Brown Spot Disease Research Articles

Accurate cotton diseases and pests detection in complex background based on an improved YOLOX model

Cotton diseases and pests are essential factors affecting the quality and yield of cotton in agricultural production. To meet the needs of intelligent agriculture development and solve the problems of low efficiency and poor reliability of cotton diseases and pests detection, this study proposed a real-time high-performance detection model based on improved YOLOX. The model introduced Efficient Channel Attention (ECA), hard-Swish activation function, and Focal Loss function into YOLOX, which improved the ability of the model to extract image features, solved the problem of sample imbalance, improved the detection speed and accuracy, and enhanced the detection effect of cotton diseases and pests. A total of 5760 manually labeled cotton diseases and pests images (including five kinds of red leaf blight, verticillium wilt, cotton spider mite damage, double-spotted leaf beetle damage, and brown spot disease) were used to fine-tune and test the model. The mean Average Precision (mAP) of cotton diseases and pests detection reached 94.60%, the precision was 94.04%, the F1-score was 0.90, and the FPS was 74.21. Furthermore, the results were compared with five classical object detection algorithms (Faster R-CNN, SSD, YOLOv3, YOLOv4, and YOLOv5). The comparative results showed that the mAP of the improved model was 11.50%, 21.17%, 9.34%, 10.22%, and 8.33% higher than the other five algorithms, and the detection speed can meet the real-time requirements. Finally, a cotton diseases and pests detection software was designed and developed based on the improved model, deployed on the smartphone to complete the real-time detection of cotton diseases and pests in the field environment. The improved model can effectively detect the infected area of cotton leaves in the field and provide theoretical reference and technical support for controlling cotton diseases and pests.

Aerobiological dynamics of Bipolaris oryzae in the rice agroecosystem and its impact on rice brown spot disease severity

Rice brown spot disease (RBS), caused by seed-borne and air-borne pathogen Bipolaris oryzae, appears throughout the rice growing season. Considering its polycyclic nature and its significance in yield loss, a one-year (2019) aerobiological study was carried out at Thiruthani (13°11′N 79°38′E) village in Thiruvallur district during two seasons viz., kharif (April 2019 to July 2019) and rabi (August 2019-December 2019). B. oryzae airspora contributed to an average of 20.7% annually. Amongst the barren land conditions, it contributed 11.83%, whereas during rabi 32.52% and kharif 18.04%. The average length (range 40.0-129.8) and average breadth (range 13.17-23.92) of B. oryzae spores were used to calculate dvolume (range 20.54-42.03) and aspect ratio (2.29-6.85). The intra-diurnal behaviour pattern indicated that B. oryzae airspora percentage contribution was highest at 12 noon-1 p.m. (55.64%) in rabi crop and two peaks at 10-11 a.m. (19.56%) and at 12 noon-1 p.m. (19.09%) in kharif crop. The calculated correlation coefficient R value revealed that relative humidity (kharif R=0.9879, rabi R=0.9977) and precipitation in kharif (0.9279) were positively correlated, while the temperature in rabi was negatively correlated with the RBS pathogen airspora percentage. Both the airspora percentage contributions of the RBS pathogen and the PDI were highly correlated (R=0.9002 in kharif, R=0.9874 in rabi). In the Step-Down regression analysis, R2 value (regression coefficient) unveiled that relative humidity (R2 value=0.99760 in kharif, 0.9954 in rabi), precipitation (R2=0.8480) and wind speed (R2=0.7319) in kharif, and temperature in rabi (R2=0.8862) had a greater impact on the RBS pathogen airspora per cent contribution. The airspora B. oryzae per cent contribution aggravated disease occurrence and reflected in the PDI (R2 = 0.8103 in kharif, R2=0.9748 in rabi). The calculated P value and OD ratio supported the significance of the experimental results.

