Search Results (43)

Morkhor 60-3 is an upland rice variety primarily cultivated in northeastern Thailand. This glutinous rice is valued for its adaptability and rich aroma but remains susceptible to significant diseases, particularly blast and bacterial blight. Using resistant varieties represents the most cost-effective approach to address this limitation. This study incorporated the QTLs/genetic markers qBl1, qBl2, and xa5 from Morkhor 60-1 through marker-assisted backcrossing. From the BC₁F₃ population, ten lines were selected based on their parentage and evaluated for blast resistance using a spray inoculation method with 12 isolates of Pyricularia oryzae, and for bacterial blight (BB) resistance using a leaf-clipping method with nine isolates of Xanthomonas oryzae pv. oryzae. Broad-spectrum resistance (BSR) was also assessed in the lines for both diseases. Subsequently, BC₁F₄ lines were evaluated for field performance, including agronomic traits and aroma. Results identified three superior lines, BC₁F₄ 22-7-140-4, BC₁F₄ 22-7-322-5, and BC₁F₄ 22-7-311-9, that demonstrated resistance to both BB and blast pathogens with average BSR values of 0.61 and 1.00, 0.66 and 1.00, and 0.55 and 0.87, respectively. These lines also exhibited enhanced performance in flowering date, plant height, panicle number per plant, grain number per plant, and grain weight. These findings demonstrate the effectiveness of marker-assisted selection (MAS) for gene pyramiding in rice improvement. Full article

Leaf biochemical traits are crucial for understanding plant physiological status and ecological dynamics. Partial least squares regression (PLSR) models have been widely used to estimate leaf biochemical traits from spectral reflectance information. However, variations in sun–sensor geometry, the sensor field of view, and the random orientation of leaves can introduce multi-angular reflection properties that differ between leaf sides. In this context, the transferability of PLSR models across different leaf sides and viewing zenith angles (VZAs) remains unclear. This study investigated the potential of multi-angular spectral reflection from dorsiventral leaves to improve the transferability of PLSR models for estimating the leaf chlorophyll content (LCC) and equivalent water thickness (EWT). We compared models trained using multi-angular data from both leaf sides with models trained using nadir data (from the adaxial side, abaxial side, or their combination). The results show that the PLSR models trained with multi-angular data from both leaf sides outperformed the models trained with nadir data, achieving the highest accuracy in estimating biochemical traits (LCC: R² = 0.87, RMSE = 7.17 μg/cm², NRMSE = 10.71%; EWT: R² = 0.86, RMSE = 0.0015 g/cm², NRMSE = 10.00%). In contrast, the PLSR models trained using single-angle reflection from either the adaxial or abaxial side showed a lower estimation accuracy and greater variability across leaf sides and VZAs. The superior performance across datasets obtained under different measurement conditions (e.g., integrating spheres and leaf clips) further confirmed the improved generalizability of the PLSR model trained with multi-angular data from dorsiventral leaves. These findings highlight the potential of the multi-angular spectral reflection of dorsiventral leaves to enhance the estimation of biochemical traits across various leaf sides, viewing angles, and measurement conditions. They also underscore the importance of incorporating spectral diversity into model training for improved transferability. Full article

Bird cherry-oat aphid (Rhopalosiphum padi L.; Hemiptera: Aphididae) and English grain aphid (Sitobion avenae Fabricius; Hemiptera: Aphididae) are economically important cereal crop pests and effective vectors of barley yellow dwarf virus (BYDV). While these aphid species have traditionally been managed with synthetic chemical insecticides, their use is increasingly difficult due to target organism resistance and potential non-target effects. Exploiting genetic diversity among cereal varieties offers a more sustainable control strategy. In this study, we evaluated how an experimental confinement method using clip cages to restrict an aphid to a single leaf versus free movement on the host plant affects the performance (growth and reproduction) of these two aphid species on various wheat varieties. Aphid performance was significantly influenced by both confinement and wheat variety. Notably, the two aphid species responded in opposite ways to confinement, with S. avenae growing quicker and producing a greater number of offspring under clip cage confinement compared to R. padi, which performed better when left free on the plant. This contrast is likely explained by species-specific feeding site preferences and sensitivity to the microenvironment created by the clip cages. We also found significant differences in aphid performance among host plant varieties, with both aphid species achieving their lowest growth rates on “Wolverine”, a modern BYDV-resistant wheat cultivar. Although none of the tested varieties were completely resistant to aphids, our results indicate that existing commercial cultivars may already carry partial resistance traits that can be leveraged in integrated pest management programs to help suppress aphid populations. Full article

