Ontogeny Research Articles

Effect of cultivation and geographical location on chemical composition and enantiomeric distribution in linalool-rich Origanum dubium Boiss. essential oils obtained at different growth stages

ABSTRACT The current study is objected to investigate the essential oil yields, chemical compositions, and enantiomeric distribution of wild and cultivated linalool rich Origanum dubium Boiss. essential oils obtained at pre-flowering, initial flowering, and post-flowering growth stages. Wild plant materials are collected from Mediterranean region of Turkey. Samples were also cultivated in Mediterranean and Aegean coastlines of Turkey in the experimental fields of Ministry of Agriculture and Forestry of Turkey. O. dubium samples yielded between 6.7 and 9.7% (mL/100 g) of essential oils. The compositions were dominated by oxygenated monoterpenes (98.2–98.9%). Linalool was detected as the major compound in all essential oils (97.0–97.6%). The dominant enantiomer was discovered as (R)-(-)-linalool with a 99.3–99.4% enantiomeric purity. The essential oil yields, chemical compositions and enantiomeric distributions were found to be constant regardless of cultivation, geographical locations or different growth stages. The result of this manuscript indicates that by culturing linalool chemotype of O. dubium, the industrial need for (R)-(-)-linalool could be met at more affordable costs.

Deciphering the roles of bacterial and fungal communities in the formation and quality of agarwood

Aquilaria sinensis is a significant resin-producing plant worldwide that is crucial for agarwood production. Agarwood has different qualities depending on the method with which it is formed, and the microbial community structures that are present during these methods are also diverse. Furthermore, the microbial communities of plants play crucial roles in determining their health and productivity. While previous studies have investigated the impact of microorganisms on agarwood formation, they lack comprehensiveness, particularly regarding the properties of the microbial community throughout the entire process from seedling to adult to incense formation. We collected roots, stems, leaves, flowers, fruits and other tissues from seedlings, healthy plants and agarwood-producing plants to address this gap and assess the dominant bacterial species in the microbial community structures of A. sinensis at different growth stages and their impacts on growth and agarwood formation. The bacteria and fungi in these tissues were classified and counted from different perspectives. The samples were sequenced using the Illumina sequencing platform, and sequence analyses and species annotations were performed using a range of bioinformatics tools to assess the plant community compositions. An additional comparison of the samples was conducted using diversity analyses to assess their differences. This research revealed that Listeria, Kurtzmanomyces, Ascotaiwania, Acinetobacter, Sphingobium, Fonsecaea, Acrocalymma, Allorhizobium, Bacillus, Pseudomonas, Peethambara, and Debaryomyces are potentially associated with the formation of agarwood. Overall, the data provided in this article help us understand the important roles played by bacteria and fungi in the growth and agarwood formation process of A. sinensis, will support the theoretical basis for the large-scale cultivation of A. sinensis, and provide a basis for further research on microbial community applications in agarwood production and beyond.

Study on Maturity Indices and Effect of Growing Substrates on Seed Germination of Litsea glutinosa

Aim: Litsea glutinosa is a multipurpose, fast-growing tree widely exploited due to its multipurpose utilisation. Species reported low seed viability, hence generally propagated through vegetative methods. We studied the maturity indices of fruits and seeds to determine the optimal collection stage. Studied the effect of different growing substrates and Gibberellic acid (GA3) treatment on the germination percent (GP), mean germination time (MGT), germination value (GV), peak value (PV), and germination index (GI). Study Design: Study was conducted at Forest Tree Seed Laboratory, ICFRE-FRI. The change in fruit colour from green, dark brown purple, and dark blue stages was compared with the seed parameters, germination percent, mean germination time, germination value, etc. to obtain the most suitable stage for fruit and seed maturity. Seed germination experiment was conducted in Complete Randomized Design with four replications of 25 seeds for each treatment. Seeds were sown directly on different germination substrates, such as filter paper, sand, and vermiculite, without treatment and also with 0.05% GA3 pretreatment. Seeds were kept in the seed germinator at 25±1ºC with a 24-hour photoperiod and high relative humidity (RH > 90%). Results: The highest germination rate was observed in vermiculite with GA3 soaking . i.e., 80±3.26%, followed by vermiculite 68±8.64%. The lowest germination percent of 5±4.08% was observed in seeds plated on filter paper with GA3 soaking. No germination was recorded in the seeds plated on the filter paper. Conclusion: Most suitable germination medium to maximise the germination of L. glutinosa seed is vermiculite. Furthermore, the pretreatment of seeds in GA3 (0.05%) for 24 hours and sowing in vermiculite improves overall performance with respect to GP, MGT, GV, PV, and GI. In the context of maturity indices, the dark blue stage with a moisture content of around 50% is the right stage for the collection of seeds of the species. This stage is characterised by high GP, GV, and low MGT values as compared to other growth stages.