Selection and characterization of a yeast strain for the suppression of brown spot on Tru Long pummelo (Citrus maxima)

Tru Long pummelo is an endemic plant of Quang Nam province with high economic value. However, many different diseases, especially brown spot disease caused by Alternaria fungus on pummelos, have affected the quality of pummelos. Pesticides are overused and misused for the environment and human health. Therefore, there is an urgent need to reduce the use of synthetic chemicals. Biological control offers an alternative to the use of pesticides. Eight yeast strains have been isolated from healthy leaves, pummelo fruits, and healthy mulberries. They were screened for biological activity against Alternaria sp. by the dual culture method. The inhibitory potency ranged from 10.46% to 59.86%. The strain with the highest percentage (59.86%) was identified as Candida tropicalis using sequence analysis of the ITS region.

Morpho-Cultural Variability Characterization of Bipolaris oryzae Causing Brown Leaf Spot of Rice in Jammu Sub-Tropics

Extensive fortnightly surveys were undertaken in major rice growing districts of Jammu division to study cultural and morphological variability amongst the isolates of brown leaf spot disease causing fungus (Bipolaris oryzae). Based on disease severity 24 isolates (BO1 to BO24) were divided into three groups (I, II and III). Group ‘I’ comprised 11 isolates with disease severity values between 51.22-67.44% and categorized under highly virulent and group ‘II’ comprised disease severity values between 43.17-48.17% and were categorized under moderately virulent isolates. Group ‘III’ included eight isolates with disease severity value between 26.11-38.17% and categorized under less virulent isolates. On the basis of colony growth pattern, margin and colour, the isolates were categorized into 4 groups (A, B, C and D). Group A comprised of six isolates had entire margin, circular form, umbonate elevation with smooth surface and black to fluffy growth. Eleven isolates were categorised under group B developed colonies with undulated margin, irregular form, umbonate elevation with grey fluffy growth and greyish colour. Group C comprised three isolates had undulated margin, irregular form, umbonate elevation with smooth surface. Group D contained four isolates developed entire margin, circular form; raised elevation with wrinkled surface, mycelium was grey which turned to black and showed grey to black pigmentation. The largest conidial size was recorded in isolate BO1 (98.43-101×24.54-25.54 µm). The conidia developed mean septa of 4.5-7.4 with straight to curve shaped. The sporulation of different isolates of B. oryzae ranged between 6.1-7.6×106 spores/ml.

Effect of Rates and Sources of N Fertilizer Application on Dynamics of Rice Brown Leaf Spot Disease (Bipolaris oryzae) Incidences in the Dry Zone of Sri Lanka

Nitrogen inputs; sources or application amounts are key determinants of yield determination and determination of resistance or sensitivity to pathogen activities. This study aimed at assessing the impact of source and rate of application of N fertilizers on Rice Brown Leaf Spot (RBLS) disease incidences and dynamics in lowland irrigated rice crops. Leaf N using relative leaf chlorophyll content, leaf N concentration and crop yield were assessed during the wet 2018/19, 2019/20 and 2020/21 seasons and dry 2019 and 2020 seasons in the field research facility of Rajarata University of Sri Lanka. The conventional systems (The Department of Agriculture recommended inorganic fertilizer application at 100% N), integrated system (50% N with conventional through inorganic fertilizer and 25% N with organic manure mixture), and organic system (50% of N conventional through organic manure) were tested using a new improved rice variety Bg300, using a randomized complete block design with six replicates. Wet and dry seasons were contrastingly different in disease prevalence, where critical levels of incidences were visible earlier in the wet season compared to the dry season. Initial stages of the study, organic systems resulted in higher disease incidences, thus reaching infections of the full crop before conventional and integrated. Several seasons of continuous organic manure incorporation enhanced the resistance of organic systems to RBLS disease compared to the rest. The leaf N concentrations were higher in conventional, thus the RBLS incidences were relatively low, due to negative correlations between disease incidences. Rice yields also resulted in a significant negative correlation with disease incidences and were diminished in integrated and organic systems later. The yield suppression due to diseases such as RBLS in organic transition can be overcome by using an integrated approach and building a balanced substitutable nutrient management strategy.

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%.