Oat (Avena sativa L.) is one of the most important self-fertilizing field plants belonging to the Poaceae family. It has no significant requirements regarding growing conditions but has a very good reaction to fertilization. The current research evaluated the significance of the effects of individual applications of mineral (NPK) and organo-mineral (OMF) fertilizers, as well as their individual combination with slaked lime (calcium hydroxide, Ca(OH)₂), on the yield, morphological traits [mean number of leaves per plant—MNLP, minimum leaf length (cm) per plant—MinLL, maximum leaf length (cm) per plant—MaxLL, number of ears per plant—NEP], and physiological parameters (nitrogen balance index—NBI, content of chlorophyll—Chl, flavonoids—Flv, anthocyanins—Ant) of organic and pelleted (graded) oat plants, comparing the treatments and in relation to the control. The experiment was performed in semi-controlled glasshouse conditions, in pots, from the fourth week of March to the fourth week of June 2024, using Vertisol soil. This soil is characterized as light clay with an acid reaction. Physiological parameters were measured using a Dualex leaf clip sensor. The results obtained showed that physiological parameters in both oat types significantly differed (p < 0.05) between the treatments applied and in relation to the control, whereas the morphological traits did not significantly differ (p > 0.05) between the treatments. Statistically significant differences (p < 0.05) in the yield of both oat types were most pronounced in the OMF + Slaked Lime treatment (organic: 4.49 g pot⁻¹; pelleted: 4.61 g pot⁻¹) in relation to the control (organic: 2.48 g pot⁻¹; pelleted: 2.63 g pot⁻¹). The pelleted oats showed slightly better results for the effects of different treatments across all tested parameters compared to organic oats. In conclusion, the best results were obtained with the use of OMF + Slaked Lime, which could be proposed as the optimal fertilization treatment for pelleted and organic oat cultivation based on this research. Full article

Timely harvest is pivotal for the pasture management of tall wheatgrass, which has recently been suggested for coastal saline and alkaline soils. In this work, different culm parts in the top three internodes of tall wheatgrass during various heading stages were investigated to explore the precise harvesting time for the first cut, factors influencing forage quality, and correlations between the expression levels of genes involved in cellulose and lignin biosynthesis and forage nutritive value. The results show that the culms clipped at the half heading stage produced the highest crude protein (CP) yield. The top three leaves contributed the greatest proportion of total culm CP yield, accounting for 49%, 40%, and 30% of total culm CP yield at the just, half, and full heading stages, respectively. By contrast, the leaves and spikes produced lower yields of neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), crude cellulose (CC), and hemicellulose (HC) than leaf sheaths and stems, indicating that the leaf/stem ratio can be used as an index for the cultivation and genetic improvement of tall wheatgrass. The lignin and cellulose biosynthesis genes expressed differentially in different culm parts of tall wheatgrass in response to the heading stage. The expression levels of HCT, encoding a hydroxycinnamoyl CoA:shikimate hydroxycinnamoyl transferase, were negatively correlated with the CP content and relative feed value, but positively correlated with the yields of dry matter, NDF, ADF, CC, and HC, suggesting that it may be used as a marker gene linked to the forage quality of tall wheatgrass. Full article