Effects of depressions and drainage systems on soybean (Glycine max (L.) Merill) growth during the early growth stages under excess water stress conditions

ABSTRACT Soybean (Glycine max (L.) Merill) productivity is severely compromised under conditions of excess water, particularly during the early growth stages. Depressions in converted paddy fields are believed to significantly contribute to excess water stress. Farmers typically use drainage systems, such as field ditches and pipe drains to improve field drainage. In this study, we aimed to evaluate the impacts of depressions and drainage systems on soybean growth during the early growth stages in farming fields. Using uncrewed aerial vehicles and imaging processing software, we observed the soybean coverage ratio at two and four weeks after sowing. We analyzed the relationship between the coverage ratio and depression depth, distance from field ditches, and distance from pipe drains. Depressions deeper than 2 cm were present in all study fields, and the coverage ratio increase from 2 to 4 weeks after sowing was negatively correlated with depression depth. Although the distance from pipe drains was negatively related to the coverage ratio increase, the distance from field ditches had a limited effect. The construction of pipe drains significantly improved the coverage ratio increase, particularly in areas with deep depressions and near pipe drains, by up to 23.0 times in this study. Therefore, constructing pipe drains near depressions is an effective way to mitigate the impact of depressions and excess water stress on soybean growth during the early stages. Our results will be useful for developing strategies to improve drainage and soybean growth during early growth stages.

Optimizing Stand Spatial Structure at Different Development Stages in Mixed Hard Broadleaf Forests

Thinning plays a key role in regulating the stand spatial structure (SpS) to improve the development of stand quality, and the stand has different characteristics of stand structure (SS) at different growth and development stages (DSs), so it is most important to reasonably determine the stage of growth and development of the stand to optimize the stand structure. We applied the TWINSPAN two-way indicator species analysis method to classify the different development stages of mixed hard broadleaf forests. We provided a comprehensive stand spatial structure optimization model for three selected plots at different development stages, respectively, to optimize the SpS. The results demonstrated the classified DS of 29 mixed hard broadleaf plots for three forest stages: the establishment stage, competitive stage, and quality selection stage. We then applied the SpS optimization model to our three plots; the Q(x) increased by 124.04%, 333.74%, and 116.83% when compared with those with no harvest, in which, upon the removal of 10% of the trees from the three plots, the maximum RIP values were all observed. Our results indicated that the SpS optimization model could regulate the SS for different growth stages and DSs.

Soil bacterial communities are influenced by mulching methods and growth stages in dryland wheat fields

Soil bacterial communities are influenced by mulching methods and growth stages in dryland wheat fields

Qingqianliutianosides A–E: five new sweet dammarane triterpenoid glycosides derived from the leaves of Cyclocarya paliurus – identification, characterization and interactions with T1R2/T1R3 sweet taste receptors

