Search Results (123)

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

Brassinosteroids (BRs) are plant growth regulators (PGRs) with pleiotropic effects on plant growth and development. They play a role in seed germination, vegetative and reproductive growth, photosynthetic efficiency, vascular differentiation, fruit yield, quality, and resilience to biotic and abiotic stresses. They engage in crosstalk with other hormones like auxin, gibberellins, ethylene and abscisic acid, influencing all plant growth and development aspects. Studies on the effect of BRs on the reproductive growth of fruit crops are accumulating, given the potential of this PGR as a management tool in agriculture. This review explores the multifaceted roles of BRs in fruit crop maturation. From their biosynthesis and signal transduction pathways to their influence on fruit production, development, and maturation, we focus on the effect of this plant hormone on different aspects of fruit yield and quality, including fruit set and firmness, sugar accumulation, and fruit development. We address BRs’ interaction with different hormones at molecular and physiological levels in regulating these processes in climacteric and non-climacteric fruits. We also identify areas where knowledge is still lacking regarding hormonal crosstalk involving BRs in the regulation of developmental processes governing fruit quality and yield so knowledge generated can inform management decisions in fruit crop production. Full article

Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. Genomic (G) data were combined with hyperspectral (H) and multispectral + thermal (M) imaging across the 2022 and 2023 growing seasons at the Plant Science Research and Education Unit, Citra, Florida. A panel of 312 wheat genotypes was analyzed using GBLUP-based models, integrating G + H and G + M data from SP to predict BP yield. SP models demonstrated promising predictive ability, with G + H models achieving moderate within-year (0.43 to 0.51) and across-year (0.43) prediction accuracies, while G + M models reached 0.53 to 0.58 and 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy of 0.47 when M data from the 2022 SP, combined with G, was used to predict BP yield in 2023. Additionally, the top 25% specificity (coincide index) was evaluated, with models showing up to 47–51% within a year and 43–45% between years overlap in the highest predicted-yielding lines between SP and BP trials, further emphasizing the potential of SP data for early selection. These findings suggest that SP trials can provide meaningful predictions for BP yields, enabling earlier selection and faster breeding cycles. Full article

Tropical cyclones can severely disturb shallow, continental shelf ecosystems, affecting habitat structure, diversity, and ecosystem services. This study examines the impacts of Hurricane Ian on the Southwest Florida Shelf by assessing water quality, substrate type, and epibenthic and microbial community characteristics at eight sites (3 to 20 m in depth) before and after Ian’s passage in 2022. Hurricane Ian drastically changed substrate type and biotic cover, scouring away epibenthos and/or burying hard substrates in mud and sand, especially at mid depth (10 m) sites (92–98% loss). Following Hurricane Ian, the greatest losses were observed in fleshy macroalgae (58%), calcareous green algae (100%), seagrass (100%), sessile invertebrates (77%), and stony coral communities (71%), while soft coral (17%) and sponge communities (45%) were more resistant. After Ian, turbidity, chromophoric dissolved organic matter, and dissolved inorganic nitrogen and phosphorus increased at most sites, while total nitrogen, total phosphorus, and silica decreased. Microbial communities changed significantly post Ian, with estuary-associated taxa expanding further offshore. The results show that the shelf ecosystem is highly susceptible to disturbances from waves, deposition and erosion, and water quality changes caused by mixing and coastal discharge. More routine monitoring of this environment is necessary to understand the long-term patterns of these disturbances, their interactions, and how they influence the resilience and recovery processes of shelf ecosystems. Full article

Florida citrus production has declined by over 90% since the bacterial disease huanglongbing (HLB) was found in the state. In the absence of an effective cure, growers are adopting more frequent fertilization and irrigation practices to improve tree health and prolong the life span of their orchards. However, Florida’s soils under citrus production are sandy, with little organic matter, a low water holding capacity, and a low cation exchange capacity (CEC), rendering them prone to nutrient leaching. Organic amendments can be used to improve soil health and the environment for citrus roots, but may promote a higher incidence of weeds competing with trees for water and nutrients. A large field trial was established in a commercial citrus orchard in southwest Florida to evaluate the effects of organic amendments and weed management on young tree growth. The organic amendment treatments were as follows: (1) plant-based compost, (2) humic acid, and (3) a non-amended control. The weed management (herbicide) treatments were (1) glyphosate, (2) glufosinate, (3) flumioxazin, and (4) a maintenance herbicide control. Trees were planted in August 2019, and treatments began in 2021. Tree growth and physiological variables and soil physicochemical properties were evaluated during the two-year study. Compost-amended plots had a higher volumetric water content throughout the experiment, and soil nutrient content, organic matter, CEC, and pH were higher after two years of application. Humic acid amendments were less effective in altering these soil properties. Compost’s effects on tree and fibrous root physiology were moderate, and tree growth, fruit yield and fruit quality were not affected by either organic amendment. In contrast, the use of post-emergent herbicides (glyphosate and glufosinate) improved tree growth and nutrient uptake. The results suggest that in Florida, the use of organic amendments needs to be integrated with weed management to prevent resource competition. In the short term, these practices did not improve the productivity of the trees in the current Florida production environment. Full article

