Plant Diseases Research Articles

PEMENUHAN GIZI DAN PENINGKATAN PENDAPATAN MASYARAKAT DENGAN BUDIDAYA SAYURAN DI LAHAN PEKARANGAN

Fulfilling people's food needs faces many obstacles, especially due to changes in environmental conditions, such as climate change, land conversion, and the increasing number of cases of pest attacks and plant diseases which cause a decrease in crop yields. The solution offered is to use yard land for cultivating plants. Apart from being able to be used for daily consumption, the harvest from the yard can also be sold as a side business for family members. The community service program aims to assist the target community in increasing their knowledge and skills in optimally utilizing yard land for cultivating vegetable crops. The method used is community empowerment with activities: counseling, training and participatory mentoring of target community groups. The results of the implementation of the community service program show an increase in community knowledge and skills in cultivating vegetable plants in their yards. Planting vegetable plants in the yard can be a source of food, medicine and environmental beauty, as well as increasing people's income.

First Report of Anthracnose Caused by Colletotrichum spaethianum on Zephyranthes candida in China.

Zephyranthes candida, an bulbous perennial plant, are planted in almost every park. In October 2023, anthracnose symptoms were observed on Z. candida leaves in Jiangxi Agricultural University (28.75° N, 115.83°E), Nanchang, Jiangxi Province, China, and the incidence of disease were up to 35% (140 of 400 plants). The lesions extended fromtheleafapextothebase, appearing as a dark brown color, and later changed to yellow and became dry. To isolate the pathogen, 20 symptomatic leaves were collected and cut into small pieces (4×4 mm, one pieces per leave), surface-sterilized with 70% ethanol for 10 s and 1% NaClO for 30 s, rinsed thrice with sterile water, placed onto potato dextrose agar (PDA) plates and incubated at 25℃ for 5 days. Fifteen isolates (15 out of 20) with similar morphological characteristics were obtained. The colonies on PDA presented effuse mycelium, initially white and later pale gray. Conidia were hyaline, curved or slightly curved, aseptate, with a truncate base and acute apex, measuring 17 to 23 × 3 to 6 μm (n = 50), and were matched to Colletotrichum species (Damm et al. 2009). To further confirm species, two representative isolates (JFRL 03-2873 and JFRL 03-2874) were selected for molecular identification. The internal transcribed spacer (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), chitin synthase (CHS), histone 3 (HIS3), actin (ACT) and β-tubulin 2 (TUB2) regions were amplified and sequenced by using primers sets ITS5/ITS4, Gpd1/Gpd2, CHS-79F/CHS354R, CYLH3F/CYLH3R, ACT-512F/ACT-783R and T1/Bt2b (Tan et al. 2022), respectively. These sequences were deposited into GenBank with accession number PP425890-PP425891 (ITS), PP437551-PP437552 (GAPDH), PP437549-PP437550 (CHS), PP480643-PP480644 (HIS3), PP437547-PP437548 (ACT) and PP437553-PP437554 (TUB2). A BLASTN search revealed high similarity of 99%-100% to ITS (GU227807, 518 nt/519 nt), GAPDH (GU228199, 525 nt/526 nt), CHS (GU228297, 251 nt/251 nt), HIS3 (GU228003, 372 nt/373 nt), ACT (GU227905, 236 nt/237 nt) and TUB2 (GU228101, 490 nt/490 nt) sequences of Colletotrichum spaethianum CBS 167.49. A maximum likelihood phylogenetic tree was constructed by combining ITS, GAPDH, CHS , HIS3, ACT and TUB2 sequences in IQtree web server (Ngugen et al. 2015). The result indicated that the two representative isolates were clustered together with Colletotrichum spaethianum in a clade with 100% bootstrap support. Based on morphologicalobservationandsequenceanalysis, the isolates were identified as C. spaethianum. To confirm pathogenicity, six surface-sterilized leaves of Z. candida were wounded and inoculated with 1 × 106 conidia/ml conidial suspension of JFRL 03-2873, and control leaves were inoculated with sterile water. They were incubated at 25 ℃ with 12 h photoperiod and 80% humidity, theexperimentwasrepeatedtwice. After five days, all leaves inoculated with JFRL 03-2873 displayed anthracnose symptom, whereas the control leaves remained unaffected. We re-isolated C. spaethianum from the symptomatic leaves and identified it based on morphological and molecular characteristics. Previous studies reported that C. spaethianum caused anthracnose on various common herbaceous plants in China (Vieira et al. 2014, Guo et al. 2013), but to our knowledge, this is the first report of C. spaethianum causing anthracnose on Z. candida in China. Anthracnose disease caused great economic loss to the cultivation of landscape plant Z. candida. Therefore, it is necessary to pay more attention to the anthracnose disease of herbaceous plants caused by C. spaethianum and develop appropriate control strategies.