Analysis of Methods of Machine Learning Techniques for Detection and Classification of Brown Spot (Rice) Disease

Rice is a major crop and the increased production is pertinent to ensure our food security. Modern techniques and advancements are required in agriculture to enable us to improve productivity and environment-friendliness as well as improve their farming conditions. Automatic disease detection techniques can help in identifying the various rice plant diseases. In agriculture, image processing is commonly used to obtain valuable information about crops. Images are often considered a source of information and data. Machine learning (ML) is a promising modern technique for image processing, and it has been successfully used in various areas such as agriculture. This paper aims to study the various research efforts that are focused on this technology in the field of agriculture. One particular class of ML that is commonly used in this work is convolutional neural networks. Here convolutional neural networks (CNNs) have been developed over the support vector machine (SVM) techniques to identify rice diseases (Brown spot) and measure their accuracy. Their potential applications include the development of screening tools and solutions for agricultural production. This paper presents an overview and research outcomes using SVM and CNN techniques in the study of agricultural problems. This study includes a dataset of 1488 healthy leaves and 523 brown spot leaf data samples. The research outcomes are based on studies and development using SVM and CNN. ML techniques give 82% accuracy using the SVM classification method, while the CNN method gives 95% accuracy.

Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

In order to improve the problems of poor accuracy and low efficiency in tobacco leaves disease recognition and diagnosis and avoid the misjudgment in tobacco disease recognition, a disease recognition and spot segmentation method based on the improved ORB algorithm was proposed. The improved FAST14-24 algorithm was used to preliminarily extract corners. It overcame the deficiency of the sensitivity of the traditional ORB corner detection algorithm to image edges. During the experiment, 28 parameters were obtained through the extraction of color features, morphological features, texture, and other features of tobacco disease spots. Through the experimental comparisons, it was found that the fitness of the improved ORB algorithm was 96.68 and the cross-checking rate was 93.21%. The validation and recognition rate for samples was 96%. The identification rate of tobacco brown spot disease and frog eye disease was 92%, and the identification rate of 6 categories in different periods was over 96%. The experimental results verified the effects of the disease identification fully.

Effects of silica soil amendment against Exserohilum rostratum, the fungal pathogen of rice brown spot disease in Peninsular Malaysia

Rice brown spot (BS) exerts devastating agronomic effects on grain quality and overall productivity. In Peninsular Malaysia, BS disease incidence is fairly prevalent and little is known about the diversity of BS pathogens in the local granaries. Fifteen isolates from BS symptomatic rice plants were identified at five different rice granaries across Peninsular Malaysia. Based on the morphological and molecular analyses, two isolates were confirmed as Bipolaris oryzae while the rest were identified as Exserohilum rostratum. Phylogenetic tree analysis revealed that BS incidence in rice granaries in Peninsular Malaysia is caused by a pair of closely related fungal pathogens, E. rostratum and B. oryzae, with the former being more predominant. Cultural characterization of E. rostratum isolate KT831962 showed the best growth and sporulation activity on corn meal agar plates incubated in complete darkness. The effects of calcium silicate (CaSiO3) and rice husk ash (RHA) soil amendment against MR219 and MR253 rice varieties were evaluated during rice-E. rostratum interaction. Results showed that soil amelioration using CaSiO3 and RHA singly and in combination with manganese (Mn) significantly reduced rice BS disease severity. The BS disease index was reduced significantly to less than 31.6% in the silicon-treated rice plants relative to the control plants at 41.2%. Likewise, the grain yield at the harvest stage showed significantly higher yield in the Si-treated rice plants in comparison to the control, non-Si treated rice plants. The findings highlight the potential of RHA agro-waste as Si fertilizer in a sustainable rice production system.

Elucidation of the population structure and genetic diversity of Bipolaris oryzae associated with rice brown spot disease using SSR markers.

The online version contains supplementary material available at 10.1007/s13205-022-03347-4.

