and T2 mycotoxins were present at 13.9 and 10.9 μg/g in one genotype and were lower in the other. In F. avenaceum-inoculated tissues, the mycotoxins enniatin A, enniatin A1, enniatin B, and enniatin B1 were produced at varying concentrations, depending on the isolate and cannabis genotype. Unexpectedly, these tissues also contained detectable levels of 3-acetyl DON, DON, and zearalenone, which was attributed to apre-existing natural infection by F. graminearum that was confirmed by RT-qPCR. Beauvericin was detected in tissues infected by F. avenaceum and F. sporotrichiodes, but not by F. graminearum. Naturally infected, dried inflorescences from which F. avenaceum was recovered contained beauvericin, enniatin A1, enniatin B, and enniatin B1 as expected. Uninoculated cannabis inflorescences were free of mycotoxins except for culmorin at 0.348 μg/g, reflecting pre-existing infection by F. graminearum. The mycotoxin levels were markedly different between the two cannabis genotypes, despite comparable mycelial colonization. Tall fescue plants growing in the vicinity of the greenhouse were shown to harbor F. avenaceum and F. graminearum, suggesting a likely external source of inoculum. Isolates of both species from tall fescue produced mycotoxins when inoculated onto cannabis inflorescences. These findings demonstrate that infection by F. graminearum and F. avenaceum, either from artificial inoculation or natural inoculum originating from tall fescue plants, can lead to mycotoxin accumulation in cannabis inflorescences. However, extensive mycelial colonization following prolonged incubation of infected tissues under high humidity conditions is required. Inoculations with Penicillium citrinum and Aspergillus ochraceus under these conditions produced no detectable mycotoxins. The mycotoxins alternariol and tentoxin were detected in several inflorescence samples, likely as a result of natural infection by Alternaria spp. Fusarium avenaceum is reported to infect cannabis inflorescences for the first time and produces mycotoxins in diseased tissues. Full article

Wheat leaf rust (Lr), caused by Puccinia triticina Eriks. (Pt), is one of the most important diseases affecting wheat production worldwide. Using resistant wheat cultivars is the most economic and environmentally friendly way to control leaf rust. The Chinese wheat cultivar Xinong1163-4 has shown good resistance to Lr in field trials. To identify the genetic basis of Lr resistance in Xinong1163-4, 195 recombinant inbred lines (RILs) from the Xinong1163-4/Thatcher cross were phenotyped for Lr severity in three environments: the 2017/2018, 2018/2019, and 2019/2020 growing seasons in Baoding, Hebei Province. Bulked segregant analysis and simple sequence repeat markers were then used to identify the quantitative trait loci (QTLs) for Lr adult plant resistance (APR) in the population. As a result, six QTLs were detected, designated as QLr.hbau-1BL.1, QLr.hbau-1BL.2, and QLr.hbau-1BL.3. These QTLs were predicted to be novel. QLr.hbau-4BL, QLr.hbau-4BL.1, and QLr.hbau-3A were identified at similar physical positions to previously reported QTLs. Based on chromosome positions and molecular marker testing, QLr.hbau-1BL.3 shares similar flanking markers with Lr46. Lr46 is a non-race-specific APR gene for leaf rust, stripe rust, and powdery mildew. Similarly, QLr.hebau-4BL showed resistance to multiple diseases, including leaf rust, stripe rust, Fusarium head blight, and powdery mildew. The QTLs identified in this study, as well as their closely linked markers, can potentially be used for marker-assisted selection in wheat breeding. Full article