InViTMixup: plant disease classification using convolutional vision transformer with Mixup augmentation

ABSTRACT Computer vision-based research is carried out to enhance the crop yield with automation solutions. In Tamil Nādu, tomato cultivation is carried out in many districts, including Coimbatore, and its contribution to the Indian economy, i.e. GDP growth, is significant. This paper proposes InViT Mixup, a novel approach toward image classification of diseased tomato leaves. The effectiveness of convolutional neural network (CNN) in obtaining spatial information and the attention mechanism of vision transformers, along with Mixup data augmentation, are combined, and the model’s generalization in predicting the diseased tomato based on leaf symptoms is evaluated. The proposed convolutional transformer model improvised the classification results of tomato disease in terms of accuracy and training speed compared to pure CNN or transformer-based models. In the proposed work, 10 tomato leaf disease classes from the PlantVillage dataset are used. Top-1 accuracy of 93.5% and Top-5 accuracy of 99.7% are achieved.

Antibacterial activity of high surface area zinc oxide nanoparticles for controlling black rot disease of Chinese kale

AbstractZinc oxide nanoparticles (ZnO NPs) are inorganic compounds listed as “generally recognized as safe” (GRAS) materials and have been used in plant production as well as for plant disease control. This study investigated the antibacterial efficacy of ZnO NPs with various surface areas against Xanthomonas campestris pv. campestris, assessed the effectiveness of ZnO NPs in controlling black rot disease in Chinese kale, and examined the influence of ZnO NPs application on soil bacterial communities. The results showed that ZnO NPs with high surface area effectively inhibited X. campestris pv. campestris by restraining growth and causing cell damage. Seed treatment and foliar spray application of high surface area ZnO NPs at 250 μg/mL significantly reduced the disease severity of black rot. Furthermore, in the greenhouse experiment, the soil bacterial communities in the treatment of plants applied with ZnO NPs did not differ from those in soil of nontreated plants. Therefore, ZnO NPs have the potential to serve as an alternative substance for plant disease management.

Alternaria in berry: Effects on its quality, pathogenic mechanisms and potential control strategies

AbstractIn recent years, the production and demand of berries in the world have gradually increased, but the infection of fungal pathogens has become one of the important factors restricting the production and sales of fresh berries. It has been found that Alternaria can cause a variety of plant diseases and fruit rot worldwide. This review introduces the effect of Alternaria infection on berry quality, including physical, chemical and physiological aspects, and discusses the possible infection mechanisms. In addition, Alternaria produces toxins during infections and seriously threatens the safety of berries and consumer health. Therefore, the prevention and control measures of Alternaria are systematically summarized. This review provides the basis for further optimization of postharvest preservation technology and fruit quality, to prolong the shelf life of berries. We can realistically expect to control plant diseases with increasing efficiency, which is conducive to improving the quality and safety of postharvest berry fruits.

Assessing the Effectiveness of Neem oil and Microalgae in the Management of Cowpea (Vigna unguiculata L.) Leaf Spot Caused by Alternaria spp.

The current study aimed to assess the effectiveness of neem oil and microalgae in controlling leaf spot caused by Alternaria spp. in cowpea (Vigna unguiculataL.). The research was conducted during the Zaid season of 2022 at the Central Research Field of the Department of Plant Pathology, SHUATS, Prayagraj, Uttar Pradesh, using a Randomized Block Design with three replications and seven treatments. Analysis of variance (ANOVA) was utilized to draw conclusions from the data. Leaf spot disease caused by Alternaria spp poses a significant threat to cowpea crops, resulting in yield losses and decreased plant vigor. An in vivo study was carried out to evaluate the impact of different concentrations of microalgae combined with neem oil at 1% on plant growth parameters and Disease Intensity (%). The application of microalgae @540 grams in various splits and neem oil @1% was done. Among all the treatments, T4 (Microalgae 48gms @5 DAS 96gms@15,45 DAS + Neem oil @1% showed promising results. Notably, the T4 treatment significantly increased plant height (84.63 cm) and the number of branches(10.83) at60DAS. Moreover, an increased numberof nodules per plant (21.44) was observed at 75 DAS, indicating improved root health and nitrogen fixation. Additionally, the treatment led to longer pod length (34.44 cm) in three pickings, suggesting enhanced yield potential. Furthermore, the combined application of microalgae and neem oil resulted in a significant reduction in disease intensity, with a notable decrease of 24.47% compared to untreated plants and those treated with chemicals (Bavistin). This indicates that the use of microalgae and neem oil together effectively suppressed leaf spot disease caused by Alternaria spp. in cowpea, thereby improving plant health and productivity. In conclusion, these findings underscore the potential use of eco-friendly alternatives like microalgae and neem oil for managing leaf spot disease in cowpea cultivation. Further research in this area is recommended for sustainable agricultural practices.

