Leaf Stem Research Articles

Rapid detection of the phytopathogenic oomycete Phytopythium helicoides with a visualized loop-mediated isothermal amplification assay.

The phytopathogenic oomycete Phytopythium helicoides, previously known as Pythium helicoides, has emerged as a new threat to the Shatangju citrus cultivar (Citrus reticulata cv. Shatangju; '' in Chinese) in southern China. To enable rapid diagnosis and control of the leaf blight and stem rot caused by P. helicoides, we developed a visualized loop-mediated isothermal amplification (LAMP) assay targeting the β-tubulin gene of the pathogen. Using a carefully selected set of primers and optimized reaction conditions at 60°C for 60min, the LAMP assay can specifically detect the pathogen among various citrus-associated phytopathogens. The assay demonstrated high sensitivity, detecting as little as 30pg of the pathogen DNA, comparable to the sensitivity of quantitative polymerase chain reaction (qPCR). We validated the practical application of this LAMP assay for diagnosing infections in citrus leaves and strawberry crowns. This newly developed LAMP assay offers a specific, sensitive, and rapid detection tool for P. helicoides, aiding in the control of Shatangju citrus leaf blight and stem rot, as well as other related crop diseases like strawberry crown rot.

Mineral nutrition for Cannabis sativa in the vegetative stage using response surface analysis.

Cannabis cultivated for medical and adult use is a high-value horticultural crop in North America; however, we lack information on its optimal mineral nutrition due to previous legal restrictions. This study evaluated the mineral requirements of nitrogen (N), phosphorus (P), and potassium (K) for cannabis in the vegetative stage using response surface analysis. Plants were cultivated in a hydroponic system with various nutrient solution treatments (mg L-1) of N (132.7, 160, 200, 240, and 267.3), P (9.6, 30, 60, 90, and 110.5), and K (20.8, 60, 117.5, 175, and 214.2) according to a central composite design. Nutrient interactions (N × K, K × P, and N × P × K) had a significant effect on the vegetative growth of the cannabis plants. N × K interaction had a significant effect on leaf mass and stem mass. K × P interaction had a significant effect on dry root mass, leaf mass, stem mass, leaf area, specific leaf area, and chlorophyll a and b contents. N × P × K interaction had a significant effect on root mass, leaf mass, stem mass, stem diameter, leaf area, and chlorophyll a and b contents. The optimum concentrations of total nitrogen, P, K, calcium, and sulfur in the cannabis leaves were 0.54, 0.073, 0.27, 0.56, and 0.38 mg g-1, respectively. An increase in P and K concentrations decreased the magnesium concentration in the leaves, but it was unaffected by the increase in N concentration. The recommended primary macronutrients for cannabis plants in the vegetative stage based on the maximum desirability and nutrient use efficiencies were 160-200 mg L-1 N, 30 mg L-1 P, and 60 mg L-1 K. These findings can offer valuable insight and guidance to growers regarding the mineral requirements for cannabis during the vegetative stage.

Leaf Orientation Method as a Proxy for Sensing Nitrogen and Water Deficit in Tomato Plants

