Plant Diseases Research Articles

Integrated Farming System: A Sustainable Approach towards Modern Agriculture

Integrated farming system (IFS) is an integrated approach towards sustainable agriculture in this modern era. It is the integration of various agricultural enterprises or practices such as crops, livestock, Poultry, fish, agroforestry, horticulture, vermicomposting, Mushroom cultivation and beekeeping, in a single farming system. IFS is considered as the efficient tool for enhancing the profitability of farming systems especially for small and marginal farmers. The purposes or objectives of the IFS are: It enhances the productivity per unit area; Agriculture waste management; Soil health management and soil conservation; Generation of diversified income round the year; less use of chemicals; Increasing the yield of all integrated components and food security. IFS include various components or enterprises. These enterprises not only increase the farmer’s income but also increase employment generation of a family. The juadicious integration of these agricultural enterprises improves the socio-economic status of the farmers. The crop production component of IFS includes the cultivation of various economically important crops such as cereals, pulses, vegetables, fruits, and herbs. To maintain the soil health & fertility and management of Plant diseases and pests, various cultural methods such as intercropping, crop rotation, soil solarisation, organic amendment, Mulching, etc. Mushroom cultivation is also one of the important enterprises in IFS that contribute towards increase in farmer’s income. Another important component of IFS includes the rearing of different types of livsestock such as cows, goats, sheep, Duckery, Piggery, Apiary, and poultry. Livestock provides a source of income and produces manure, which can be used as fertilizer for crops. Biogas is another enterprise which is the use of agricultural and animal waste to produce energy. This Bioenergy helps to reduce the dependence on fossil fuels and provide a source of income for farmers. There are various models that can be used in IFS like Horticulture+ Piggery+ Fisheries+ Plantation crops and Agriculture+ Horticulture+ Poultry+ Fishery+ Mushroom. Hence, Integrated Farming System is a promising approach for increasing productivity, increasing farmer’s income by utilizing the agricultural and animal waste, recycling farm by-products and efficient utilization of available resources.

Use and impact of endophytic entomopathogenic fungi: Their potential in the context of agricultural sustainability

The use of entomopathogenic fungi (EF) as endophytes is an environmentally friendly alternative for sustainable food production, given that the current paradigm in crop protection is based on the use of organosynthetic pesticides, with more than two million tons per year worldwide. For these reasons, EF have the ability to live within plant tissues as endophytes acting as biopesticides. Under this scenario, this review analyzes and discusses the global status of the endophytic entomopathogenic fungi (EEF), their potential in plant protection against plant diseases and insect pests and as plant growth promoters. Successes and failures, and prospects for field application are examined. More than 7000 studies on EEF have been published, with important success cases. However, it is necessary to understand that the agricultural production is based on the use of external inputs, mainly pesticides. While progressive changes occur, it is fundamental to investigate the effect of these substances on the efficacy and persistence of EEF, without neglecting that the lack of knowledge of the effect of biotic and abiotic factors on EEF is an important cause of failures. Future studies should be focused on clarifying aspects such as: application strategies, endophytic persistence and transmission routes to improve the sustainability of agricultural production.

Application and antagonistic mechanisms of atoxigenic Aspergillus strains for the management of fungal plant diseases.

This review covers, for the first time, all methods based on the use of Aspergillus strains as biocontrol agents for the management of plant diseases caused by fungi and oomycetes. Atoxigenic Aspergillus strains have been screened in a variety of hosts, such as peanuts, maize kernels, and legumes, during the preharvest and postharvest stages. These strains have been screened against a wide range of pathogens, such as Fusarium, Phytophthora, and Pythium species, suggesting a broad applicability spectrum. The highest efficacies were generally observed when using non-toxigenic Aspergillus strains for the management of mycotoxin-producing Aspergillus strains. The modes of action included the synthesis of antifungal metabolites, such as kojic acid and volatile organic compounds (VOCs), secretion of hydrolytic enzymes, competition for space and nutrients, and induction of disease resistance. Aspergillus strains degraded Sclerotinia sclerotiorum sclerotia, showing high control efficacy against this pathogen. Collectively, although two Aspergillus strains have been commercialized for aflatoxin degradation, a new application of Aspergillus strains is emerging and needs to be optimized.