AvrSr27 is a zinc-bound effector with a modular structure important for immune recognition.

Effector proteins are central to the success of plant pathogens, while immunity in host plants is driven by receptor-mediated recognition of these effectors. Understanding the molecular details of effector-receptor interactions is key for the engineering of novel immune receptors. Here, we experimentally determined the crystal structure of the Puccinia graminis f. sp. tritici (Pgt) effector AvrSr27, which was not accurately predicted using AlphaFold2. We characterised the role of the conserved cysteine residues in AvrSr27 using in vitro biochemical assays and examined Sr27-mediated recognition using transient expression in Nicotiana spp. and wheat protoplasts. The AvrSr27 structure contains a novel β-strand rich modular fold consisting of two structurally similar domains that bind to Zn2+ ions. The N-terminal domain of AvrSr27 is sufficient for interaction with Sr27 and triggering cell death. We identified two Pgt proteins structurally related to AvrSr27 but with low sequence identity that can also associate with Sr27, albeit more weakly. Though only the full-length proteins, trigger Sr27-dependent cell death in transient expression systems. Collectively, our findings have important implications for utilising protein prediction platforms for effector proteins, and those embarking on bespoke engineering of immunity receptors as solutions to plant disease.

Diagnostics System for Plant Leaf Diseases Using Convergence of Computer Vision and Deep Learning

Early and accurate detection of plant diseases is crucial for maximizing agricultural yield and minimizing economic losses.Traditional approaches, however, frequently depend on specialized knowledge.They are ineffective for large-scale applications since they take a lot of time.The suggested method makes use of deep learning models' capabilities.With Densenet-121, it automatically extracts and gains knowledge of pertinent aspects from leaf photos.To identify a bundle of leaves, it employs a multi-object deep learning model.It can decipher and comprehend visual data from the leaves using computer vision.Accuracy is increased by utilizing deep learning and computer vision. The technique of transfer learning allows the system to recognize more veggies than just those in the training dataset. The results of our experimental evaluation show that our method is effective in precisely recognizing vegetables and diagnosing plant leaf diseases. Our system provides a scalable solution for agricultural management and plant health monitoring by expanding its coverage to include a variety of vegetables and automating disease diagnosis. KEYWORDS: CNN,Densenet-121,multi-object model

Plant Disease Detection Using Convolutional Neural Network

Plant disease detection is critical in agriculture, significantly impacting crop yield and food supply. This paper introduces an innovative approach utilizing deep learning and computer vision techniques to detect and classify plant diseases. The proposed method involves image processing to analyze leaf health conditions using a dataset of varied leaf images exhibiting disease manifestations. The model is trained to recognize distinct patterns associated with different diseases, enabling early detection and intervention. This AI-based system aims to enhance agricultural productivity, minimize crop losses, and contribute to food security. Furthermore, it has potential application in mobile platforms, providing farmers with a user-friendly tool for managing plant diseases. Nonetheless, challenges such as image condition variability and the necessity for comprehensive, diverse datasets for model training require further research and development.

Root exudate-mediated plant–microbiome interactions determine plant health during disease infection