Precision field management and modeling crop growth requires precise, efficient, timely, nondestructive, and cost-effective measurements of structural component including leaf area index (LAI) and leaf orientation based on the foliage mean tilt angle (MTA), which is defined as the average leaf inclination angle of a canopy. Two experiments were carried out to investigate 1) the influence of different nitrogen (N) fertigation levels and 2) cycles of water deficit on leaf orientation (specifically, MTA) of young tomato (Solanum lycopersicum cv. Florida 47). N rates were 10, 50, 100, 200, and 300 mg·L−1 (mg N per liter of water) and irrigation treatments were well-watered and deficit irrigation. MTA was measured using the LAI-2200C Plant Canopy Analyzer. Across the experimental period, low N supply to leaves reduced the angle between leaf petiole and stem (high MTA) and produced plants with more erect leaves compared with N-sufficient plants. The MTA for the 10 mg·L−1–N treatment ranged from 54.3° to 73.6° and higher levels of N (50 to 300 mg·L−1–N) had MTA between 36.4° and 51.9°. Compared with lower N level (10 mg·L−1), LAI of tomato plants fertigated with a nutrient solution containing 300 mg·L−1–N increased by 3.6- to 4.9-fold across the study period. Similarly, canopy normalized difference vegetation index (NDVI) and leaf N% values at 300 mg·L−1–N were 2- to 3-fold higher than at 10 mg·L−1–N. However, MTA did not consistently differ across the 100 to 300 mg·L−1–N rates. In terms of water treatments, MTA of well-watered plants ranged from 32.3° to 40.5° whereas the same plants at water-deficit level had MTA between 45.7° and 51.8°. Although the relationship between MTA and canopy LAI, NDVI as well as leaf N was inconsistent or not significant under well-watered and water-deficit conditions, leaf gas exchange components (photosynthesis, stomatal conductance, and transpiration) showed a significant negative relationship (R2, 0.39–0.81, P < 0.001) with MTA. Overall, leaf orientation, specifically MTA, was modulated by N levels (highest at 10 mg·L−1; lowest at 300 mg·L−1–N) and cycles of water deficit and therefore, MTA determination hold promise as a nondestructive method to assess N and water deficit in young tomato plants.

U + LSTM-F: A data-driven growth process model of rice seedlings

Accurately predicting the growth status of rice seedlings and understanding their growth rate and health status in a timely manner helps adjust the growth cycle and management measures. By predicting the growth status of the seedlings, the best time for transplanting can be selected, improving the survival rate and overall health of the seedlings, thereby enhancing yield and quality. Therefore, this study proposes a data-driven time-series model, the U + LSTM-F model, for predicting the growth status of Wuyou Rice 4 seedlings. First, the U-Net model is employed to segment sequentially collected images, extracting features such as leaf age and stem length of the rice seedlings. Subsequently, the collected ambient temperature and humidity data are aligned with the leaf age and stem length data. Finally, the LSTM model is used for time-series analysis, enabling the model to learn the temporal relationship between environmental and growth data and predict the growth trend of the rice seedlings. Additionally, an attention mechanism is introduced to enhance model performance, and the model's effectiveness is evaluated using multiple quantitative metrics. The proposed model achieves an RMSE of 0.032 and MAPE of 0.895 % for leaf age prediction, and an RMSE of 0.067 and MAPE of 0.814 % for stem length prediction. The experimental results show that this data-driven approach, which combines growth data with environmental data, exhibits high accuracy in predicting the leaf age and stem length of rice seedlings. This provides a more accurate tool for predicting the growth of rice seedlings, offering valuable insights for rice seedling cultivation research.

Mechanism of Irrigation Before Low-Temperature Exposure on Mitigating the Reduction in Yield Loss and Spikelet Abortion at the Jointing Stage of Wheat