Morphogenetic variability, cultural characteristics, aggressiveness, and transmission of <em>Bipolaris oryzae</em> isolates in Rio Grande do sul

Brown spot disease, caused by Bipolaris oryzae, is one of the most important rice diseases. This pathogen causes reductions in rice crop yield and quality, and its variability has been a problem for the management of brown spot disease due to its morphological and cultural similarity with other species that cause leaf spots and grain blight. The aim of this study was to determine the variability of B. oryzae isolates using different approaches and report information on patterns in aggressiveness and transmission of this pathogen from rice seed to seedlings. Fifteen isolates from different geographic rice crop regions of Rio Grande do Sul were used. The isolates were analyzed for genetic diversity, morphological and cultural characteristics, aggressiveness, and transmission ability from rice seeds to seedlings. Isolates fell into five morphological groups, based on cultural characteristics. Range values of the spore dimensions, color and mycelial growth showed high variation among the isolates. The most aggressive and transmissible isolates were those that form black colonies and had suppressed growth. The results show that isolates from Rio Grande do Sul are morphologically and genetically diverse and distributed throughout the state. This is the first study in which isolates representing the main rice growing areas in Rio Grande do Sul were taken into consideration for the study of variability, aggressiveness, and transmission.

Studies on the Quality and Health Status of T. Aman Rice Seeds Collected From Different Sources

A laboratory experiment was carried out at the Professor GAF Seed Pathology Centre and Laboratory of Department of Seed Science and Technology, Bangladesh Agricultural University (BAU), Mymensingh during the period of July 2018 to January 2019 to determine the effect of seed sources on quality and health status in some Transplanted Aman rice varieties in Bangladesh. Among the seed sources, the moisture content was recorded from 10.40% to 13.37%. In case of purity analysis of seed pure seed ranged from 39.23g to 39.94g. Germination test of rice seeds was recorded and the percentage of total number of germination ranged from 55.75% to 93.0%, normal seedling (51.50 % to 86.50%), abnormal seedling (4.75 % to 10.75%), fresh ingeminated seed (1.75% to15.0%), hard seed (0.0 % to 11.75%), dead seed (2.75% to 21.25%), respectively. Vigour index in the seed sources ranged from (1255.80 to 2089.86). In dry inspection test apparently healthy seeds (17.74gto 28.92g), spotted seeds (9.8g to 17.34g), deformed seeds (0.36g to 5.58g), discolored seeds (0.34g to 3.40g), inert mater (0.00g to 0.38g), insect damaged seeds (0.00g to 0.24g) and chaffy seeds (0.08 g to 1.14g) were recorded(2.75% to 21.25%), respectively. In the seed health study, seven species of fungi were recorded. In the seed health test Alternaria padwickii (1.25 % to 5.50%), Aspergilus niger (0.0% to 1.75%), Bipolaris oryzae (0.0% to 1.50%), Curvularia lunata (0.00% to 1.0%), Aspergillus flavus (0.00% to 1.25%), Penicillium sp. (0.00% to 1.50%) were recorded, respectively. Most of the farmers were failed to obtain the quality and good health status. In this study, BRRI dhan34 in farmer's seed recorded the highest performance in the seed sources and the lowest performance was recorded in BRRI dhan49 in farmer's seed. Among the seed sources BR22 of BADC seed (T8) performed the best in respect of seed quality and seed health.
 Vol. 9, No. 2, August 2022: 103-116

First Report of Leaf Spot of Rice Caused by Epicoccum sorghinum in the United States.