Soil and plant analyzer development (SPAD) value and leaf nitrogen concentration (LNC) based on dry weight are important indicators affecting rice yield and quality. However, there are few reports on the use of machine learning algorithms based on hyperspectral monitoring to synchronously predict SPAD value and LNC of indica rice. Meixiangzhan No. 2, a high-quality indica rice, was grown at different nitrogen rates. A hyperspectral device with an integrated handheld leaf clip-on leaf spectrometer and an internal quartz-halogen light source was conducted to monitor the spectral reflectance of leaves at different growth stages. Linear regression (LR), random forest (RF), support vector regression (SVR), and gradient boosting regression tree (GBRT) were employed to construct models. Results indicated that the sensitive bands for SPAD value and LNC were displayed to be at 350–730 nm and 486–727 nm, respectively. Normalized difference spectral indices NDSI (R497, R654) and NDSI (R729, R730) had the strongest correlation with leaf SPAD value (R = 0.97) and LNC (R = −0.90). Models constructed via RF and GBRT were markedly superior to those built via LR and SVR. For prediction of leaf SPAD value and LNC, the model constructed with the RF algorithm based on whole growth periods of spectral reflectance performed the best, with R² values of 0.99 and 0.98 and NRMSE values of 2.99% and 4.61%. The R² values of 0.98 and 0.83 and the NRMSE values of 4.88% and 12.16% for the validation of leaf SPAD value and LNC were obtained, respectively. Results indicate that there are significant spectral differences associated with SPAD value and LNC. The model built with RF had the highest accuracy and stability. Findings can provide a scientific basis for non-destructive real-time monitoring of leaf color and precise fertilization management of indica rice. Full article

Rice spotted-leaf mutants serve as valuable resources for studying plant programmed cell death (PCD) and disease resistance mechanisms, making them crucial for research on disease resistance in rice. Map-based cloning was used to identify and clone the spotted-leaf gene OsSPL42. Then, functional complementation and CRISPR/Cas9 techniques were also employed to further validate the function of this gene. By applying leaf clippings for bacterial blight (BB) inoculation, the BB resistance of different rice lines was assessed. The results in this study were as follows: The OsSPL42 behaved as a recessive nuclear gene and was narrowed down to a 111 kb region on chromosome 8. All T₀ transgenic rice plants in the complementation experiments exhibited a wild-type phenotype, without any lesion spots on the rice leaves. This suggests that the LOC_Os08g06100 encoding O-methyltransferase is the candidate gene for the mutant spl42. The OsSpl42 is widely expressed and the OsSPL42-GFP protein is mainly localized in the cytoplasm. OsSPL42 overexpression lines are more susceptible to BBs, which indicates that OsSPL42 may act as a negative regulator of rice resistance to BB. In summary, we speculate that OsSPL42 plays an important role in the regulation of pathogen response, providing new insights into plant defense mechanisms. Full article

In recent years, deep-learning methods have significantly improved the classification results in the field of plant-leaf recognition. However, limited by the model input, the original image needs to be compressed to a certain size before it can be input into the convolutional neural network. This results in great changes in the shape and texture information of some samples, thus affecting the classification accuracy of the model to a certain extent. Therefore, a minimum enclosing quadrate (MEQ) method is proposed to standardize the sample datasets. First, the minimum enclosing rectangle (MER) of the leaf is obtained in the original image, and the target area is clipped. Then, the minimum enclosing quadrate of the leaf is obtained by extending the short side of the rectangle. Finally, the sample is compressed to fit the input requirements of the model. In addition, in order to further improve the classification accuracy of plant-leaf recognition, an EC-ResNet50 model based on transfer-learning strategy is proposed and further combined with the MEQ method. The Swedish leaf, Flavia leaf, and MEW2012 leaf datasets are used to test the performance of the proposed methods, respectively. The experimental results show that using the MEQ method to standardize datasets can significantly improve the classification accuracy of neural networks. The Grad-CAM visual analysis reveals that the convolutional neural network exhibits a higher degree of attention towards the leaf surface features and utilizes more comprehensive feature regions during recognition of the leaf samples processed by MEQ method. In addition, the proposed MEQ + EC-ResNet50 method also achieved the best classification results among all the compared methods. This experiment provides a widely applicable sample standardization method for leaf recognition research, which can avoid the problem of sample deformation caused by compression processing and reduce the interference of redundant information in the image to the classification results to a certain degree. Full article