The increasing frequency of low-temperature events in spring, driven by climate change, poses a serious threat to wheat production in Northern China. Understanding how low-temperature stress affects wheat yield and its components under varying moisture conditions, and exploring the role of irrigation before exposure to low temperatures, is crucial for food security and mitigating agricultural losses. In this study, four wheat cultivars—semi-spring (YZ4110, LK198) and semi-winter (ZM366, FDC21)—were tested across two years under different conditions of soil moisture (irrigation before low-temperature exposure (IBLT) and non-irrigation (NI)) and low temperatures (−2 °C, −4 °C, −6 °C, −8 °C, and −10 °C). The IBLT treatment effectively reduced leaf wilt, stem breakage, and spikelet desiccation. Low-temperature stress adversely impacted the yield per plant—including both original and regenerated yields—and yield components across all wheat varieties. Furthermore, a negative correlation was found between regenerated and original yields. Semi-spring varieties showed greater yield reduction than semi-winter varieties, with a more pronounced impact under NI compared to IBLT. This suggests that the compensatory regenerative yield is more significant in semi-spring varieties and under NI conditions. As low-temperature stress intensified, the primary determinant of yield loss shifted from grain number per spike (GNPS) to spike number per plant (SNPP) beyond a specific temperature threshold. Under NI, this threshold was −6 °C, while it was −8 °C under IBLT. Low-temperature stress led to variability in fruiting rate across different spike positions, with semi-spring varieties and NI conditions showing the most substantial reductions. Sensitivity to low temperatures varied across spikelet positions: Apical spikelets were the most sensitive, followed by basal, while central spikelets showed the largest reduction in grain number as stress levels increased, significantly contributing to reduced overall grain yield. Irrigation, variety, and low temperature had variable impacts on physiological indices in wheat. Structural equation modeling (SEM) analysis revealed that irrigation significantly enhanced wheat’s response to cold tolerance indicators—such as superoxide dismutase (SOD), proline (Pro), and peroxidase (POD)—while reducing malondialdehyde (MDA) levels. Irrigation also improved photosynthesis (Pn), chlorophyll fluorescence (Fv/Fm), and leaf water content (LWC), thereby mitigating the adverse effects of low-temperature stress and supporting grain development in the central spike positions. In summary, IBLT effectively mitigates yield losses due to low-temperature freeze injuries, with distinct yield component contributions under varying stress conditions. Furthermore, this study clarifies the spatial distribution of grain responses across different spike positions under low temperatures, providing insights into the physiological mechanisms by which irrigation mitigates grain loss. These findings provide a theoretical and scientific basis for effective agricultural practices to counter spring freeze damage and predict wheat yield under low-temperature stress.

Nutritional composition of New Zealand native shrubs edible portions with fodder potential for ruminants

ABSTRACT In New Zealand, willow and poplar are used on hill farms for erosion control and to supplement pasture during low pasture periods. Some native shrubs are browsed by wild herbivores but remain unexplored for farmed ruminants. We evaluated the seasonal nutritional composition of five native New Zealand shrubs (Karamū, Pāpāuma, Karo, Whauwhaupaku, and Houhere) and compared them with willow (Kinuyanagi). Leaf and edible stem were analysed for nutritional composition and in vitro digestibility following AOAC procedures. Results showed that except Houhere, all other native shrubs leaves had lower crude protein (CP) (11 MJ/ kg DM) than Kinuyanagi. Kinuyanagi leaves were comparable to Houhere in ME and CP in spring but had lower CP in summer. Stem nutritional composition was similar across all shrubs. Acid and neutral detergent fibres in edible portions were comparable to those in common pastures, fodder crops, and forages in New Zealand. The findings suggest native shrubs could supplement low ME in summer pastures or winter herbage scarcity, but further research is needed on animal preference and intake.

Identifying genetic determinants of forage sorghum [Sorghum bicolor (Moench)] adaptation through GWAS

BackgroundForage sorghum is a highly valued crop in livestock feed production due to its versatility, adaptability, high productivity, and resilience under adverse environmental conditions, making it a crucial option for sustainable forage production. This study aimed to investigate ninety-five forage sorghum genotypes and identify the marker – trait associations (MTAs) in adaptive traits, including yield and flowering through genome-wide association studies (GWAS).ResultsUsing 41,854 polymorphic SNPs, a GWAS involving the GLM, MLM, and FarmCPU models was performed to analyse fourteen adaptive traits. The population structure revealed the presence of two subpopulation groups. Linkage disequilibrium (LD) plots showed varying degrees of LD decay across the chromosomes, with an average LD decay of 19.49 kbp. Twelve common significant QTNs, encoding 17 putative candidate genes, were simultaneously co-detected and studied by at least two or more GWAS methods. Three QTNs were associated to days to 50% flowering; two each to leaf-to-stem ratio and number of nodes per plant; and one each to plant height, leaf width, number of leaves per plant, stem girth, and internodal length. Six candidate genes were associated with days to 50% flowering, two each with leaf width, stem girth, leaf-to-stem ratio, and number of nodes per plant, and one each with plant height, number of leaves per plant, and internodal length.ConclusionFarmCPU was identified as the most suitable and effective among all the models for controlling both false positives and false negatives. Further in-depth analysis of the newly discovered QTNs may lead to the identification of new candidate genes for the trait of interest. These studies elucidate gene functions and could transform forage sorghum breeding through marker-assisted selection and transgenic approaches, accelerating the development of superior forage sorghum varieties and enhancing global food security.

