Estimating insect pest density using the physiological index of crop leaf.

Estimating population density is a fundamental study in ecology and crop pest management. The density estimation of small-scale animals, such as insects, is a challenging task due to the large quantity and low visibility. An herbivorous insect is the big enemy of crops, which often causes serious losses. Feeding of insects results in changes in physiology-related chemical compositions of crops, but it is unknown whether these changes can be used to estimate the population density of pests. The brown planthopper (BPH), Nilaparvata lugens, is a serious insect pest hiding under rice canopy to suck the sap of rice stems. BPH density is a crucial indicator for determining whether the control using pesticides will be carried out or not. Estimating BPH density is still dependent on manmade survey and light-trap methods, which are time-consuming and low-efficient. Here, we developed a new method based on the physiological traits of rice leaves. The feeding of BPHs significantly decreased the contents of chlorophyll (the SPAD readings), water, silicon, and soluble sugar in rice leaves. Four ratio physiological indices based on these four physiological traits of the BPH-damaged rice leaves to those of healthy leaves were established, and they were significantly correlated with BPH density in rice plants. A rice growth stage-independent linear model based on the four ratio physiological indices and adding the other two variables, BPH damage duration and population increase rate, was developed. This model exhibited a reasonable accuracy for estimating BPH density. This new method will promote the development of density estimation of pest populations toward nonprofessionalization and automation.

Identification of a Rice Leaf Width Gene Narrow Leaf 22 (NAL22) through Genome-Wide Association Study and Gene Editing Technology.

Rice leaf width (RLW) is a crucial determinant of photosynthetic area. Despite the discovery of several genes controlling RLW, the underlying genetic architecture remains unclear. In order to better understand RLW, this study conducted a genome-wide association study (GWAS) on 351 accessions from the rice diversity population II (RDP-II). The results revealed 12 loci associated with leaf width (LALW). In LALW4, we identified one gene, Narrow Leaf 22 (NAL22), whose polymorphisms and expression levels were associated with RLW variation. Knocking out this gene in Zhonghua11, using CRISPR/Cas9 gene editing technology, resulted in a short and narrow leaf phenotype. However, seed width remained unchanged. Additionally, we discovered that the vein width and expression levels of genes associated with cell division were suppressed in nal22 mutants. Gibberellin (GA) was also found to negatively regulate NAL22 expression and impact RLW. In summary, we dissected the genetic architecture of RLW and identified a gene, NAL22, which provides new loci for further RLW studies and a target gene for leaf shape design in modern rice breeding.

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet.

In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%.

A hybrid attention-enhanced DenseNet neural network model based on improved U-Net for rice leaf disease identification.

Rice is a necessity for billions of people in the world, and rice disease control has been a major focus of research in the agricultural field. In this study, a new attention-enhanced DenseNet neural network model is proposed, which includes a lesion feature extractor by region of interest (ROI) extraction algorithm and a DenseNet classification model for accurate recognition of lesion feature extraction maps. It was found that the ROI extraction algorithm can highlight the lesion area of rice leaves, which makes the neural network classification model pay more attention to the lesion area. Compared with a single rice disease classification model, the classification model combined with the ROI extraction algorithm can improve the recognition accuracy of rice leaf disease identification, and the proposed model can achieve an accuracy of 96% for rice leaf disease identification.

Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages.

BACKGROUND: Rice blast, which is prevalent worldwide, represents a serious threat to harvested crop yield and quality. Hyperspectral imaging, an emerging technology used in plant disease research, is a stable, repeatable method for disease grading. Current methods for assessing disease severity have mostly focused on individual growth stages rather than multiple ones. In this study, the spectral reflectance ratio (SRR) of whole leaves were calculated, the sensitive wave bands were selected using the successive projections algorithm (SPA) and the support vector machine (SVM) models were constructed to assess rice leaf blast severity over multiple growth stages. RESULTS: The average accuracy, micro F1 values, and macro F1 values of the full-spectrum-based SVM model were respectively 94.75%, 0.869, and 0.883 in 2019; 92.92%, 0.823, and 0.808 in 2021; and 88.09%, 0.702, and 0.757 under the 2019-2021 combined model. The SRR-SVM model could be used to evaluate rice leaf blast disease during multiple growth stages and had good generalizability. CONCLUSIONS: The proposed SRR data analysis method is able to eliminate differences among individuals to some extent, thus allowing for its application to assess rice leaf blast severity over multiple growth stages. Our approach, which can supplement single-stage disease-degree classification, provides a possible direction for future research on the assessment of plant disease severity during multiple growth stages.