First Report of Klebsiella variicola Causing Tomato Pith Necrosis in Yunnan Province, China.

Tomato (Solanum lycopersicum L.) is a key vegetable crop in China. In August 2023, an outbreak of bacterial pith necrosis in tomato occurred in Lufeng County, Yunnan Province, China, affecting over 40% of the tomato plants in a greenhouse. The stems of infected plants developed a waterlogged soft rot and the disease progressed, the lower leaves and lateral branches of infected plants gradually wilted and died. A longitudinal cut of the stem revealed hollow pith with brown vascular tissue. To isolate the pathogen, the plant surface was disinfested with 75% ethanol. Then, a piece of infected tissue from the base of the stem was excised and immersed in sterile water for 2 min. A small amount of liquid was streaked onto TTC (2,3,5-triphenyltetrazolium chloride) agar medium using an inoculation loop, and plates were incubated at 28℃ for 24 h. Colonies on the TTC plate were white, indicating that the pathogen was not Ralstonia solanacearum. Colonies grown on LB (Luria-Bertani) agar medium were randomly selected and subjected to preliminary pathogenicity tests. Based on the results, a colony named Kv4 was selected and purified through six subcultures in LB agar medium. Biochemical tests showed the strain utilized D-sorbitol, raffinose and citrate but not adonitol, and was positive for methyl red, D-glucose (acid), urea hydrolysis, lysine decarboxylase, and motility, and negative for phenylalanine deaminase, H2S production, indole production, and ornithine decarboxylase. These characteristics align with Klebsiella species (Garrity et al. 2007). To determine the species of strain Kv4, partial sequences of the 16S rDNA, phoE, leuS, and rpoB genes were amplified (Barrios-Camacho et al. 2019) and sequenced. Through BLASTn analysis, strain Kv4 sequences of 16S rDNA (OR888750) had 99.47% identity (1488/1496 bp), phoE (OR899599) had 98.69% (605/613 bp) identity, leuS (OR899598) had 99.07% identity (959/968 bp), and rpoB (OR899597) had 97.69% (633/648 bp) identity with Klebsiella variicola strain FF0907. Using the ClustalW algorithm in MEGA11 for nucleotide sequence alignment, phylogenetic trees were constructed with 16S and phoE, leuS, and rpoB via the neighbor-joining method, confirming strain Kv4 as K. variicola. To test pathogenicity, the roots of 25 'Moneymaker' tomato plants with four to five true leaves were wounded, then each plant inoculated with a 15 mL bacterial suspension (OD600=0.6) of strain Kv4, while the control plants received sterile water. Plants were incubated at 28℃ with a 16 h photoperiod. Experiments were done twice. At 15 days after inoculation (DAI), all plants inoculated with Kv4 showed yellowing, unevenly distributed small black necrotic spots on the leaf surface, and purple-brown soft rot at the stem base. By 18 DAI, there was a gradual transformation of the stem bases from green to purplish brown. At 21 DAI, 60% of the inoculated plants displayed brownish soft rot at the stem base. In contrast, the control plants remained symptom-free. The pathogen was re-isolated from the stem and identified as K. variicola via sequence analysis of 16S, phoE, leuS, and rpoB. In recent years, several new bacterial pith necrosis diseases were reported in tomato (Guo et al. 2023; Ivić et al. 2023). This is the first study documenting K. variicola causing bacterial pith necrosis in tomato. Once considered a benign plant endophyte, Sun et al. (2023) reported K. variicola causing banana sheath rot in Guangdong and Guangxi Provinces, China. Malik et al. (2023) reported that K. variicola caused leaf streak in sorghum in India. This report of bacterial pith necrosis in tomato caused by K. variicola strain Kv4 underscores the escalating threat posed by emerging pathogens to agricultural production. The emergence of K. variicola as a tomato pathogen complicates plant disease management strategies.