In nature, plants have developed a series of resistance mechanisms to face various external stresses. As understanding of the molecular mechanisms underlying plant resistance continues to deepen, exploring endogenous resistance in plants has become a hot topic in this field. Despite the multitude of studies on plant-induced resistance, how plants respond to stress under natural conditions remains relatively unclear. To address this gap, we investigated Chinese pine (Pinus tabuliformis) using pine caterpillar (Dendrolimus tabulaeformis) under natural conditions. Healthy Chinese pine trees, approximately 10 years old, were selected for studying induced resistance in Huangtuliangzi Forestry, Pingquan City, Chengde City, Hebei Province, China. Pine needles were collected at 2 h and 8 h after feeding stimulation (FS) via 10 pine caterpillars and leaf clipping control (LCC), to simulate mechanical damage caused by insect chewing for the quantification of plant hormones and transcriptome and metabolome assays. The results show that the different modes of treatments significantly influence the contents of JA and SA in time following treatment. Three types of differentially accumulated metabolites (DAMs) were found to be involved in the initial response, namely phenolic acids, lipids, and flavonoids. Weighted gene co-expression network analysis indicated that 722 differentially expressed genes (DEGs) are positively related to feeding stimulation and the specific enriched pathways are plant hormone signal transduction and flavonoid biosynthesis, among others. Two TIFY transcription factors (PtTIFY54 and PtTIFY22) and a MYB transcription factor (PtMYB26) were found to be involved in the interaction between plant hormones, mainly in the context of JA signal transduction and flavonoid biosynthesis. The results of this study provide an insight into how JA activates, serving as a reference for understanding the molecular mechanisms of resistance formation in conifers responding to mandibulate insects. Full article

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees. Full article

The objective of content-based image retrieval (CBIR) is to locate samples from a database that are akin to a query, relying on the content embedded within the images. A contemporary strategy involves calculating the similarity between compact vectors by encoding both the query and the database images as global descriptors. In this work, we propose an image retrieval method by using hierarchical K-means clustering to efficiently organize the image descriptors within the database, which aims to optimize the subsequent retrieval process. Then, we compute the similarity between the descriptor set within the leaf nodes and the query descriptor to rank them accordingly. Three tree search algorithms are presented to enable a trade-off between search accuracy and speed that allows for substantial gains at the expense of a slightly reduced retrieval accuracy. Our proposed method demonstrates enhancement in image retrieval speed when applied to the CLIP-based model, UNICOM, designed for category-level retrieval, as well as the CNN-based R-GeM model, tailored for particular object retrieval by validating its effectiveness across various domains and backbones. We achieve an 18-times speed improvement while preserving over 99% accuracy when applied to the In-Shop dataset, the largest dataset in the experiments. Full article

Aeluropus lagopoides, a dominant palatable species in various sabkha and coastal regions of Saudi Arabia, can withstand harsh saline environments through phenotypic plasticity. When subjected to grazing, how A. lagopoides adapt phenotypically is currently unknown. There is a breakage in the chain of study on the spatial and temporal expansion strategy of A. lagopoides plants when subjected to different grazing stresses in different saline soil habitats. A grazing experiment was conducted to investigate the phenotypic plasticity and resource allocation pattern response of A. lagopoides in different saline soils. Individual A. lagopoides rhizomes from five saline regions were grown and exposed to varied grazing treatments in the form of clipping, viz; light, moderate, and heavy grazing, as compared to a grazing exclusion control. Our results showed that heavy grazing/clipping significantly decreased the shoot system and above-ground biomass in high-saline region plants in the early season. Plant length, root length, root and shoot biomass, the number of stolons, average stolon length, leaf area, and SLA of A. lagopiodes responded significantly to grazing intensities. A. lagopoides from the Qareenah, Qaseem, and Jizan regions were more tolerant to light grazing than A. lagopoides from the Salwa and Jouf regions. Light grazing showed significantly good re-growth, especially during the late season. Light grazing decreased the synthesis of chlorophyll content. Also, A. lagopiodes reduced the risk caused by reactive oxygen species via the increased accumulation of proline content. Overall, plants adapted to different morphological and physiological strategies to tolerate different levels of grazing intensities by adapting their morphological attributes. Though heavy grazing damages the plant, light and moderate grazing can be allowed to maintain the productivity and economic benefits of sabka habitats where soil conditions are moderately saline. Full article