Brown spot (Cochliobolus miyabeanus), blast (Magnaporthe oryzae) and stackburn (Alternaria padwickii) are common diseases in rice with similar leaf spot symptoms. In August 2021, a leaf spot disease, with symptoms dissimilar to these diseases, occurred on almost 100% of the leaves and sheaths of rice plants (cv. Presidio) in a 1-hectare field in Eagle Lake, Texas. Lesions started as small dark brown spots on lower leaves and sheaths. The spots enlarged to become round or oval (1.5 to 5.0 mm) spots having round ends with gray centers, dark-brown borders or rings, and slight gold halos. The spots on the sheaths were similar to those on the leaf blades, with lesion size ranging from 2 to 5 mm. Pieces of infected tissue were cut from the margin of necrotic lesions, surface disinfected with 1% NaOCl for 2 min followed by 75% ethanol for 30 s and rinsed with sterile distilled water three times. The tissues were then dried on sterilized filter paper, placed on potato dextrose agar (PDA), and incubated at 25℃ for 7 days. Two isolates (LS36 and LS37) were obtained, and their colonies were initially villose, gray at the center and pale at the margin, and then turned dark gray, with the reverse side becoming scarlet. Chlamydospores were unicellular or multicellular and massively produced in nearly spherical shape (11 to 26 × 10 to 22 μm, n=100). Pycnidia were dark and mostly spheroid (105 to 171 × 76 to 128 µm, n=100). Conidia were unicellular, hyaline, ellipsoidal, with the size of 3.6 to 5.8× 1.9 to 2.8 µm (n=100). These morphological characteristics were similar to those described for Epicoccum sorghinum (Zhou et al. 2018). The rDNA internal transcribed spacer (ITS), rRNA large subunit (LSU), and translation elongation factor 1 alpha (EF1) gene of an representative isolate (LS37) were amplified (Fell et al. 2000; Wang et al. 2014) and sequenced. The ITS sequence (OK189534) of the isolate was 96.95% identical to E. sorghinum (KX758542); the EF1 sequence (OK236518) was 98.37% identical to E. sorghinum (MN461167); and the LSU sequence (OK189535) was 99.29% identical to E. sorghinum (MK817520, MK817521, and MK817522). Rice plants (cv. Presidio) at heading were inoculated with the two isolates individually by placing a drop of conidial suspension of 1 x 106 conidia/ml or a 2-mm PDA plug of 7-day-old cultures on the wounded or unwounded leaves and sheaths (3 sites/leaf or sheath, 3 plants/treatment). The wound was made by penetrating the epidermis using a 0.5-mm-diameter pin. The plants inoculated with sterilized water or PDA-only plugs served as the controls. The treated plants were placed in a dew chamber at 26℃ for 2 days and then transferred in a greenhouse (25 to 30℃). After 5 days, typical symptoms, similar to those observed in the field, developed on all of the inoculated leaves and sheaths, with the wound inoculation inducing more rapid development of symptoms than the unwounded inoculation. No symptoms developed on the controls. The two isolates produced similar symptoms and the fungus was consistently re-isolated from the infected plants and confirmed to be E. sorghinum based on morphological characteristics. The pathogenicity test was repeated twice with similar results. To our knowledge, this is the first report of leaf spot caused by E. sorghinum in rice in the United States. This disease was first reported on rice in China in 2020 (Liu et al. 2020). This research will help identify this new disease from other leaf spot-like diseases and develop management strategies for control of this disease.

Studies of genetic correlation and path coefficient analysis between resistance to brown spot disease and yield related traits in rice (Oryza sativa L.)

Brown spot disease in rice is caused by Cochliobolus miyabeanus (Anamorph: Bipolaris oryzae (Breda de Haan) Shoemaker, 1959 (Synonyms: Helminthosporium oryzae). It causes significant losses by affecting both economic yield and grain quality. Though, it is a minor disease in most of the parts of the world but the historical famines like Krishna Godaveri Delta famine and Bengal famines and huge crop losses in a number of incidences as in Guyana and Nigeria renders it as a potential threat to rice crop and adverted the requirement of efficient, sustainable and economical strategies to cope with the pathogen. In this context, availability of resistant sources against the pathogen is a noteworthy alternative for disease management. Realising the importance of resistant sources, the present research investigation was undertaken to study association between resistance to brown spot disease and yield attributing traits in rice via correlation studies and path analysis to identify high yielding resistant lines for brown spot disease in rice. In this study disease resistance expressed in terms of AUDPC showed negative correlation with yield and yield attributing traits and direct negative effect on yield. Thus, AUDPC can be utilised as a selection parameter for developing improved cultivars with higher grain yield and lower susceptibility towards the brown spot pathogen.

Brown leaf spot of Jacaranda mimosifolia Caused by Colletotrichum karstii in Sichuan, China.