Transcriptomic and physio-biochemical features in rice (Oryza sativa L.) in response to mercury stress.

Mercury (Hg) is a toxic and nonessential element for organisms, and its contamination in the environment is a global concern. Previous research has shown that Hg stress may cause severe damage to rice roots; however, the transcriptomic changes in roots and physio-biochemical responses in leaves to different levels of Hg stress are not fully understood. In the present study, rice seedlings were exposed to 20, 80, and 160 µM HgCl2 for three days in hydroponic experiments. The results showed that the majority of Hg was accumulated in rice roots after Hg exposure, and the 80- and 160-µM Hg stresses significantly increased the root-to-shoot translocation factors relative to 20-µM Hg stress, resulting in elevated Hg concentrations in rice shoots. Only the 160-µM Hg stress significantly inhibited root growth compared with the control, while photosynthesis capacity in leaves was significantly reduced under Hg stress. RNA transcriptome sequencing analyses of the roots showed that common responsive differentially expressed genes were strongly associated with glutathione metabolism, amino acid biosynthesis, and secondary metabolite metabolism, which may play significant roles in Hg accumulation by rice plants. Nine crucial genes identified by protein-protein interaction network analysis may be used as candidate target genes for further investigation of the detoxification mechanism, encoding proteins involved in jasmonic acid synthesis, sugar metabolism, allene oxide synthase, glutathione peroxidase, dismutase, and catalase. Furthermore, physio-biochemical analyses of the leaves indicated that higher production of reactive oxygen species was induced by Hg stress, while glutathione and antioxidant enzymes may play crucial roles in Hg detoxification. Our findings provide transcriptomic and physio-biochemical features of rice roots and shoots, which advance our understanding of the responsive and detoxification mechanisms in rice under different levels of Hg stress.

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model.

Rice is considered one the most important plants globally because it is a source of food for over half the world's population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20-40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.

Effects of temperature and humidity on the contact angle of pesticide droplets on rice leaf surfaces.

The effects of external factors such as temperature, humidity, pesticide formulation, and pesticide concentration on the contact angle of pesticide droplets on rice leaf surfaces were analyzed. The experiments showed that there were significant differences in the contact angles of droplets on the leaf surfaces under different temperatures and humidity. As the ambient temperature increased, the contact angle first decreased and then increased, reaching a minimum value at 25°C. With a gradual increase in humidity, the contact angle significantly increased and reached a maximum at 100% humidity. Finally, it was concluded that both the formulation and concentration of the pesticide had a significant effect on the contact angle of droplets on rice leaf surfaces. The experiments also illustrated that the effects of the pesticide formulation and concentration on the contact angle were more significant than those of temperature and humidity.

Superhydrophobic SLA 3D printed materials modified with nanoparticles biomimicking the hierarchical structure of a rice leaf.

The rice leaf, combining the surface properties of lotus leaves and shark skin, presents outstanding superhydrophobic properties motivating its biomimesis. We created a novel biomimetic rice-leaf superhydrophobic surface by a three-level hierarchical structure, using for a first time stereolithographic (SLA) 3D printed channels (100µm width) with an intrinsic roughness from the printing filaments (10µm), and coated with TiO2 nanoparticles (22 and 100nm). This structure presents a maximum advancing contact angle of 165° characterized by lower both anisotropy and hysteresis contact angles than other 3D printed surfaces, due to the presence of air pockets at the surface/water interface (Cassie-Baxter state). Dynamic water-drop tests show that the biomimetic surface presents self-cleaning, which is reduced under UV-A irradiation. The biomimetic surface further renders an increased floatability to 3D printed objects meaning a drag-reduction due to reduced water/solid contact area. Numerical simulations of a channel with a biomimetic wall confirm that the presence of air is essential to understand our results since it increases the average velocity and decreases the friction factor due to the presence of a wall-slip velocity. Our findings show that SLA 3D printing is an appropriate approach to develop biomimetic superhydrophobic surfaces for future applications in anti-fouling and drag-reduction devices.