AbstractBACKGROUNDCyclocarya paliurus, as a new food resource, is utilized extensively in human and animal diets due to its bioactive compounds, health benefits, and its highly prized sweet flavor. This study aimed to investigate the sweet‐taste ingredient of C. paliurus leaves.RESULTSFive new dammarane triterpenoid glycosides were isolated and identified as qingqianliutianosides A–E (1–5) by comprehensive spectroscopic data analysis and a single crystal X‐ray diffraction experiment. Qingqianliutianoside A (1) and qingqianliutianoside C (3), present in relatively high quantities in the plant, were shown to exhibit sweetness by sensory evaluation and electronic tongue analysis. Further monitoring was conducted on the content changes in 3 in leaves at different growth stages, indicating that 3 reached its peak content in April and then showed a decreasing trend. Molecular docking studies revealed that T1R2/T1R3 receptors Ser212, Ser105, Thr239, Asn380, Thr305, and Val381 may play critical roles, demonstrating that hydrogen bonding and hydrophobic interactions were the dominant interaction forces between all of the identified compounds and the active sites in the Venus flytrap module of the T1R2/T1R3 receptors.CONCLUSIONQingqianliutianosides A–E are promising natural source sugar substitutes for use in functional foods and beverages. © 2024 Society of Chemical Industry.

Coulomb Driven Electro-Convection within Two Stacked Layers of Miscible Dielectric Liquids

This article investigates the behavior of two parallel layers of different miscible dielectric liquids enclosed and sandwiched between two electrodes. By applying an electric potential to one electrode while grounding the other, electro-convection occurs when the electric Rayleigh number exceeds a critical value, setting the fluid into motion and resulting in rapid mixing between the two liquids. A numerical model is developed to account for the varying ionic mobility and permittivity of the two liquids, considering their evolution based on the relative concentration field. The simulations confirm that electro-convection significantly enhances the mixing between the two liquids, as expected. Additionally, intriguing ripples are observed near the initial interface during the early stages of electro-convection instability growth. To explain and describe the flow dynamics in terms of stability analysis, a semi-analytical model is presented. This study provides insights into the mixing behavior and flow dynamics of miscible dielectric liquids under the influence of electro-convection. The findings contribute to a better understanding of the underlying mechanisms and can be valuable for applications such as microfluidics, energy conversion, and mixing processes. Further research is encouraged to explore additional parameters and optimize the control of electro-convection for practical applications.

Changes in Spatiotemporal Pattern and Its Driving Factors of Suburban Forest Defoliating Pest Disasters

Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study’s methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations.

Effect of Nano Urea on Growth, Yield and Nutrient uptake of Finger Millet (Eleusine coracana L.)

Aim: The foliar application of nano urea is aimed at reducing the soil application of conventional urea. Nano fertilizers enhance crop growth and yield while enhancing nutrient uptake and reducing costs. They provide precise nutrient management, match crop growth stages, and offer an increased surface area for metabolic reactions. This boosts photosynthesis, leading to higher dry matter production and crop yield. Foliar application of nano urea (liquid) at tillering and booting stages in finger millet fulfills the nitrogen requirement and reflects higher crop productivity. Higher productivity of crops in a sustainable manner could be achieved by applying an appropriate combination of conventional fertilizers and nano fertilizers. Study Design: The experiment was laid out in split design with three replications. Place and Duration of Study: A field experiment was conducted in deep black soils at Agricultural Research Station, Siruguppa, during kharif 2022. Methodology: There were sixteen treatment combinations, consisting of different doses of nitrogen (no nitrogen, 50% RDN, 75% RDN and 100% RDN) with different concentrations of nano urea and conventional urea sprayed at tillering and booting stages for HR-13 variety of finger millet. Results: Application of 100% RDN has recorded significantly higher plant height, tillers plant-1, total dry matter accumulation, grain yield and straw yield (129.82 cm, 10.10, 36.17 g plant-1, 36.87 q ha-1 and 71.72 q ha-1, respectively) in comparison to no nitrogen and 50% RDN. However, 75% RDN (123.04 cm, 9.61, 34.02 g plant-1, 34.84 q ha-1 and 68.01 q ha-1, respectively) found on par with 100% RDN. Among foliar nitrogen, spray of nano urea @ 4 ml L-1 recorded significantly higher plant height (121.59 cm), tillers plant-1 (9.37), total dry matter accumulation (33.16 g plant-1), grain yield (31.97 q ha-1) and straw yield (65.52 q ha-1) compared to 0.5% and 1% conventional urea. However, nano urea @ 2 ml L-1 was on par with 4 ml L-1 nano urea and 1% conventional urea. Similar trend was recorded with regard to nutrient uptake by finger millet. Conclusion: Foliar application of nano urea reduced the dosage of conventional urea by 25 per cent.