Investigating the incidence and severity of field diseases impacting tuberose (Polianthes tuberosa L.) cultivation: A case study in the Jashore district, Bangladesh

A research investigation was conducted in the Jashore district of Bangladesh to examine diseases affecting tuberose plants. The laboratory analysis was performed at the plant disease study facility at Sher-e-Bangla Agricultural University, Dhaka, Bangladesh. The study encompassed eighteen villages across four unions within the Jhikargacha upazila of the Jashore district. The primary objective was to document the occurrence and severity of diseases in tuberose fields under natural conditions. Seven fungal diseases were detected in the natural field setting and were subjected to further scrutiny in the research facility. The pathogens responsible for these diseases were isolated and identified, revealing botrytis blight (Botrytis cinerea), blossom blight (Fusarium equiseti), alternaria leaf spot (Alternaria polyanthi), leaf tip blight (Phoma sp.), peduncle blight (Lasidiplodia theobromae), stem rot (Sclerotium rolfsii), and tuber rot (Fusarium oxysporum). Disease incidence ranged from 0 % to 64.33 %, and severity ranged from 0 % to 24.00 %. Stem rot, botrytis blight, alternaria leaf spot, and leaf tip blight were identified as significant challenges in tuberose cultivation in the Jashore district. This study represents the first documentation of tuberose pathogen prevalence in Bangladesh, providing valuable insights for disease identification and contributing to the improvement of tuberose cultivation practices in the region.

Wheat Rust Disease Detection Using Convolutional Neural Network

Wheat is a vital crop globally, playing a crucial role in ensuring food security. Any damage to wheat crops, particularly from diseases like wheat rust, can exacerbate the global food crisis. Wheat rust, along with other crop diseases, poses a serious threat to agricultural sustainability and food supply. Technological approaches to detect crop health can significantly enhance disease prevention compared to traditional manual methods. This study focuses on detecting wheat rust diseases, specifically wheat leaf rust, stem rust, and yellow rust, all of which vary in their impact on crops. By employing image analysis of wheat plants, the research aims to accurately distinguish between healthy crops and those affected by rust diseases. The experiments consider multiple variables, such as learning rate, dropout, and train-test split ratio, reflecting a comprehensive research approach. The model achieved an impressive 99.64% accuracy in detecting wheat rust, highlighting its potential for early disease detection and prevention.

Mathematical modelling of vacuum cooling of leafy vegetables using bidirectional conjugate model combining heat and mass transfer with leaf structural deformation

The numerical model is an effective solution to regulate the uneven temperature distribution, water loss, and structural shrinkage of leafy vegetables during vacuum cooling (VC). Nevertheless, the developed models mainly concentrate on the heat and mass transfer of leafy vegetables during the cooling, ignoring the structural deformation and its interaction with heat and mass transfer. For a better understanding of the cooling process, a macroscopic bidirectional coupled thermo-hydro-mechanical (THM) model was developed to describe the VC of leafy vegetables using spinach as a representative case. The simulations, validated by the experimental results, accurately predicted the changes in vapour pressure, weight loss, evaporation rate, moisture concentration, and shrinkage of the leafy vegetable, as well as the cooling gradients of the stem, blade, midvein, and lateral veins. Results showed that the cooling of the blade was easier, but the cooling gradient of the blade was lower than that of the leaf stem, with a maximum temperature gradient of stem, veins, and blade after cooling being 2.09 °C. Reducing the cooling end pressure could shorten the cooling time to the desired cooling temperature and minimise the water loss. Sensitivity analysis revealed that porosity significantly affected the changes in temperature, moisture concentration, and shrinkage. Notably, water evaporation was the primary reason for the shrinkage of the leaf tip and edge, but the contraction of the solid matrix cannot significantly aggravate further water evaporation. These findings highlight the potential of the THM model to promote the commercialisation of VC for leaf vegetables.