Relationship between reduction in rice (Nipponbare) leaf blade size under elevated CO2 and miR396-GRF module.

Elevated CO2 (eCO2; 1000 ppm) influences developing rice leaf formation, reducing leaf blade length and width as compared to rice grown under ambient CO2 (aCO2; 400 ppm). Since micro RNAs (miRNAs) are known to play multiple roles in plant development, we hypothesized that miRNAs might be involved in modulating leaf size under eCO2 conditions. To identify miRNAs responding to eCO2, we profiled miRNA levels in developing rice leaves (P4; plastochron number of the fourth-youngest leaf) under eCO2 using small RNA-seq. We detected 18 mature miRNA sequences for which expression levels varied more than two-fold between the eCO2 and aCO2 conditions. Among them, only miR396e and miR396f significantly differed between the two conditions. Additionally, the expression of growth-regulating factors (GRFs), potential target mRNA of miR396s, were repressed under the eCO2 condition. We used an antisense oligonucleotide approach to confirm that single-strand DNA corresponding to the miR396e sequence effectively downregulated GRF expression in developing leaves, reducing the leaf blade length, such as for rice grown under eCO2. These results suggest that the miR396-GRF module is crucially relevant to controlling rice leaf blade length in eCO2 environments.

Reproduction does not impede the stopover departure to ensure a potent migration in Cnaphalocrocis medinalis moths.

Despite the importance of reproduction in insects, its relation with multi-stop flight remains poorly understood in migratory species. To clarify whether reproductive maturation commences during the multi-stop flight or after the completion of migration, we conducted physiological and behavioral assays in the rice leaf roller Cnaphalocrocis medinalis with laboratory-simulated conditions and field-captured populations. We found that the ovarian development was significantly promoted by tethered flight treatment for 1-2 nights when compared to the unflown group, while the flight muscle development was not impaired. There was no significant difference in flight duration, flight distance and flight velocity between mated and virgin female moths, indicating that mated moths remained competent for the subsequent flights as did the virgins. Using an integrated field assay, we identified that over 60% of the female moths in the migrating populations captured by high-altitude searchlights in the Immigration period of a season had completed the ovarian development and mating. Sexually mature and mated moths collected in the rice field in the Emigration period were found capable of engaging in migratory take-off, as observed using an indoor monitoring platform. Overall, our findings point out that C. medinalis managed to complete reproductive maturation to a large extent during the multi-stop migratory flight without compromising the migration performance. Such a cost-effective strategy ensures a successful migration for the moths. These findings advance our understanding of the relationship between reproduction and migration, thus shedding light on the development of novel control measures for the outbreak of migratory insect pests.

Role of jasmonate signaling in rice resistance to the leaf folder Cnaphalocrocis medinalis.

KEY MESSAGE: Jasmonate-induced accumulation of anti-herbivore compounds mediates rice resistance to the leaf folder Cnaphalocrocis medinalis. The rice leaf folder (LF), Cnaphalocrocis medinalis, is one of the most destructive insect pests in the paddy field. LF larvae induces leaf folding and scrapes the upper epidermis and mesophyll tissues reducing photosynthesis and yield in rice. Identifying plant defense pathways and genes involved in LF resistance is essential to understand better this plant-insect interaction and develop new control strategies for this pest. Jasmonate (JA) signaling controls a plethora of plant defenses against herbivores. Using RNA-seq time series analysis, we characterized changes in the transcriptome of wild-type (WT) leaves in response to LF damage and measured the dynamics of accumulation of JA phytohormone pools in time-course experiments. Genes related to JA signaling and responses, known to mediate resistance responses to herbivores, were induced by LF and were accompanied by an increment in the levels of JA pools in damaged leaves. The accumulation of defense compounds such as phenolamides and trypsin proteinase inhibitor (TPI) also increased after LF infestation in WT but not in JA mutant plants impaired in JA biosynthesis (aoc-2) and signaling (myc2-5). Consistent with all these responses, we found that LF larvae performed better in the JA mutant backgrounds than in the WT plants. Our results show that JA signaling regulates LF-induced accumulation of TPI and phenolamides and that these compounds are likely an essential part of the defense arsenal of rice plants against this insect pest.