Jacaranda mimosifolia D. Don is widely cultivated in southwest China (Yunnan, Sichuan, and other regions). It is widely applied in papermaking, medicine, environmental monitoring, timber, urban and rural afforestation, and soil and water conservation. In October 2020, a new brown leaf spot disease of J. mimosifolia was discovered in Xichang City (27°49' to 27°56'N, 102°16' to 102°11'E), with approximately 66.23% disease incidence. Firstly, the typical symptoms showed deep yellow necrotic lesions in the center or on the margin of the leaves. Gradually, the necrotic lesions expanded and developed into brown spots. Under humid conditions, the edges of necrotic lesions turned dark brown progressively. Finally, the leaves withered, died, and fell off. Infected tissues from ten samples were cut into small pieces of 2.5 × 2.5 mm. The surfaces of infected tissues were sterilized for 30 s in 3% sodium hypochlorite, 60 s in 75% ethanol, and rinsed three times in sterile water. They were then blot-dried with autoclaved paper towels and cultured on potato dextrose agar (PDA) at 25℃ for 3 to 8 days. After culturing for 8 days at 25℃ and 12 h/12 h light/dark on PDA, the colony diameter reached 78.2 to 82.7 mm. The colonies were light orange, turned pale pink with light orange beneath. The conidia were single-celled, aseptate, cylindrical, smooth-walled, straight, hyaline with both ends bluntly rounded, measuring 12.3 to 16.8 × 4.3 to 5.6 μm (n = 100; average=14.5 × 5.1μm). These morphological characteristics were consistent with the description of C. karstii (Zhao et al. 2021). For molecular identification, the genomic DNA of the representative isolate JM202010 was extracted using a fungal genomic DNA extraction kit (Solarbio, Beijing). The internal transcribed spacer (ITS) [ITS1/ITS4 (White et al., 1990)], calmodulin (CAL) [CL1C/CL2C (Weir et al., 2012)], actin (ACT) [ACT512F/ACT-783R (Carbone & Kohn, 1999)], chitin synthase (CHS-1) [CHS-79F/CHS-345R (Carbone & Kohn, 1999)], β-tubulin (TUB2) [BT2A/BT2B (O'Donnell et al., 1997)], and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) [GDF/GDR (Templeton et al. 1992)] were amplified. Sequences were deposited in GenBank (ITS: OL454787, CAL: OL518966, ACT: OL518967, CHS-1: OL518968, TUB2: OL518969, and GAPDH: OL518970). BLAST results indicated that the ITS, CAL, ACT, CHS-1, TUB2 and GAPDH sequences showed >99% identity with Colletotrichum karstii sequences at NCBI (GenBank MW494453.1, MW495036.1, MG387951.1, MW495038.1, MW495042.1, and MG602034.1). The conidial suspension (1 × 106 conidia/ml) was sprayed on the leaves of 4-year-old J. mimosifolia plants (10 plants) and inoculated for pathogenicity test. Fifteen leaves of each plant (10 pots in total) were inoculated with spore suspensions on both sides of the leaves. An equal number of control leaves was sprayed with sterilized distilled water as a control. Finally, all pots were kept in a greenhouse at 26°C under a 16 h/8 h photoperiod and 60 to 68% relative humidity. The inoculated plants showed symptoms similar to those of the original diseased plants, but the controls remained asymptomatic. Colletotrichum karstii was re-isolated from the infected leaves and identified by both morphological characteristics and DNA sequence analysis. The pathogenicity test was repeated thrice, which showed similar results, confirming Koch's postulates. To our knowledge, this is the first report of brown leaf spot on J. mimosifolia caused by C. karstii in China. C. karstii was previously reported as the causal agent of anthracnose on Fatsia japonica (Xu et al. 2020) and Nandina domestica (Li et al. 2017) in China. This finding provides an important basis for further research on the control of this disease.