Correction to: Three-dimensional image analysis specifies the root distribution for drought avoidance in the early growth stage of rice

Correction to: Three-dimensional image analysis specifies the root distribution for drought avoidance in the early growth stage of rice

Harnessing UAVs and deep learning for accurate grass weed detection in wheat fields: a study on biomass and yield implications

Weeds are undesired plants competing with crops for light, nutrients, and water, negatively impacting crop growth. Identifying weeds in wheat fields accurately is important for precise pesticide spraying and targeted weed control. Grass weeds in their early growth stages look very similar to wheat seedlings, making them difficult to identify. In this study, we focused on wheat fields with varying levels of grass weed infestation and used unmanned aerial vehicles (UAVs) to obtain images. By utilizing deep learning algorithms and spectral analysis technology, the weeds were identified and extracted accurately from wheat fields. Our results showed that the precision of weed detection in scattered wheat fields was 91.27% and 87.51% in drilled wheat fields. Compared to areas without weeds, the increase in weed density led to a decrease in wheat biomass, with the maximum biomass decreasing by 71%. The effect of weed density on yield was similar, with the maximum yield decreasing by 4320 kg·ha− 1, a drop of 60%. In this study, a method for monitoring weed occurrence in wheat fields was established, and the effects of weeds on wheat growth in different growth periods and weed densities were studied by accurately extracting weeds from wheat fields. The results can provide a reference for weed control and hazard assessment research.

The Early Prediction of Kimchi Cabbage Heights Using Drone Imagery and the Long Short-Term Memory (LSTM) Model

Accurate and timely crop growth prediction is crucial for efficient farm management and food security, particularly given challenges like labor shortages and climate change. This study presents a novel method for the early prediction of Kimchi cabbage heights using drone imagery and a long short-term memory (LSTM) model. High-resolution drone images were used to generate a canopy height model (CHM) for estimating plant heights at various growth stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this time series data to predict the final height at harvest well before the actual harvest date. The model trained on data from 44 days after planting (DAPs) demonstrated the highest accuracy (R2 = 0.83, MAE = 2.48 cm, and RMSE = 3.26 cm). Color-coded maps visualizing the predicted Kimchi cabbage heights revealed distinct growth patterns between different soil types, highlighting the model’s potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed most suitable for practical applications, enabling timely interventions in cultivation management. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for the early and accurate prediction of Kimchi cabbage heights, facilitating data-driven decision-making in precision agriculture for improved crop management and yield optimization.

Interaction Effects of Nutrient and Water on Crop Production System under Dry and Irrigated Agriculture System

Many areas in India, including dryland areas, are under stress from two main obstacles to agricultural output, namely, inadequate soil nutrients and a scarcity of water. In most rainfed regions, except for a few places that can conduct irrigation, low precipitation levels and their erratic distribution usually result in soil water deficiency which leads to water as well as the instances of nutritional stress. Hence, in sustainable agricultural production systems, soil water and nutrient stress are major concerns. In crop production systems, the interplay between water and nutrients might yield favourable results, contingent on the growth stages, volumes, combinations, and balance of the crops. In irrigated areas, water and nutrients have interdependent effects. Efficient water management can enhance nutrient availability and transformation in soil or fertilizers. Water boosts plant nutrient content, uptake, and overall efficiency. Water scarcity stresses plants, impeding root growth and nutrient absorption. Additionally, it has been discovered that there is typically a strong, nearly linear correlation between crop yield and nutrient and water management techniques. Water availability and nutrient efficiency are therefore intimately related. The water state of the soil should be taken into consideration when applying nutrients in a balanced manner, taking into account their types, ratios, amounts, timing, and methods, in addition to their capacity to give nutrients. Therefore, determining the best time or growth stage to add water and nutrients to various crops, as well as how water and nutrients interact, are essential for a sustainable crop production system.