Plasmodiophora brassicae effector PbPE23 induces necrotic responses in both host and non-host plants.

Plasmodiophora brassicae is an obligate biotroph that causes clubroot disease in cruciferous plants, including canola and Arabidopsis. In contrast to most known bacterial, oomycete and fungal pathogens that colonize at the host apoplastic space, the protist P. brassicae establishes an intracellular colonization within various types of root cells and secretes a plethora of effector proteins to distinct cellular compartments favourable for survival and growth of the pathogen during pathogenesis. Identification and functional characterization of P. brassicae effectors has been hampered by the limited understanding of this unique pathosystem. Here, we report a P. brassicae effector, PbPE23, containing a Ser/Thr kinase domain, that induces necrosis after heterologous expression by leaf infiltration in both host and non-host plants. While PbPE23 is an active kinase, the kinase activity itself is not required for triggering the necrosis in plants. PbPE23 shows a nucleocytoplasmic localization in Nicotiana benthamiana and its N-terminal 25TPdPAQKQ32 sequence, resembling the contiguous hydrophilic TPAP motif and Q-rich region in many Nep1-like proteins (NLPs) from plant-associated microbes, is required for the induction of necrosis. Further, transcript profiling of PbPE23 reveals its high expression at the transition stages from primary to secondary infection, suggesting its potential involvement in the development of clubroot disease.

Assessing the impact of turnip yellows virus infection and drought on canola performance: implications under a climate change scenario

IntroductionCanola (Brassica napus L.) is one of the most important crops worldwide. Turnip yellows virus (TuYV), transmitted by aphids, is one of the most damaging viruses affecting canola crops and is challenging to control. With the prediction of more intense and prolonged drought events due to future climate change, an additional factor may extensively impact the epidemiology of plant diseases. This study aimed to understand the impact of drought on canola plants infected with TuYV and to explore the relationship between virus infection and drought.MethodsTwo glasshouse experiments were conducted: 1. Competition: Four plants (two infected, two non-infected) were grown in the same pot. 2. No Competition: One plant was grown per pot. In both experiments, infected and non-infected canola plants were exposed to well-watered conditions, water stress (simulated drought), and terminal drought. Various plant traits were recorded, including biomass, leaf area, height, number of leaves, chlorophyll content, water use efficiency, and virus symptom expression.ResultsBoth virus infection and water stress reduced dry biomass, leaf area, and height. Virus infection alone reduced canola biomass by up to 49% compared to non-infected, well-watered controls. Under water stress or terminal drought, the biomass of TuYV-infected plants was further reduced by up to 71% and 65%, respectively. Virus infection also reduced the number of leaves, although water treatment alone did not. Chlorophyll content was higher in water-stressed and terminal drought plants compared to well-watered ones, while virus infection reduced chlorophyll content. The impact of drought and virus infection was more pronounced when plants were under competition.DiscussionGiven the expected increase in prolonged and frequent droughts in many canola-growing regions due to climate change, a significant detrimental effect on canola production due to the combined influence of drought and TuYV is anticipated. This study underscores the need for developing mitigation strategies to protect canola production in a changing climate.

Complete Genome of Achromobacter xylosoxidans, a Nitrogen-Fixing Bacteria from the Rhizosphere of Cowpea (Vigna unguiculata [L.] Walp) Tolerant to Cucumber Mosaic Virus Infection.