In the absence of long-term hydrologic records, field-measured hydrologic indicators are useful for inferring past wetland hydrologic conditions, which can support research, regulation, and restoration. Inflection points on the buttresses of pondcypress trees (Taxodium ascendens) are frequently used in west-central Florida to estimate cypress wetland high water levels, known as normal pool. However, little is known about how this indicator develops. A method to estimate tree age using diameter at breast height was developed for Florida pondcypress, which can be used by forested wetland managers to constrain the maximum potential age of hydrologic indicators in groups of cypress trees. This model was applied to a waterbody with a complex history of hydrologic alterations. The waterbody had two distinct populations of buttress inflection elevations, corresponding to historic versus current water level regimes. This represents one of the first documented instances in the literature where a waterbody showed multiple buttress inflection populations in the absence of soil subsidence. This work underscores the need to consider the development timelines when interpreting the hydrologic meaning of indicator elevations. Full article

This study presents a methodological framework for predicting soil organic carbon (SOC) using laboratory spectral recordings from a handheld near-infrared (NIR, 1350–2550 nm) device combined with open geospatial data derived from remote sensing sensors related to landform, climate, and vegetation. Initial experiments proved the superiority of convolutional neural networks (CNNs) using only spectral data captured by the low-cost spectral devices reaching an $R^2$ of 0.62, RMSE of 0.31 log-SOC, and an RPIQ of 1.87. Furthermore, the incorporation of geo-covariates with Neo-Spectra data substantially enhanced predictive capabilities, outperforming existing approaches. Although the CNN-derived spectral features had the greatest contribution to the model, the geo-covariates that were most informative to the model were primarily the rainfall data, the valley bottom flatness, and the snow probability. The results demonstrate that hybrid modeling approaches, particularly using CNNs to preprocess all features and fit prediction models with Extreme Gradient Boosting trees, CNN-XGBoost, significantly outperformed traditional machine learning methods, with a notable RMSE reduction, reaching an $R^2$ of 0.72, and an RPIQ of 2.17. The findings of this study highlight the effectiveness of multimodal data integration and hybrid models in enhancing predictive accuracy for SOC assessments. Finally, the application of interpretable techniques elucidated the contributions of various climatic and topographical factors to predictions, as well as spectral information, underscoring the complex interactions affecting SOC variability. Full article

Gray snapper (Lutjanus griseus) move from inshore to offshore habitats as they mature and spawn along the West Florida Shelf. The connectivity between offshore spawning sites and inshore settlement regions along the Eastern Gulf of America (formerly Gulf of Mexico, hereafter Gulf) coast is unknown, and this study therefore predicted these larval dispersal pathways. To determine larval transport, an ocean model was integrated with the Connectivity Modeling System (CMS), which is a biophysical model that allowed for the inclusion of larval behavior and updated spawning information for the gray snapper. Our larval dispersal model showed that spawning sites offshore of Tampa, in the Florida Keys, and in the Florida Middle Grounds had the highest percentages of successfully settled larvae inshore. Larvae that were spawned at the offshore Tampa Bay and offshore Southwest Florida spawning sites were mostly transported to the Tampa Bay and Southwest Florida settlement regions, showing local retention. In contrast, larvae spawned offshore in the Florida Middle Grounds were transported northwest, exclusively to the Florida Panhandle. In addition, there was no difference in the proportion of successful larval settlers between full and new moon spawning events. Since gray snapper are an important recreational fishery in the eastern Gulf, especially off the west coast of Florida, it is important to identify spawning sites that have the largest proportions of settling larvae, such as offshore Tampa Bay. Full article