The regulation of Alternaria alternata resistance by LRR-RK4 through ERF109, defensin19 and phytoalexin scopoletin in Nicotiana attenuata

Leucine-rich repeat receptor-like kinases (LRR-RKs), belonging to the largest subfamily of transmembrane receptor-like kinases in plants, are proposed to be involved in pathogen resistance. However, it is currently unknown whether LRR-RKs regulate Nicotiana attenuata resistance to Alternaria alternata, a notorious fungal pathogen causing tobacco brown disease. During transcriptome analysis, we identified a highly induced receptor kinase (NaLRR-RK4) in N. attenuata leaves after A. alternata inoculation. We speculated that this NaLRR-RK4 might be the resistance gene of tobacco to brown spot disease, and if so, what is its function and mechanism of action? Silencing of NaLRR-RK4 via virus-induced gene silencing (VIGS) lead to plants highly susceptible to A. alternata, and this result was further confirmed by two stable transformation lines (NaLRR-RK4-RNAi lines) generated by RNA interference technology. The susceptible of NaLRR-RK4-RNAi lines to A. alternata was associated with reduced levels of phytoalexin scopoletin and its key synthesis gene NaF6′H1. Further transcriptome analysis of leaves of WT and NaLRR-RK4-RNAi line after A. alternata inoculation revealed that NaLRR-RK4 regulated NaERF109 and NaDEF19. Silencing NaERF109 or NaDEF19 by VIGS lead to plants more susceptible to A.alternata, demonstrating their role in pathogen resistance. Interestingly, A.alternata-induced expression of NaF6′H1 and NaDEF19 were dramatically reduced in NaERF109-silenced VIGS plants. Taken all together, we identified LRR-RK4 as the first Leucine-rich repeat receptor-like kinases involved in A.alternata resistance in tobacco species, by regulating NaERF109, and subsequently NaDEF19 and NaF6′H1.

First Report of Bipolaris oryzae on Typha latifolia and the Pathogenicity of Its Isolates on Different Rice Varieties

AbstractRice (Oryza sativaL.) is the staple food of more than half of the world population. However, its production is facing several biotic constraints. Among serious biotic factors that harm rice crops, the Helminthosporium disease has severe adverse impacts on rice yield, generating heavy losses of up to 90%. FourBipolaris oryzaeisolates were recovered for the first time from leaf lesions in the weed speciesTypha latifolia, and then subjected to pathogenicity tests on several rice varieties. The results indicated that Moroccan isolates ofB. oryzaealtered the leaf surface of five rice varieties tested. Among four isolates, Hor4 was the most pathogenic, showing high aggressiveness on the Cererrer and Elio varieties, with disease severity of 92.59%, followed by the Hor1, Hor2, and Hor3 isolates. The Arpa variety showed higher resistance to the Hor1 isolate, with a severity index of 35.18%. Through mycelial cutting or conidial suspension,B. oryzaeisolated fromT. latifoliawas able to produce conidia on the leaves of this weed species.

An IoT-based intelligent farming using CNN for early disease detection in rice paddy

Huge economic losses occur in the agricultural industry due to bacterial, viral or fungal infections in crops due to which farmers incur 15–20% losses on their profits annually. India is the second largest producer of rice and a leading exporter of the same in the global market. Thus, early disease detection in crops is essential. Implementing Smart Farming is a burning area of research in order to prevent further damage to crops. The widespread development of Deep Learning makes it possible to achieve the goal of disease detection in crops. In this paper we have proposed an intelligent model based on Smart farming integrating Machine Learning with the IoT network. The novelty of this project is early detection of Brown spot disease in rice paddy for the very first time using Convolutional Neural Networks. Deep Learning uses Neural Networks to implement Artificial Intelligence. This project makes use of Image recognition and pre-processing based on real time data. Data pre-processing and feature extraction has been done using a self-designed image-processing tool. Tensor flow and Keras framework has been implemented on both training and testing data collected manually from rice fields. The proposed model achieved an accuracy of 97.701% posing the ability to minimise the losses overall to the national and global productions. Further an app has been designed for the farmers for the same.

First Report of Brown Leaf Spot of Rice Caused by Curvularia hawaiiensis in the United States.