Achromobacter xylosoxidans is one of the nitrogen-fixing bacteria associated with cowpea rhizosphere across Africa. Although its role in improving soil fertility and inducing systemic resistance in plants against pathogens has been documented, there is limited information on its complete genomic characteristics from cowpea roots. Here, we report the complete genome sequence of A. xylosoxidans strain DDA01 isolated from the topsoil of a field where cowpea plants tolerant to cucumber mosaic virus (CMV) were grown in Ibadan, Nigeria. The genome of DDA01 was sequenced via Illumina MiSeq and contained 6,930,067 nucleotides with 67.55% G + C content, 73 RNAs, 59 tRNAs, and 6421 protein-coding genes, including those associated with nitrogen fixation, phosphate solubilization, Indole3-acetic acid production, and siderophore activity. Eleven genetic clusters for secondary metabolites, including alcaligin, were identified. The potential of DDA01 as a plant growth-promoting bacteria with genetic capabilities to enhance soil fertility for resilience against CMV infection in cowpea is discussed. To our knowledge, this is the first complete genome of diazotrophic bacteria obtained from cowpea rhizosphere in sub-Saharan Africa, with potential implications for improved soil fertility, plant disease resistance, and food security.

Detection and Classification of Cotton Plant Disease Using Deep Learning Network

Detection and Classification of Cotton Plant Disease Using Deep Learning Network

Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition

Deep learning models have demonstrated great promise in plant disease identification. However, existing approaches often face challenges when dealing with unseen crop-disease pairs, limiting their practicality in real-world settings. This research addresses the gap between known and unknown (unseen) plant disease identification. Our study pioneers the exploration of the zero-shot setting within this domain, offering a new perspective to conceptualizing plant disease identification. Specifically, we introduce the novel Cross Learning Vision Transformer (CL-ViT) model, incorporating self-supervised learning, in contrast to the previous state-of-the-art, FF-ViT, which emphasizes conceptual feature disentanglement with a synthetic feature generation framework. Through comprehensive analyses, we demonstrate that our novel model outperforms state-of-the-art models in both accuracy performance and visualization analysis. This study establishes a new benchmark and marks a significant advancement in the field of plant disease identification, paving the way for more robust and efficient plant disease identification systems. The code is available at https://github.com/abelchai/Cross-Learning-Vision-Transformer-CL-ViT.

The antagonistic activity of Streptomyces spiroverticillatus (No. HS1) against of poplar canker pathogen Botryosphaeria dothidea

BackgroundPoplar canker caused by Botryosphaeria dothidea is one of the most severe plant disease of poplars worldwide. In our study, we aimed to investigate the modes of antagonism by fermentation broth supernatant (FBS) of Streptomyces spiroverticillatus HS1 against B. dothidea.ResultsIn vitro, the strain and FBS of S. spiroverticillatus HS1 significantly inhibited mycelial growth and biomass accumulation, and also disrupted the mycelium morphology of B. dothidea. On the 3rd day after treatment, the inhibition rates of colony growth and dry weight were 80.72% and 52.53%, respectively. In addition, FBS treatment damaged the plasma membrane of B. dothidea based on increased electrical conductivity in the culture medium, and malondialdehyde content of B. dothidea mycelia. Notably, the analysis of key enzymes in glycolysis pathway showed that the activity of hexokinase (HK), phosphofructokinase (PFK), and pyruvate kinase (PK), Ca2+Mg2+-ATPase were significantly increased after FBS treatment. But the glucose contents were significantly reduced, and pyruvate contents were significantly increased in B. dothidea after treatment with FBS.ConclusionsThe inhibitory mechanism of S. spiroverticillatus HS1 against B. dothidea was a complex process, which was associated with multiple levels of mycelial growth, cell membrane structure, material and energy metabolism. The FBS of S. spiroverticillatus HS1 could provide an alternative approach to biological control strategies against B. dothidea.