Characterization of Neopestalotiopsis species associated with lingonberry (Vaccinium vitis-idaea L.) diseases in Canada

Lingonberry (Vaccinium vitis-idaea L.) is a dwarf shrub that is native to Northern climates. The berries are harvested from wild plants and the disease incidence in the crop is generally low. However, disease pressure may increase since the surface area dedicated to the crop is expected to expand in the future. In 2021 and 2022, brown necrotic leaves and stem dieback symptoms were observed on lingonberry plants grown at the St. John’s Research and Development Center, Newfoundland and Labrador, Canada. Pathogens were isolated from the diseased plant tissues and identified as Neopestalotiopsis rosae (n = 2) and Neopestalotiopsis zimbabwana (n = 2) using colony and conidia morphology as well as sequencing of the Internal Transcribed Spacer (ITS), beta-tubulin gene (ß-tub), and the translation elongation factor 1-alpha gene (tef1). Pathogenicity assays in detached lingonberry and strawberry leaves as well as lingonberry plants indicated that all four isolates were able to cause disease. This is the first report of N. rosae and N. zimbabwana causing leaf spot and stem dieback of lingonberry. Further monitoring of this disease is important to understand its prevalence in berry crops, as Neopestalotiopsis spp. in Canada have been reported to occur since 2018 in strawberry and other small fruits. Control measures and strategies are required to mitigate the potential risk from the disease, as there are no registered fungicides available to the growers.

Analysis of the Role of Garbage Bank in Improving the Community Economy in Medan Belawan District

Waste is a serious problem in Indonesia, including in Medan Belawan Regency. The Waste Bank Program is presented as a solution for waste management and community economic empowerment. This study aims to determine the role of the Waste Bank in community income. The research method used is descriptive statistics presented in the form of frequency distribution tables, histograms, steam and leaf (stem and leaf diagrams) or box plots (box line diagrams). Data collection was carried out through interviews, questionnaires and observations. The data analysis technique used is statistical analysis. The Waste Bank Program in Medan Belawan Regency has provided economic, social and environmental benefits. Economic benefits include increasing community income, creating jobs, and saving waste transportation costs. Social benefits include increasing public awareness of the importance of good waste management, and increasing public concern for the environment. Environmental benefits include reducing rubber production, reducing environmental pollution, and preserving natural resources. The Waste Bank Program in Medan Belawan Regency is one example of the application of an effective and sustainable green economy concept. This program can be a model for other waste management programs in Indonesia.

Identification of Rust Resistance Genes in Wheat (Triticum aestivum L.) Using Molecular Markers and Host–Pathogen Interaction Tests

ABSTRACTThe leaf rust (Puccinia triticina f. sp. tritici), stripe rust (Puccinia striiformis f. sp. tritici), and stem rust (Puccinia graminis f. sp. tritici) are major fungal constraints affecting wheat production worldwide. Identifying and deploying wheat varieties with diverse resistance are the best ways to manage all the rusts. Therefore, a continuous search goes on to identify diverse germplasm with effective rust resistance that expresses at different stages of plant growth (seedling and adult plant). A set of 22 rust resistant wheat genotypes and 4 checks (controls), viz., Avocet‐Yr10, Avocet ‐Yr15, Agra Local, and respective positive checks were studied for characterising rust resistance genes using host–pathogen interactions complemented by molecular markers. Among 22 elite genotypes, 05 genotypes amplified 191 bp fragment with marker PSY1E1, confirmed the presence of gene Lr19/Sr25. These genotypes also expressed resistance to most virulent leaf rust pathotypes, 77‐5 and 77‐9 in host–pathogen interaction test (HPI). Seven genotypes showed the presence of Lr34/Yr18/Sr57/Pm38/Ltn1 in homozygous state, whereas G4 showed its presence in heterozygous condition. Among 22 genotypes, 16 genotypes possessed Yr10. Five genotypes (22.7%) exhibited two gene combinations, Lr19/Sr25, and Yr10 as revealed through the detection of 191 bp fragment with marker PSY1E1 and 260 bp fragment with co‐dominantly inherited microsatellite marker Xpsp3000, respectively. All five genotypes (G2, G3, G8, G9, and G18) also expressed brown glumes controlled by the gene Rg1 tightly linked to Yr10 on the 1BS chromosome. Broad spectrum rust resistance present in these lines in good agronomic backgrounds could be used as potent genetic donors for diverse and durable rust resistance breeding programmes in wheat.