Multiple diseases, including brown spot (Cochliobolus miyabeanus), leaf spot (Epicoccum sorghimum), and blast (Magnaporthe oryzae), can cause spot-like symptoms on the leaves of rice. In July 2021, a disease showing symptoms like brown spot was observed in an 8-hectare field of rice, with disease incidence of >30%, in Beaumont, Texas. Lesions started as small pinhead-size blackish spots on leaf tips or from the edges of leaf blades. The spots enlarged to become irregular (most) or oval brown spots with a slight chlorotic halo. Diseased leaves were collected, washed in running tap water and cut into small pieces. Pieces of the tissue were surface sterilized with 1%NaOCl for 2 min followed by 75% ethanol for 30 s and then washed in sterile distilled water three times with each time lasting for 1 min. The disinfected tissue pieces were air dried, placed on potato dextrose agar (PDA) medium and incubated at 25℃. Initially fungal colonies were hairy in texture with light dark brown center and whitish edge and dark brown pigmentation at the reverse side. Mature colonies turned to black in the center and dark brown toward the edge, with black at the reverse side after 2 or more weeks of incubation. Conidia were oval to narrowly oblong, rounded at the ends, with 2 to 6 distoseptate, and 15 to 35 × 6 to 10 µm in size. These morphological characteristics were similar to those described for Curvularia hawaiiensis (Aslam et al. 2019; Ellis 1971; Kusai et al. 2015). For molecular identification, DNA was extracted and the two different rRNA regions internal transcribed spacer (ITS) and large subunit (LSU), and the two genes RNA Polymerase II (RPB1) and translation elongation factor 1 alpha (EF1) of the fungus were amplified using the primers of ITS1/ITS4 (Wang et al. 2014), D1/D2 domain region of LSU (Fell et al. 200), and RPB1 and EF1 (Wang et al. 2014), respectively, and sequenced. The ITS sequence (OK397200) was 98.27% identical to C. hawaiiensis (KP131943); the EF1 sequence (OK492159) was 99.78% identical to C. hawaiiensis (KC503942); the LSU sequence (OK397295) was 98.96% identical to multiple C. hawaiiensis (MN055715, MH160813, MH875853, etc.); the RPB1 sequence (OK492160) was 97.41% identical to C. hawaiiensis (JN992363). To evaluate pathogenicity, three rice plants (cv. Presidio) at the 3-leaf stage were spray inoculated with a conidial suspension of 1 x 106 conidia/ml. Another set of three plants that were sprayed with sterilized distilled water served as the controls. Treated plants were maintained in a greenhouse with temperature ranging from 25 to 30℃. After 2 weeks, typical symptoms, like those observed in the field, developed on the inoculated plants while no symptoms developed on the control plants. The same fungus was consistently re-isolated from the diseased plants. The pathogenicity test was conducted three times with similar results. To our knowledge, this is the first report of brown leaf spot caused by C. hawaiiensis in rice in the United States. Curvularia species are frequently associated with rice grain and cause blackish discoloration symptoms on grain kernels. Recently, however, C. hawaiiensis has also been reported to cause brown leaf spot in Malaysia (Kusai et al. 2015) and Pakistan (Aslam et al. 2019). This research will help identify this disease from other leaf spot-like diseases and develop effective management strategies.

Isolation and Characterization of Phylloplane Associated Bacteria and its in vitro Antagonistic Activity against Bipolaris oryzae

Background: Rice brown leaf spot is a major fungal disease caused by Bipolaris oryzae. Biological management of brown leaf spot can be achieved by bacteria associated with phyllosphere region of rice crop. Methods: Antagonistic bacteria were isolated from phylloplane of the rice crop using dilution method. Phylloplane bacteria were morphologically and biochemically characterized. Antagonistic activity of bacteria was tested against Bipolaris oryzae using Dual culture technique. Result: Among the isolates, PB1, PB3, PB6, PB7, PB9 were identified as Pseudomonas fluorescens and PB2, PB4, PB5, PB8, PB10 were identified as Bacillus subtilis by morphological and biochemical characterization. Phylloplane bacteria PB5 showed the maximum inhibition of B. oryzae mycelium (70.00%) followed by PB1 (67.77%). Minimum inhibition of mycelial growth (31.11) was found in PB9.