Spatio-temporal dynamics of the vector-born plant disease model

Spatio-temporal dynamics of the vector-born plant disease model

Barley disease recognition using deep neural networks

Plant disease negatively impacts food production and quality. It is crucial to detect and recognise plant diseases correctly. Traditional approaches do not offer a rapid and comprehensive management system for detecting plant diseases. Deep learning techniques (DL) have achieved encouraging results in discriminating patterns and anomalies in visual samples. This ability provides an effective method to diagnose any plant disease symptoms automatically. However, one of the limitations of recent studies is that in-field disease detection is underexplored, so developing a model that performs well for in-field samples is necessary. The objective of this study is to develop and investigate DL techniques for in-field disease detection of barley (Hordeum vulgare L.), one of the main crops in Australia, given visual samples captured at barley trials using a consumer-grade RGB camera. Consequently, A dataset was captured from test-bed trials across multiple paddocks infected with three diseases: net form net blotch (NFNB), spot form net blotch (SFNB), and scald, in various weather conditions. The collected data, 312 images (6000 × 4000 pixels), are divided into patches of 448 × 448 pixels, which are manually annotated into four classes: no-disease, scald, NFNB and SFNB. Finally, the data was augmented using random rotation and flip to increase the dataset size. The generated barley disease dataset is then applied to several well-known pre-trained DL networks such as DenseNet, ResNet, InceptionV3, Xception, and MobileNet as the network backbone. Given limited data, these methods can be trained to detect anomalies in visual samples. The results show that MobileNet, Xception, and InceptionV3 performed well in barley disease detection. On the other hand, ResNet showed poor classification ability. Moreover, Augmenting the data improves the performance of DL networks, particularly for underperforming backbones like ResNet, and mitigates the limited data access for these data-intensive networks. The augmentation step improved MobileNet performance by approximately 6 %. MobileNet achieved the highest accuracy of 98.63 % (the average of the three diseases) in binary classification and an accuracy of 93.50 % in multi-class classification. Even though classifying SFNB and NFNB is challenging in the early stages, MobileNet achieved the minimum misclassification rate among the two diseases. The results show the efficiency of this model in diagnosing barley diseases using complex data collected from the field environment. In addition, the model is lighter and comprises fewer trainable parameters. Consequently, MobileNet is suitable for small training datasets, reducing data acquisition costs.

Synergistic Effect of Two Peptaibols from Biocontrol Fungus Trichoderma longibrachiatum Strain 40418 on CO-Induced Plant Resistance.

Trichoderma longibrachiatum is a filamentous fungus used as a biological control agent against different plant diseases. The multifunctional secondary metabolites synthesized by Trichoderma, called peptaibols, have emerged as key elicitors in plant innate immunity. This study obtained a high-quality genome sequence for the T. longibrachiatum strain 40418 and identified two peptaibol biosynthetic gene clusters using knockout techniques. The two gene cluster products were confirmed as trilongin AIV a (11-residue) and trilongin BI (20-residue) using liquid chromatography coupled with tandem mass spectrometry. Further investigations revealed that these peptaibols induce plant resistance to Pseudomonas syringae pv tomato (Pst) DC3000 infection while triggering plant immunity and cell death. Notably, the two peptaibols exhibit synergistic effects in plant-microbe signaling interactions, with trilongin BI having a predominant role. Moreover, the induction of tomato resistance against Meloidogyne incognita showed similarly promising results.

Development of a Web-based Tomato Plant Disease Detection and Diagnosis System using Transfer Learning Techniques

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

Evaluation of Bacillus velezensis F9 for Cucumber Growth Promotion and Suppression of Fusarium wilt Disease.