While not classified as an essential plant nutrient, silicon (Si) assimilation following exogenous Si application has enhanced the wear resistance of cool-season turfgrass. Given this beneficial supplementation of lignin by Si reported in epidermal tissue of monocotyledonous plants, our research objective was to quantify root morphology, vegetative nutrition and vigor, soil chemistry, and putting green performance in response to split applications of pelletized liming agents rich in Si and/or Ca and Mg. Field evaluation of granular liming agent treatment, 2441 kg (ha year)⁻¹, was conducted on creeping bentgrass (Agrostis stolonifera L. cv. Penn G-2) putting green maintained in the Mid-Atlantic US. Pelletized Ca/Mg-SiO₃ slag or dolomitic limestone treatments were conducted in frequent split applications and incorporated into the upper 5 cm of the rootzone. Measurements of canopy color and density, shoot growth as clipping yield, soil pH, Si and nutrient content of clippings, and soil extractable Si were performed each season. Cumulative Ca/Mg-SiO₃ application (kg ha⁻¹) increased mean acetic acid (HOAc) extractable Si by 35 to 60 mg kg⁻¹ and leaf Si content by 1.0 to 1.5 mg g⁻¹. However, neither putting green canopy quality, shoot nutrient concentration, 5 to 15 cm depth root length density nor ball roll distance was improved by liming agent treatment. Liming agent-treated or untreated plots showed statistical, yet inconsistent, differences in clipping yield 4, 14, 15, 16, and 17 months from initiation (MFI). This thorough shuffling of treatment rank, resulting in identical experiment-wide means precludes the expectation of dependably superior vigor by any. Full article

The sweet potato transplanters of diverse transplanting configurations have been shown to produce various planting properties in relation to different raised bed cropping systems, thus affecting crop growth and yield in sweet potato cultivation. In Shandong Province, a field experiment assessed the effects of three treatments (RB1, mulched raised beds with a finger-clip type transplanter; RB2, bare raised beds with a finger-clip type transplanter; and RB3, bare raised beds with a clamping-plate type transplanter) on soil temperature, plant growth, yield, and economic benefits. With the lowest coefficient variation of plant spacing and planting depth, the RB1 with the finger-clip type transplanter had 6.4% and 6.0% higher temperature at 5–10 cm soil layer by using the plastic-mulch for rapid early slips growth as compared with the RB2 and the RB3, respectively. Consequently, the leaf area index in the RB1 was increased by 5.6% and 6.4% as compared to the RB2 and the RB3, separately. This finally contributed to 57.5–70.8% greater fresh vines weight and 23.8–33.8% higher tubers yield in the RB1 compared with both the RB2 and the RB3 treatments, respectively. In general, in the mulched raised bed system of the Huang-Huai-Hai region of China, the finger-clip type transplanter could be a suitable option for the transplanting of sweet potato slips. In the bare raised bed system, meanwhile, the clamping-plate type transplanter has the potential to increase the production of sweet potatoes. Full article

Knowledge about the spectral response of camelina under different regimes of nitrogen (N) fertilization is very scarce. Therefore, 2-year open-field trials were carried out in the 2021 and 2022 growing seasons with the aim of evaluating the spectral response of spring camelina to four different N fertilization regimes by using remote (UAV) and proximal (leaf-clip Dualex) sensing techniques. The tested treatments were: (i) control: no N application (T0); (ii) top dressing: 60 kg N ha⁻¹ before stem elongation (T1); basal dressing: 60 kg N ha⁻¹ at sowing (T2); basal + top dressing combination: 60 kg N ha⁻¹ at sowing + 60 kg N ha⁻¹ before stem elongation (T3). Camelina seed yield and N use efficiency were strongly affected by fertilization regimes, with the best results obtained at T2. A reduction in plant development and seed yield was detected in 2022, probably due to the rise in air temperatures. A significant effect of both growing season and N fertilization was observed on the photosynthetic pigments content with the T1 highest values in 2022. The highest seed oil content was achieved at T1, while the protein content increased with increasing N, with the best values at T3. Positive and significant correlations were observed among several vegetation indices obtained through UAV flights (NDVI, MRS705, FGCC) and seed yield, as well as between FGCC and leaf N concentration. Overall, these findings demonstrate the feasibility of utilizing remote sensing techniques from UAVs for predicting seed yield in camelina. Full article