Tithonia diversifolia (Asteraceae) as a reservoir of natural enemies to promote pest control and improve development of tomato (Solanum lycopersicum) in open conditions

ABSTRACT Tithonia diversifolia has the potential for use as a reservoir for entomophages in pest control, as it attracts and preserves natural enemies before migrating to the target crop, where a high incidence of pests occurs. For example, in tomato crop production one of the limiting factors, under field condition, is the incidence of phytophagous insects. We determined the effects of Tithonia diversifolia on species assemblage structure of natural enemies and pest insects (abundance and diversity), plant growth (leaf area, stem length and diameter, number of leaves, flowers and fruits, and fruits weight/per plant), plant defense (leaf thickness and hardness) and fruit colouration on tomato plants in open conditions. Through direct and indirect samplings, a total of 456 insects were collected, comprising 76 natural enemies, 269 pests and 111 belonging other guilds. Length, stem diameter, number of flowers, leaf thickness, and hardness increased, while the number of leaves decreased. T. diversifolia plants increased natural enemies but not pest abundance in tomato plants, demonstrating that this plant can alter the structure of the insect community associated with tomato.

Effect of CO2 Elevation on Tomato Gas Exchange, Root Morphology and Water Use Efficiency under Two N-Fertigation Levels.

Atmospheric elevated CO2 concentration (e[CO2]) decreases plant nitrogen (N) concentration while increasing water use efficiency (WUE), fertigation increases crop nutrition and WUE in crop; yet the interactive effects of e[CO2] coupled with two N-fertigation levels during deficit irrigation on plant gas exchange, root morphology and WUE remain largely elusive. The objective of this study was to explore the physiological and growth responses of ambient [CO2] (a[CO2], 400 ppm) and e[CO2] (800 ppm) tomato plant exposed to two N-fertigation regimes: (1) full irrigation during N-fertigation (FIN); (2) deficit irrigation during N-fertigation (DIN) under two N fertilizer levels (reduced N (N1, 0.5 g pot-1) and adequate N (N2, 1.0 g pot-1). The results indicated that e[CO2] associated with DIN regime induced the lower N2 plant water use (7.28 L plant-1), maintained leaf water potential (-5.07 MPa) and hydraulic conductivity (0.49 mol m-2 s-1 MPa-1), greater tomato growth in terms of leaf area (7152.75 cm2), specific leaf area (223.61 cm2 g-1), stem and total dry matter (19.54 g and 55.48 g). Specific root length and specific root surface area were increased under N1 fertilization, and root tissue density was promoted in both e[CO2] and DIN environments. Moreover, a smaller and denser leaf stomata (4.96 µm2 and 5.37 mm-2) of N1 plant was obtained at e[CO2] integrated with DIN strategy. Meanwhile, this combination would simultaneously reduce stomatal conductance (0.13 mol m-2 s-1) and transpiration rate (1.91 mmol m-2 s-1), enhance leaf ABA concentration (133.05 ng g-1 FW), contributing to an improvement in WUE from stomatal to whole-plant scale under each N level, especially for applying N1 fertilization (125.95 µmol mol-1, 8.41 µmol mmol-1 and 7.15 g L-1). These findings provide valuable information to optimize water and nitrogen fertilizer management and improve plant water use efficiency, responding to the potential resource-limited and CO2-enriched scenario.