Cucumber wilt, caused by Fusarium oxysporum f. sp. cucumerinum (FOC), is a soilborne disease that poses a significant threat to cucumber production, resulting in substantial yield losses. This study aimed to evaluate the biocontrol and growth-promoting effects of Bacillus velezensis, a highly active bacterial strain. In vitro assays revealed that B. velezensis F9 exhibited broad-spectrum antifungal activity against eight plant pathogenic fungi, with inhibition ratio ranging from 62.66% to 88.18%. Additionally, the strain displayed the ability to produce IAA (5.97 ± 1.75 µg/mL), fix nitrogen, produce siderophores, and form biofilms. In vitro growth promotion assays demonstrated that different concentrations of B. velezensis F9 significantly promoted cucumber seedling growth. Furthermore, two pot experiments revealed that the strain exhibited biocontrol efficacy against cucumber wilt, with disease control rates ranging from 42.86% to 67.78%. Notably, the strain significantly increased the plant height, fresh weight, and dry weight, with increases ranging from 20.67% to 60.04%, 40.27% to 75.51%, and 22.07% to 52.54%, respectively. Two field trials confirmed the efficacy of B. velezensis F9 in controlling cucumber wilt, with disease control rates of 44.95% and 33.99%, respectively. The strain effectively alleviated the dwarfing and wilting symptoms caused by the pathogen. Compared with the FOC treatment, the F9 + FOC treatment significantly increased the plant height, fresh weight, and dry weight, with increases of 43.85% and 56.28%, 49.49% and 23.70%, and 36.25% and 73.63%, respectively. Enzyme activity assays indicated that inoculation significantly increased SOD activity in cucumber leaves and neutral phosphatase, sucrase, and urease activity in rhizosphere soil. Correlation analysis revealed a negative correlation between the disease index and plant height, fresh weight, dry weight, and peroxidase activity, with correlation coefficients of -0.53, -0.60, -0.38, and -0.45, respectively. These findings suggest that plant height, fresh weight, and dry weight are significantly negatively correlated with the cucumber disease index, highlighting their importance as indicators for evaluating the biocontrol efficacy of B. velezensis F9. In conclusion, B. velezensis F9 is a highly effective plant growth-promoting rhizobacterium with excellent biocontrol potential, showcasing promising applications in agricultural production.

Resolving the metabolism of monolignols and other lignin-related aromatic compounds in Xanthomonas citri

Lignin, a major plant cell wall component, has an important role in plant-defense mechanisms against pathogens and is a promising renewable carbon source to produce bio-based chemicals. However, our understanding of microbial metabolism is incomplete regarding certain lignin-related compounds like p-coumaryl and sinapyl alcohols. Here, we reveal peripheral pathways for the catabolism of the three main lignin precursors (p-coumaryl, coniferyl, and sinapyl alcohols) in the plant pathogen Xanthomonas citri. Our study demonstrates all the necessary enzymatic steps for funneling these monolignols into the tricarboxylic acid cycle, concurrently uncovering aryl aldehyde reductases that likely protect the pathogen from aldehydes toxicity. It also shows that lignin-related aromatic compounds activate transcriptional responses related to chemotaxis and flagellar-dependent motility, which might play an important role during plant infection. Together our findings provide foundational knowledge to support biotechnological advances for both plant diseases treatments and conversion of lignin-derived compounds into bio-based chemicals.

Effect of nozzles on disease control with spraying fungicides in soybean crops

ABSTRACT The productivity of the soybean crop (Glycine max) can be limited by several factors, including diseases. In the integrated management of diseases, one of the strategies used is chemical control. The aim of this experiment was to evaluate the influence of spray nozzle types on the chemical control of fungal diseases in soybean plants. The experimental design was randomised blocks, with six treatments and four replications. The treatments consisted of control (without fungicide application on soybean plants) and application of fungicides with different spray nozzles: hollow cone jet, double flat jet, extended range flat jet, wide angle flat jet and air induction flat jet. The sprayer used was the backpack pressurised by carbon dioxide CO2. The experiment was installed in the Campos Gerais region (Paraná, Brazil); during three cropping seasons. The evaluated variables were incidence and severity, spray quality (covered area, droplet density, and relative amplitude) and yield components. It was concluded that the chemical control of fungal diseases in soybean plants reduced the proliferation of pathogens and losses in crop yield. There were significant changes in spraying quality in all analysed variables. There were no differences among the spray nozzle types regarding the analysis of incidence, severity and soybean yield components.