Abundance and diversity of cotton insect pests under repeatedly cultivated cotton fields of Tanzania

IntroductionCotton production in Tanzania is facing significant challenges due to insect pests that cause extensive damages to the crop. The most notable pests include the African bollworm (Heliothis armigera Hubner), Spiny bollworm (Earias biplaga Walker), Cotton stainers (Dysdercus sidae (Herrich-Schaeffer), Cotton Aphids (Aphis gossypii Glover), Thrips (Thrips tabaci Lindeman), Jassids (Amrasca biguttula, Bigutula), Leafhoppers (Cicadellidae jassidae), and Whiteflies (Bemisia tabaci Genn). If left uncontrolled, these pests can cause up to 60% damage to the crop. Despite the importance of cotton and the fact that most of these pests are endemic, there are scanty knowledge on the dynamics and distribution of cotton pests across the seasons of the year and crop’s phenological growth stages (germination, vegetative growth, flowering and boll formation) in areas under repeated cultivation of the crop in Tanzania. Here we report on the influence of seasons and cotton’s phenological stages on the abundance, diversity, distribution and richness of cotton insect pests.MethodsThe study was conducted in the Misungwi district for two cotton-growing seasons, using the UKM08 cotton variety. Stick traps and handpicking methods were deployed in catching the cotton insect pests.ResultsOn average, a total of 8,500 insect specimen of diverse families and species were collected every season. The four dominant species among the collected were Aphis gossypii (17.37%), Amrasca biguttula (11.42%), Nezara viridura (10.7%), and Bemisia tabacci (10.68%). Both cotton phenological growth stages and seasons significantly (p<0.05) influenced the abundance, diversity, distribution and richness of cotton insect pests. In particular, the phenological growth stage 3 exhibited greater diversity of insect pests. The pests’ distribution patterns remained relatively uniform across the crop growth stages.DiscussionFindings from the present study could contribute to developing sustainable pest management strategies in areas under repeated cotton production in Tanzania and elsewhere.

Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data

IntroductionCrop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling.MethodsThe current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height.Results and DiscussionThis study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model’s estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model’s R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93.ConclusionIn conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

Substituting partial chemical nitrogen fertilizers with organic fertilizers maintains grain yield and increases nitrogen use efficiency in maize

IntroductionLong-term application of excessive nitrogen (N) not only leads to low N use efficiency (NUE) but also exacerbates the risk of environmental pollution due to N losses. Substituting partial chemical N with organic fertilizer (SP) is an environmentally friendly and sustainable fertilization practice. However, the appropriate rate of SP in rainfed maize cropping systems in semi-arid regions of China is unknown.MethodsTherefore, we conducted a field experiment between 2021 and 2022 in a semi-arid region of Northern China to investigate the effects of SP on maize growth, carbon and N metabolism (C/NM), and NUE. The following treatments were used in the experiment: no N application (CK), 100% chemical N (SP0, 210 kg N ha–1), and SP substituting 15% (SP1), 30% (SP2), 45% (SP3), and 60% (SP4) of the chemical N. The relationship between these indicators and grain yield (GY) was explored using the Mantel test and structural equation modeling (SEM).Results and discussionThe results found that the SP1 and SP2 treatments improved the assimilates production capacity of the canopy by increasing the leaf area index, total chlorophyll content, and net photosynthetic rate, improving dry matter accumulation (DMA) by 6.2%–10.6%, compared to the SP0 treatment. SP1 and SP2 treatments increased total soluble sugars, starch, free amino acids, and soluble protein contents in ear leaves via increasing the enzymatic reactions related to C/NM in ear leaves during the reproductive growth stage compared with SP0 treatment. The highest plant nitrogen uptake (PNU) and nitrogen recovery efficiency were obtained under the SP2 treatment, and the GY and nitrogen agronomic efficiency were higher than the SP0 treatment by 9.2% and 27.8%. However, SP3 and SP4 treatments reduced DMA and GY by inhibiting C/NM in ear leaves compared to SP0 treatment. Mantel test and SEM results revealed that SP treatments indirectly increased GY and PNU by directly positively regulating C/NM in maize ear leaves. Therefore, in the semi-arid regions, substituting 30% of the chemical N with SP could be considered. This fertilizer regime may avoid GY reduction and improve NUE. This study provides new insights into sustainable cultivation pathways for maize in semi-arid regions.