Morphological Traits and Biomass Allocation of Leymus secalinus along Habitat Gradient in a Floodplain Wetland of the Heihe River, China

Plant organ biomass allocation and morphological characteristics are important functional traits. The responses of plant root, stem, and leaf traits to heterogeneous habitats in floodplain wetlands are highly important for understanding the ecological adaptation strategies of riparian plants. However, the patterns of these responses remain unclear. In a floodplain wetland in the middle reaches of the Heihe River, we studied the responses of the root, stem, and leaf morphological traits and biomass allocation of Leymus secalinus to varying habitat conditions. We measured these traits in three sample plots, delineated based on distance from the riverbank: plot I (near the riparian zone, 50–150 m from the riverbank), plot II (middle riparian zone, 200–300 m from the riverbank), and plot III (far riparian zone, 350–450 m from the riverbank). The results showed that in plot I, L. secalinus tended to have slender roots and stems and small leaves, with a biomass allocation strategy that maximized the root–shoot ratio (RSR). In plot II, L. secalinus had thick stems and moderate leaf and root patterns, and the RSR values were between those of plot I and plot III. In plot III, L. secalinus had thin and short stems and large leaves; furthermore, among the root morphological structures, plot III had the shortest Rhizome length (RL) and longest Rhizome diameter (RD), and the RSR was the lowest. Moreover, there was a significant correlation between organ biomass and leaf thickness, stem length, RD, and RL in the three habitats (p < 0.05). By balancing the biomass allocation among organs, wetland plants in floodplains balance changes in root, stem, and leaf morphological characteristics to improve their environmental adaptation.

Cucumber Disease Image Classification with A Model Combining LBP and VGG-16 Features

Cucumber (Cucumis sativus) is a significant horticultural crop worldwide, highly valued for both fresh consumption and processing. However, cucumber cultivation faces challenges due to diseases that can substantially reduce yield and quality. Diseases like leaf spots, stem wilt, and fruit rot are caused by pathogens including viruses, bacteria, and fungi. Traditionally, disease detection in cucumbers is performed manually, which is time-consuming and inefficient. Therefore, developing machine vision-based models using Deep Learning (DL) and Machine Learning (ML) for early disease detection through image analysis is crucial for assisting farmers. While many studies on plant disease classification using various DL and ML models show optimal results, research on cucumbers has mostly focused on leaf diseases. This study aims to optimize cucumber disease image classification by developing a model that combines Local Binary Pattern (LBP) texture features and VGG-16 convolutional features. The dataset used, Cucumber Disease Recognition Dataset consists of 8 classes of cucumber plant disease images covering leaves, stems, and fruits. This study classifies cucumber plant disease images using Random Forest (RF) combined with LBP texture features and VGG-16 visual features and compares its performance with models using VGG-16, LBP+RF, and VGG-16+RF on the same dataset. The results show that the proposed model achieved a precision of 84.7%, recall of 84%, F1-Score of 83.8%, and accuracy of 84%. These results outperform the comparative models, demonstrating the effectiveness of the combined approach in classifying cucumber plant diseases.

Comparative analysis of water-use strategies in three subtropical mangrove species: a study of sap flow and gas exchange monitoring.

Water-use strategies play a crucial role in the adaptive capabilities of mangroves to the saline intertidal conditions, yet the intricacies of daily water-use patterns in mangrove species, which are pivotal for maintaining water balance, remain poorly understood. In this comprehensive study, we aimed to clarify the water use strategies of three co-occurring mangrove species, Avicennia marina, Aegiceras corniculatum and Kandelia obovata, through stem sap flow monitoring, leaf gas exchange and stem diameter change measurements. Our findings revealed that the daily sap flow density of Avicennia and Aegiceras reached the peak about 1 h earlier than that of Kandelia. When transpiration was strong, Kandelia and Aegiceras used stem storage to meet water demand, while Avicennia synchronized stem water storage. These three mangrove species adopted cross-peak water used and unique stem water storage to regulate their water balance. In Kandelia, the daily sap flow in per sapwood area was significantly lower, while water-use efficiency was significantly higher than those of Avicennia and Aegiceras, indicating that Kandelia adopted a more conservative and efficient water-use strategy. Sap flow in Avicennia was the most sensitive to environmental changes, while Kandelia limited water dissipation by tightly controlling stomata. Meteorological factors (photosynthetically active radiation, vapor pressure deficit and air temperature) were the main driving factors of sap flow. The increase of soil temperature can promote the water use of mangrove species, while the increase of salinity resulted in more conservative water use. Our results highlight the diversity of daily water-use strategies among the three co-occurring mangrove species, pinpointing Kandelia as the most adaptive at navigating the changing conditions of intertidal habitats in the future climate. In conclusion, our findings provide a mesoscale perspective on water-use characteristics of mangroves and also provides theoretical basis for mangroves afforestation and ecological restoration.