Isolation and Functional Analysis of Effector Proteins of Magnaporthe oryzae.

In nature, plants have evolved a myriad of preformed and induced defenses to protect themselves from microbes. Upon microbial infection, the recognition of the microbe-associated molecular patterns (MAMPs) by the pattern recognition receptors (PRRs) triggers the first stage of defense response (Dodds and Rathjen, Nat Rev Genet 11:539-548, 2010). However, in order to develop microbial delivery, effectors target PRRs for deregulating immune responses and facilitating host colonization (Thomma et al., Plant Cell 23:4-15, 2011). Here, we contribute a protocol for the screening system of Magnaporthe oryzae effectors and construct a fluorescent system to trace secretory proteins in the sheath infection samples. Using the tobacco rattle virus (TRV) system, the proteins including LysM, Chitin, Cutinase, and CFEM domains were selected and divided into two kinds according to the results of cell death induced or inhibited test in Nicotiana benthamiana. Then, candidate effectors can be deleted or overexpressed in M. oryzae. The barley or rice infection with M. oryzae, rice leaf sheath inoculation, and subcellular localization during infection can be performed to explore the functions of these effectors.

Rice leaf diseases prediction using deep neural networks with transfer learning.

Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy source. The yield production quantity and quality of the rice grain is affecting by abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, virus, etc. For disease management, farmers spending lot of time and resources and they detect the diseases through their penniless naked eye approach which leads to unhealthy farming. The advancement of technical support in agriculture greatly assists for automatic identification of infectious organisms in the rice plants leaves. The convolutional neural network algorithm (CNN) is one of the algorithms in deep learning has been triumphantly invoked for solving computer vision problems like image classification, object segmentation, image analysis, etc. In our work, InceptionResNetV2 is a type of CNN model utilized with transfer learning approach for recognizing diseases in rice leaf images. The parameters of the proposed model is optimized for the classification task and obtained a good accuracy of 95.67%.

Deciphering core-microbiome of rice leaf endosphere: Revelation by metagenomic and microbiological analysis of aromatic and non-aromatic genotypes grown in three geographical zones.

We have deciphered the leaf endophytic-microbiome of aromatic (cv. Pusa Basmati-1) and non-aromatic (cv. BPT-5204) rice-genotypes grown in the mountain and plateau-zones of India by both metagenomic NGS (mNGS) and conventional microbiological methods. Microbiome analysis by sequencing V3-V4 region of ribosomal gene revealed marginally more bacterial operational taxonomic units (OTU) in the mountain zone at Palampur and Almora than plateau zone at Hazaribagh. Interestingly, the rice leaf endophytic microbiomes in mountain zone were found clustered separately from that of plateau-zone. The Bray-Curtis dissimilarity indices indicated influence of geographical location as compared to genotype per se for shaping rice endophytic microbiome composition. Bacterial phyla, Proteobacteria followed by Bacteroidetes, Firmicutes, and Actinobacteria were found abundant in all three locations. The core-microbiome analysis devulged association of Acidovorax; Acinetobacter; Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium; Aureimonas; Bradyrhizobium; Burkholderia-Caballeronia-Paraburkholderia; Enterobacter; Pantoea; Pseudomonas; Sphingomonas; and Stenotrophomonas with the leaf endosphere. The phyllosphere and spermosphere microbiota appears to have contributed to endophytic microbiota of rice leaf. SparCC network analysis of the endophytic-microbiome showed complex cooperative and competitive intra-microbial interactions among the microbial communities. Microbiological validation of mNGS data further confirmed the presence of core and transient genera such as Acidovorax, Alcaligenes, Bacillus, Chryseobacterium, Comamonas, Curtobacterium, Delftia, Microbacterium, Ochrobactrum, Pantoea, Pseudomonas, Rhizobium, Rhodococcus, Sphingobacterium, Staphylococcus, Stenotrophomonas, and Xanthomonas in the rice genotypes. We isolated, characterized and identified core-endophytic microbial communities of rice leaf for developing microbiome assisted crop management by microbiome reengineering in future.