Growth and Development Stages of Four Japanese Persimmon Varieties in North Florida

Japanese persimmon (Diospyros kaki) are a good source of vitamins A and C and dietary fiber, while also being low in calories and fat. These fruits are popular in many Asian cuisines and are enjoyed in various ways, including fresh, dried, or cooked in multiple value-added products. This publication will provide information on persimmon phenological growth stages of four established varieties grown at the UF/IFAS North Florida Research and Education Center in Quincy, Florida. The nine major persimmon growth stages are divided into two categories: vegetative and reproductive stages. This publication provides illustrative figures for each development stage. Accurate identification of phenological stages is important for making informed decisions on plant care, fertilizer, irrigation, and fruit harvesting. This information will be helpful to students, growers, homeowners, nursery workers, and Extension agents interested in growing persimmons in Florida.

Exploring Lignin Biosynthesis Genes in Rice: Evolution, Function, and Expression

Lignin is nature’s second most abundant vascular plant biopolymer, playing significant roles in mechanical support, water transport, and stress responses. This study identified 90 lignin biosynthesis genes in rice based on phylogeny and motif constitution, and they belong to PAL, C4H, 4CL, HCT, C3H, CCoAOMT, CCR, F5H, COMT, and CAD families. Duplication events contributed largely to the expansion of these gene families, such as PAL, CCoAOMT, CCR, and CAD families, mainly attributed to tandem and segmental duplication. Microarray data of 33 tissue samples covering the entire life cycle of rice suggested fairly high PAL, HCT, C3H, CCoAOMT, CCR, COMT, and CAD gene expressions and rather variable C4H, 4CL, and F5H expressions. Some members of lignin-related genes (OsCCRL11, OsHCT1/2/5, OsCCoAOMT1/3/5, OsCOMT, OsC3H, OsCAD2, and OsPAL1/6) were expressed in all tissues examined. The expression patterns of lignin-related genes can be divided into two major groups with eight subgroups, each showing a distinct co-expression in tissues representing typically primary and secondary cell wall constitutions. Some lignin-related genes were strongly co-expressed in tissues typical of secondary cell walls. Combined HPLC analysis showed increased lignin monomer (H, G, and S) contents from young to old growth stages in five genotypes. Based on 90 genes’ microarray data, 27 genes were selected for qRT-PCR gene expression analysis. Four genes (OsPAL9, OsCAD8C, OsCCR8, and OsCOMTL4) were significantly negatively correlated with lignin monomers. Furthermore, eleven genes were co-expressed in certain genotypes during secondary growth stages. Among them, six genes (OsC3H, OsCAD2, OsCCR2, OsCOMT, OsPAL2, and OsPAL8) were overlapped with microarray gene expressions, highlighting their importance in lignin biosynthesis.

A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model

Smart agriculture has become an inevitable trend in the development of modern agriculture, especially promoted by the continuous progress of large language models like chat generative pre-trained transformer (ChatGPT) and general language model (ChatGLM). Although these large models perform well in general knowledge learning, they still have certain limitations and errors when facing agricultural professional knowledge about crop disease identification, growth stage judgment, and so on. Agricultural data involves images and texts and other modalities, which play an important role in agricultural production and management. In order to better learn the characteristics of different modal data in agriculture, realize cross-modal data fusion, and thus understand complex application scenarios, we propose a framework AgriVLM that uses a large amount of agricultural data to fine-tune the visual language model to analyze agricultural data. It can fuse multimodal data and provide more comprehensive agricultural decision support. Specifically, it utilizes Q-former as a bridge between an image encoder and a language model to achieve a cross-modal fusion of agricultural images and text data. Then, we apply a Low-Rank adaptive to fine-tune the language model to achieve an alignment between agricultural image features and a pre-trained language model. The experimental results prove that AgriVLM demonstrates great performance in crop disease recognition and growth stage recognition, with recognition accuracy exceeding 90%, demonstrating its capability to analyze different modalities of agricultural data.