Estimation of Maize Biomass at Multi-Growing Stage Using Stem and Leaf Separation Strategies with 3D Radiative Transfer Model and CNN Transfer Learning

The precise estimation of above-ground biomass (AGB) is imperative for the advancement of breeding programs. Optical variables, such as vegetation indices (VI), have been extensively employed in monitoring AGB. However, the limited robustness of inversion models remains a significant impediment to the widespread application of UAV-based multispectral remote sensing in AGB inversion. In this study, a novel stem–leaf separation strategy for AGB estimation is delineated. Convolutional neural network (CNN) and transfer learning (TL) methodologies are integrated to estimate leaf biomass (LGB) across multiple growth stages, followed by the development of an allometric growth model for estimating stem biomass (SGB). To enhance the precision of LGB inversion, the large-scale remote sensing data and image simulation framework over heterogeneous scenes (LESS) model, which is a three-dimensional (3D) radiative transfer model (RTM), was utilized to simulate a more extensive canopy spectral dataset, characterized by a broad distribution of canopy spectra. The CNN model was pre-trained in order to gain prior knowledge, and this knowledge was transferred to a re-trained model with a subset of field-observed samples. Finally, the allometric growth model was utilized to estimate SGB across various growth stages. To further validate the generalizability, transferability, and predictive capability of the proposed method, field samples from 2022 and 2023 were employed as target tasks. The results demonstrated that the 3D RTM + CNN + TL method outperformed best in LGB estimation, achieving an R² of 0.73 and an RMSE of 72.5 g/m² for the 2022 dataset, and an R² of 0.84 and an RMSE of 56.4 g/m² for the 2023 dataset. In contrast, the PROSAIL method yielded an R² of 0.45 and an RMSE of 134.55 g/m² for the 2022 dataset, and an R² of 0.74 and an RMSE of 61.84 g/m² for the 2023 dataset. The accuracy of LGB inversion was poor when using only field-measured samples to train a CNN model without simulated data, with R² values of 0.30 and 0.74. Overall, learning prior knowledge from the simulated dataset and transferring it to a new model significantly enhanced LGB estimation accuracy and model generalization. Additionally, the allometric growth model’s estimation of SGB resulted in an accuracy of 0.87 and 120.87 g/m² for the 2022 dataset, and 0.74 and 86.87 g/m² for the 2023 dataset, exhibiting satisfactory results. Separate estimation of both LGB and SGB based on stem and leaf separation strategies yielded promising results. This method can be extended to the monitor and inversion of other critical variables.

Neocamarosporium betae causing leaf spot and stem necrosis disease on Chenopodium quinoa in Shanxi Province, China

Quinoa (Chenopodium quinoa) is a pseudocereal crop, and has been widely cultivated in China with excellent sources of nutrients, amino acids, and vitamins. During the inflorescence emergence stage to the grain-filling stage, Neocamarosporium betae leaf spot and stem necrosis of quinoa (NLSQ) as a new emerging disease was observed in Shanxi Province of China. NLSQ usually infected the leaves and then gradually spread towards to the stems. Typical symptoms included tan to brown lesions and with many black pycnidia, leading to lodging, leaf abscission, and grain unfilling. Based on morphological characteristics, phylogenetic analyses, and pathogenicity tests, the pathogen was identified as N. betae. The morphological characterizations of N. betae from quinoa were pycnidial conidiomata, hyaline conidiogenous cell, and smooth hyaline aseptate conidia. At 2–6 days postinoculation, the typical symptoms induced by inoculations of N. betae on leaves and stems of quinoa. To our knowledge, this study firstly reported that N. betae was the pathogen of NLSQ in China. The findings of this current study will contribute to developing the disease diagnosis and management.