This study evaluates the potential of integrating visible and near-infrared (visNIR) spectroscopy, color analysis, and firmness testing for non-destructive tomato quality assessment and shelf-life prediction. Tomato fruit (cv. HM1823) harvested at four ripening stages were monitored over 12 days at 22 °C to investigate ripening stage-specific variations in key quality parameters, including color (hue angle), firmness (compression), and nutritional composition (pH, soluble solids content, and titratable acidity ratio). Significant changes in these parameters during storage highlighted the need for advanced tools to monitor and predict quality attributes. Spectral data (340–2500 nm) captured using advanced and cost-effective portable spectroradiometers, coupled with chemometric models such as partial least squares regression (PLSR), demonstrated reliable predictions of shelf-life and nutritional quality. The near-infrared spectrum (900–1700 nm) was particularly effective, with variable selection methods such as genetic algorithm (GA) and variable importance in projection (VIP) scores enhancing model accuracy. This study highlights the promising role of visNIR spectroscopy as a rapid, non-destructive tool for optimizing postharvest management in tomato. By enabling real-time quality assessments, these technologies support sustainable agricultural practices through improved decision-making, reduced postharvest losses, and enhanced consumer satisfaction. The findings also validate the utility of affordable spectroradiometers, offering practical solutions for stakeholders aiming to balance cost efficiency and reliability in postharvest quality monitoring. Full article

In order to study the effects of emerging climate change on the cultivation of broccoli (Brassica oleraceae var. italica Plenck), transplants of three F1 hybrids (‘Cigno’, ‘Principe’, and ‘Domino’ F1) were transplanted on three successive dates (7 June, 30 June, and 4 August) at the Experimental Farm of the Aristotle University of Thessaloniki, Greece. The last planting date (4 August) corresponds to the most common establishment time for the crop in the area, while the other two dates correspond to periods with higher temperatures. The number of leaves per plant was recorded on a weekly basis during the growing period, while the plant height, the number of head leaves, the number of lateral shoots, the head diameter, and the weight and quality of the head were recorded at harvest. The results showed that the average temperature and solar radiation during the first two growing periods (GP1 and GP2) were higher by 4.4–5.4 °C and 32–75%, respectively, compared to the third one (GP3). The consequences of the higher temperature were the shortening of the growing period between transplanting and harvest by 5–6 days in ‘Cigno’ F1 and its extension by 3–18 days in the ‘Principe’ and ‘Domino’ F1 ones, as well as the increase in the quantity of water required through irrigation by 14–61%. Higher temperatures induced a significant deterioration of the head quality and a reduction in marketable production by 42–92%. Full article

Plant diseases pose significant economic challenges and may lead to ecological consequences. Although plant pathologists have a significant ability to diagnose plant diseases, rapid, accurate, and early diagnosis of plant diseases by intelligent systems could improve disease control and management. This study evaluates six efficient classification models (classifiers) based on deep learning to detect common tomato diseases by analyzing symptomatic patterns on leaves. Additionally, group learning techniques, including simple and weighted majority voting methods, were employed to enhance classification performance further. Six tomato leaf diseases, including Pseudomonas syringae pv. syringae bacterial spot, Phytophthora infestance late blight, Cladosporium fulvum leaf mold, Septoria lycopersici Septoria leaf spot, Corynespora cassiicola target spot, and Alternaria solani early blight, as well as healthy leaves, resulting in a total of seven classes, were utilized for the classification. Deep learning models, such as convolutional neural networks (CNNs), GoogleNet, ResNet-50, AlexNet, Inception v3, and MobileNet, were utilized, achieving classification accuracies of 65.8%, 84.9%, 93.4%, 89.4%, 93.4%, and 96%, respectively. Furthermore, applying the group learning approaches significantly improved the results, with simple majority voting achieving a classification accuracy of 99.5% and weighted majority voting achieving 100%. These findings highlight the effectiveness of the proposed deep ensemble learning models in accurately identifying and classifying tomato diseases, featuring their potential for practical applications in tomato disease diagnosis and management. Full article

Research on hurricane impacts in Florida’s coastal regions has been extensive, yet there remains a gap in comparing the effects and potential damage of different hurricanes within the same geographical area. Additionally, there is a need for reliable discussions on how variations in storm surges during these events influence evacuation accessibility to hurricane shelters. This is especially significant for rural areas with a vast number of aging populations, whose evacuation may require extra attention due to their special needs (i.e., access and functional needs). Therefore, this study aims to address this gap by conducting a comparative assessment of storm surge impacts on the evacuation accessibility of southwest Florida communities (e.g., Lee and Collier Counties) affected by two significant hurricanes: Irma in 2017 and Ian in 2022. Utilizing the floating catchment area method and examining Replica’s OD Matrix data with Geographical Information Systems (GISs)-based technical tools, this research seeks to provide insights into the effectiveness of evacuation plans and identify areas that need enhancements for special needs sheltering. By highlighting the differential impacts of storm surges on evacuation accessibility between these two hurricanes, this assessment contributes to refining disaster risk reduction strategies and has the potential to inform decision-making processes for mitigating the impacts of future coastal hazards. Full article