Novel members of the order Picornavirales identified in freshwater from Guarapiranga reservoir in São Paulo

The global challenge of water resource availability is exacerbated by anthropogenic influences that promote the emergence of pollutants. Among these pollutants are microbiological agents, including viruses, which are ubiquitous in the biosphere and play a pivotal role in both ecological balance and the occurrence of diseases in animals and plants. Consequently, monitoring viruses in water sources becomes indispensable for the establishment of effective prevention, promotion, and control strategies. Within this context, the study focuses on the identification of novel viruses belonging to the Picornavirales order in freshwater from the Guarapiranga Reservoir in the state of São Paulo, Brazil. The samples were subjected to viral metagenomics. Our analysis led to the characterization of four distinct sequences (GinkV-05, AquaV_10, MarV_14, and MarV_64), which exhibited significant divergence compared to other members of the Picornavirales order. This remarkable diversity prompted the identification of a potential new genus within the Marnaviridae family, tentatively named Ginkgonavirus. Additionally, we characterized four sequences in a very distinct clade and propose the recognition of a novel family (named Aquaviridae) within the Picornavirales order. Our findings contribute valuable insights into the previously uncharted diversity of Picornavirales present in water sources, shedding light on an important facet of viral ecology and evolution in aquatic environments.

CaZingipain2 Acts Positively in Pepper (Capsicum annuum L.) Immunity against R. solanacearum.

Bacterial wilt caused by Ralstonia solanacearum is one of the most important diseases in solanaceous plants, including peppers. It generally tends to be more serious under warm-temperature and moist (WM) conditions than at moist room-temperature (RM) conditions. Although immunity mechanisms at room temperature have been intensively studied, the mechanisms underlying WM conditions remain poorly understood. Herein, the pepper cysteine protease CaZingipain2 was expressed and functionally characterized in pepper immunity against R. solanacearum at WM conditions and at room temperature. The results showed that CaZingipain2 localized to the nucleus and was upregulated at the transcript level in pepper plants upon R. solanacearum infection under WM conditions (RSWM). Virus-induced gene silencing of CaZingipain2 significantly increased the susceptibility of pepper plants to RSWM, and was coupled with the downregulation of CaPRP1 and CaMgst3, which are specifically related to pepper immunity against RSWM, according to our previous studies, while its overexpression significantly reduced the susceptibility of N. benethamiana plants to RSWM compared to that of wild-type plants. In addition, our data showed that CaZingipain2 also acts positively in pepper immunity against R. solanacearum infection at room temperature by upregulating the SA- and JA-responsive PR genes, including CaNPR1 and CaDEF1. All these results indicate that CaZingipain2 improves pepper immunity against R. solanacearum under WM conditions and at room temperature by regulating different PR genes.

CNN driven nutrient deficiency detection in plants using real-world leaf images

In agriculture, our farmers currently lack efficient methods to classify and identify plant nutrient deficiencies or diseases, leading to ineffective remedial measures. However, the potential for early detection of plant nutrient deficiencies using colour gradients or leaf patterns, coupled with the advancements in Image Processing, Deep Learning (DL), and Artificial Intelligence (AI), offers a promising future. This paper presents deep learning-based methods to segregate nutrient deficiencies using leaf images, a potential game-changer for the industry. Our research has the potential to revolutionize plant nutrition management, inspiring a new wave of efficient and effective practices in agriculture. We analyzed the International Plant Nutrition Institute (IPNI) dataset and applied an image augmentation mechanism to enhance the training process with the available dataset. By utilizing Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and learning algorithms, we minimised loss and improved accuracy. The model, incorporating over 1.5 million parameters, has shown promising results in predicting deficiencies accurately. We propose an SGD-based training mechanism to balance localization and classification tasks, a step towards a more efficient and effective approach to plant nutrition management.