Comparative transcriptome analysis of the rice leaf folder (Cnaphalocrocis medinalis) to heat acclimation.

BACKGROUND: The rice leaf folder Cnaphalocrocis medinalis Güenée is a serious insect pest of rice in Asia. This pest occurs in summer, and it is sensitive to high temperature. However, the larvae exhibit heat acclimation/adaptation. To understand the underlying mechanisms, we established a heat-acclimated strain via multigenerational selection at 39 °C. After heat shock at 41 °C for 1 h, the transcriptomes of the heat-acclimated (S-39) and unacclimated (S-27) larvae were sequenced, using the unacclimated larvae without exposure to 41 °C as the control. RESULTS: Five generations of selection at 39 °C led larvae to acclimate to this heat stress. Exposure to 41 °C induced 1160 differentially expressed genes (DEGs) between the heat-acclimated and unacclimated larvae. Both the heat-acclimated and unacclimated larvae responded to heat stress via upregulating genes related to sensory organ development and structural constituent of eye lens, whereas the unacclimated larvae also upregulated genes related to structural constituent of cuticle. Compared to unacclimated larvae, heat-acclimated larvae downregulated oxidoreductase activity-related genes when encountering heat shock. Both the acclimated and unacclimated larvae adjusted the longevity regulating, protein processing in endoplasmic reticulum, antigen processing and presentation, MAPK and estrogen signaling pathway to responsed to heat stress. Additionally, the unacclimated larvae also adjusted the spliceosome pathway, whereas the heat-acclimated larvae adjusted the biosynthesis of unsaturated fatty acids pathway when encountering heat stress. Although the heat-acclimated and unacclimated larvae upregulated expression of heat shock protein genes under heat stress including HSP70, HSP27 and CRYAB, their biosynthesis, metabolism and detoxification-related genes expressed differentially. CONCLUSIONS: The rice leaf folder larvae could acclimate to a high temperature via multigenerational heat selection. The heat-acclimated larvae induced more DEGs to response to heat shock than the unacclimated larvae. The changes in transcript level of genes were related to heat acclimation of larvae, especially these genes in sensory organ development, structural constituent of eye lens, and oxidoreductase activity. The DEGs between heat-acclimated and unacclimated larvae after heat shock were enriched in the biosynthesis and metabolism pathways. These results are helpful to understand the molecular mechanism underlying heat acclimation of insects.

High-Throughput Screening of Free Proline Content in Rice Leaf under Cadmium Stress Using Hyperspectral Imaging with Chemometrics.

Tracking of free proline (FP)-an indicative substance of heavy metal stress in rice leaf-is conducive to improve plant phenotype detection, which has important guiding significance for precise management of rice production. Hyperspectral imaging was used for high-throughput screening FP in rice leaves under cadmium (Cd) stress with five concentrations and four periods. The average spectral of rice leaves were used to show differences in optical properties. Partial least squares (PLS), least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models based on full spectra and effective wavelengths were established to detect FP content. Genetic algorithm (GA), competitive adaptive weighted sampling (CARS) and PLS weighting regression coefficient (Bw) were compared to screen the most effective wavelengths. Distribution map of the FP content in rice leaves were obtained to display the changes in the FP of leaves visually. The results illustrated that spectral differences increased with Cd stress time and FP content increased with Cd stress concentration. The best result for FP detection is the ELM model based on 27 wavelengths selected by CARS and Rp is 0.9426. Undoubtedly, hyperspectral imaging combined with chemometrics was a rapid, cost effective and non-destructive technique to excavate changes of FP in rice leaves under Cd stress.

Genome-scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae.