The market demand for baby leaf lettuce is constantly increasing, while safety has become one of the most important traits in determining consumer preference driven by human health hazards concerns. In this study, the performance of visible and near-infrared (vis/NIR) spectroscopy was tested in discriminating pesticide-free against pesticide-treated lettuce plants. Two commercial fungicides (mancozeb and fosetyl-al) and two insecticides (deltamethrin and imidacloprid) were applied as spray solutions at the recommended rates on baby leaf lettuce plants. Untreated-control plants were sprayed with water. Reflectance data in the wavelength range 400–2500 nm were captured on leaf samples until harvest on the 10th day upon pesticide application, as well as after 4 and 8 days during post-harvest storage at 5 °C. In addition, biochemical components in leaf tissue were also determined during storage, such as antioxidant enzymes’ activities (peroxidase [POD], catalase [CAT], and ascorbate peroxidase [APX]), along with malondialdehyde [MDA] and hydrogen peroxide [H₂O₂] content. Partial least square discriminant analysis (PLSDA) combined with feature-selection techniques was implemented, in order to classify baby lettuce tissue into pesticide-free or pesticide-treated ones. The genetic algorithm (GA) and the variable importance in projection (VIP) scores identified eleven distinct regions and nine specific wavelengths that exhibited the most significant effect in the detection models, with most of them in the near-infrared region of the electromagnetic spectrum. According to the results, the classification accuracy of discriminating pesticide-treated against non-treated lettuce leaves ranged from 94% to 99% in both pre-harvest and post-harvest periods. Although there were no significant differences in enzyme activities or H₂O₂, the MDA content in pesticide-treated tissue was greater than in untreated ones, implying that the chemical spray application probably induced a stress response in the plant that was disclosed with the reflected energy. In conclusion, vis/NIR spectroscopy appears as a promising, reliable, rapid, and non-destructive tool in distinguishing pesticide-free from pesticide-treated lettuce products. Full article

The black skimmer (Rynchops niger) is a state-threatened, colonially nesting seabird in Florida, USA. Conservation threats include habitat alteration, human disturbances, severe weather, and predation. During nest monitoring (May–September, 2020–2022), black skimmer juveniles at colonies on Fort Myers Beach and Marco Island, Florida, had polyarthritis and died or were euthanized due to severe illness. Similarly-aged skimmers from geographically distant (considered unaffected) colonies were evaluated for comparison (2021–2023). We documented field, clinical, radiographical, and pathological findings to characterize disease and purported pathogenesis. The majority were lame and lethargic, in poor nutritional condition, and dehydrated. Additionally, 8/23 of the skimmers with dermatitis and arthritis from affected colonies also had penetrating sandspurs associated with skin ulceration, scabbing, and/or hemorrhage. The affected joints were often in limbs (interphalangeal and hock; less commonly stifle, elbow, carpus). A postmortem evaluation and bacteriology revealed Staphylococcal aureus-associated dermatitis, arthritis, tenosynovitis, and/or osteomyelitis in 21/22 of the juvenile skimmers from southwestern nest colonies. Staphylococcus aureus dissemination to internal organs occurred in 10/13 of the skimmers tested. Among skimmers evaluated from distant colonies, 5/10 that were examined histologically had skin crusting and inflammation but lacked arthritis. Occasional coinfections were documented (e.g., West Nile virus, Gram-negative bacilli). The results suggest that staphylococcal joint disease originated from sandspur-induced skin damage, followed by hematogenous dissemination to the joints and, occasionally, the internal organs. Additional nest sites should be tested to evaluate disease risk and potentially contributing environmental factors. We recommend that site managers employ techniques that reduce the risk of skimmer interactions with sandspurs. Full article

Tomato fungal diseases can cause significant economic losses to farmers. Advanced disease detection methods based on symptom recognition in images face challenges when identifying fungal diseases in tomatoes, especially with limited training images. This study utilized novel techniques designed for limited data scenarios, such as one-shot and few-shot learning, to identify three tomato fungal diseases, i.e., Alternaria solani, Alternaria alternata, and Botrytis cinerea. Automated feature extraction was performed using the ResNet-12 deep model, and a cosine similarity approach was employed during shot learning. The accuracy of diagnosing the three diseases and healthy leaves using the 4-way 1-shot learning method was 91.64, 92.37, 92.93, and 100%. For the 4-way 3-shot learning method, the accuracy improved to 92.75, 95.07, 96.63, and 100%, respectively. These results demonstrate that the proposed method effectively reduces the dependence on experts labeling images, working well with small datasets and enhancing plant disease identification. Full article