Xanthomonas oryzae pv. oryzae (Xoo) is a vascular pathogen that causes leaf blight in rice, leading to severe yield losses. Since the usage of chemical control methods has not been very promising for the future disease management, it is of high importance to systematically gain new insights about Xoo virulence and pathogenesis, and devise effective strategies to combat the rice disease. To do this, we reconstructed a genome-scale metabolic model of Xoo (iXOO673) and validated the model predictions using culture experiments. Comparison of the metabolic architecture of Xoo and other plant pathogens indicated that the Entner-Doudoroff pathway is a more common feature in these bacteria than previously thought, while suggesting some of the unique virulence mechanisms related to Xoo metabolism. Subsequent constraint-based flux analysis allowed us to show that Xoo modulates fluxes through gluconeogenesis, glycogen biosynthesis, and degradation pathways, thereby exacerbating the leaf blight in rice exposed to nitrogenous fertilizers, which is remarkably consistent with published experimental literature. Moreover, model-based interrogation of transcriptomic data revealed the metabolic components under the diffusible signal factor regulon that are crucial for virulence and survival in Xoo. Finally, we identified promising antibacterial targets for the control of leaf blight in rice by using gene essentiality analysis.

A high-performance system of multiple gas-exchange chambers with a laser spectrometer to estimate leaf photosynthesis, stomatal conductance, and mesophyll conductance.

Direct measurements of ecophysiological processes such as leaf photosynthesis are often hampered due to the excessive time required for gas-exchange measurements and the limited availability of multiple gas analyzers. Although recent advancements in commercially available instruments have improved the ability to take measurements more conveniently, the amount of time required for each plant sample to acclimate to chamber conditions has not been sufficiently reduced. Here we describe a system of multiple gas-exchange chambers coupled with a laser spectrometer that employs tunable diode laser absorption spectroscopy (TDLAS) to measure leaf photosynthesis, stomatal conductance, and mesophyll conductance. Using four gas-exchange chambers minimizes the time loss associated with acclimation for each leaf sample. System operation is semiautomatic, and leaf temperature, humidity, and CO2 concentration can be regulated and monitored remotely by a computer system. The preliminary results with rice leaf samples demonstrated that the system is capable of high-throughput measurements, which is necessary to obtain better representativeness of the ecophysiological characteristics of plant samples.

Phytochemical Analysis and Potential Biological Activities of Essential Oil from Rice Leaf.

Although many investigations on phytochemicals in rice plant parts and root exudates have been conducted, information on the chemical profile of essential oil (EO) and potent biological activities has been limited. In this study, chemical compositions of rice leaf EO and in vitro biological activities were investigated. From 1.5 kg of fresh rice leaves, an amount of 20 mg EO was obtained by distillation and analyzed by gas chromatography-mass spectrometry (GC-MS), electrospray ionization (ESI), and atmospheric pressure chemical ionization (APCI) to reveal the presence of twelve volatile constituents, of which methyl ricinoleate (27.86%) was the principal compound, followed by palmitic acid (17.34%), and linolenic acid (11.16%), while 2-pentadecanone was the least (2.13%). Two phytoalexin momilactones A and B were first time identified in EO using ultra-performance liquid chromatography coupled with electrospray mass spectrometry (UPLC/ESI-MS) (9.80 and 4.93 ng/g fresh weight, respectively), which accounted for 7.35% and 3.70% of the EO, respectively. The assays of DPPH (IC50 = 73.1 µg/mL), ABTS (IC50 = 198.3 µg/mL), FRAP (IC50 = 700.8 µg/mL) and ß-carotene oxidation (LPI = 79%) revealed that EO possessed an excellent antioxidant activity. The xanthine oxidase assay indicated that the anti-hyperuricemia potential was in a moderate level (IC50 = 526 µg/mL) as compared with the standard allopurinol. The EO exerted potent inhibition on growth of Raphanus sativus, Lactuca sativa, and two noxious weeds Echinochloa crus-galli, and Bidens pilosa, but in contrast, the growth of rice seedlings was promoted. Among the examined plants, the growth of the E. crus-galli root was the most inhibited, proposing that constituents found in EO may have potential for the control of the problematic paddy weed E. crus-galli. It was found that the EO of rice leaves contained rich phytochemicals, which were potent in antioxidants and gout treatment, as well as weed management. Findings of this study highlighted the potential value of rice leaves, which may provide extra benefits for rice farmers.