Understanding the interactions between plants and microbes during pathogen infection is crucial for plant health improvement. However, the integrated role of soil nutrients, root exudates, and plant microbiomes in plant health during disease outbreaks remains unclear. In this study, we hypothesized that root exudate-mediated healthy microbiomes (HealthyBiome) would facilitate the escape of soil-borne pathogen infection, whereas a root exudate-mediated disease-conductive microbiome (PathoBiome) would induce disease occurrence. Using watermelon wilt disease caused by Fusarium oxysporum as a model, we investigated these interactions and their implications in plant disease. We examined the pathogen load across plant compartments during the pathogen accumulation (AH) and disease outbreak periods. We analyzed the plant microbiomes and root exudates during the outbreak, which was associated with the emergence in both healthy (BH) and diseased (BD) plants. Compared with AH and BD plants, BH plants had the lowest F. oxysporum density in the root–soil system (0.74–0.84 times that of AH and BD plants). BD plants showed an elevated amino acid metabolism (2.84–3.38 times that of healthy plants), whereas BH plants showed a higher metabolism of phenolic compounds (1.73 times that of diseased plants). BH plants secreted more palmitic acid (1.53 times that of diseased plants) and recruited more nitrogen (N)-fixing bacteria (e.g., Paenarthrobacter, Sphingomonas), which potentially alleviated N scarcity in the soil, promoted plant growth, and improved the plant defense. Structural equation modeling highlighted the interactions among soil nutrients, root metabolites, and plant nutrients in recruiting pro-growth bacteria to the rhizosphere and roots for healthy plant growth. Our findings provide novel evidence of how root exudates influence plant–microbiome interactions during pathogen infections, highlighting the importance of a balanced root exudate profile and its associated “HealthyBiome” in promoting plant health and growth.

Calcium signaling: Unveiling its role in disease control in plants

<p>Calcium signaling is a fundamental regulatory mechanism in plants, governing a myriad of physiological processes crucial for growth, development, and responses to environmental stimuli, including biotic stresses such as pathogen attack. Over the past decades, research into the intricate network of calcium signaling pathways has revealed its pivotal role in orchestrating plant defense mechanisms against a diverse array of pathogens. This review comprehensively examines the multifaceted roles of calcium ions in modulating plant immune responses, elucidating the molecular mechanisms underlying calcium-mediated signal transduction and its integration with other defense signaling pathways. We delve into the intricate crosstalk between calcium signaling and various hormonal pathways, reactive oxygen species (ROS) signaling, and secondary metabolite biosynthesis, highlighting the interconnectedness of these regulatory networks in shaping plant defense strategies. Furthermore, we explore the potential applications of calcium-based strategies for sustainable disease management in agriculture, including the use of calcium channel blockers, calcium chelators, and calcium-rich compounds as novel antimicrobial agents and priming agents for enhancing plant immunity. Challenges and future directions in the field are also discussed, aiming to advance our understanding of calcium signaling in plant-pathogen interactions and harness its potential for developing innovative approaches to combat plant diseases and ensure global food security. This review provides a comprehensive overview of the current state of knowledge regarding calcium signaling in plant disease control, offering valuable insights and perspectives for future research endeavors in this rapidly evolving field.<strong></strong></p>

New reports of pathogen spectrum associated with bulb rot and their interactions during the development of rot in tulip

Bulb rot, a highly damaging disease of tulip plants, has hindered their profitable cultivation worldwide. This rot occurs in both field and storage conditions posing significant challenges. While this disease has been attributed to a range of pathogens, previous investigations have solely examined it within the framework of a single-pathogen disease model. Our study took a different approach and identified four pathogens associated with the disease: Fusarium solani, Penicillium chrysogenum, Botrytis tulipae, and Aspergillus niger. The primary objective of our research was to examine the impact of co-infections on the overall virulence dynamics of these pathogens. Through co-inoculation experiments on potato dextrose agar, we delineated three primary interaction patterns: antibiosis, deadlock, and merging. In vitro trials involving individual pathogen inoculations on tulip bulbs revealed that B. tulipae,was the most virulent and induced complete bulb decay. Nonetheless, when these pathogens were simultaneously introduced in various combinations, outcomes ranged from partial bulb decay to elongated rotting periods. This indicated a notable degree of antagonistic behaviour among the pathogens. While synergistic interactions were evident in a few combinations, antagonism overwhelmingly prevailed. The complex interplay of these pathogens during co-infection led to a noticeable change in the overall severity of the disease. This underscores the significance of pathogen-pathogen interactions in the realm of plant pathology, opening new insights for understanding and managing tulip bulb rot.

Fungal communities shift with soybean cyst nematode abundance in soils

Plant parasitic nematodes pose a significant threat to food security. Management strategies for these pathogens are limited, and additional tools for their control are needed. Some fungi have shown promise for biocontrol, however, their success in the field has varied. In contrast, some fungal plant pathogens form synergistic associations with nematodes resulting in increased plant disease severity. However, how both groups of fungi change with different plant parasitic nematode abundances in the soil is underexplored. In this study, we used the soybean cyst nematode as a model to understand these changes. We sampled soil from 171 Ohio soybean fields in 2019 and 2021 and determined soybean cyst nematode abundance. We identified the fungi in the samples through amplicon sequencing of the 18S-ITS rDNA regions. Edaphoclimatic factors were used to classify samples into geographic regions to account for environmental differences between sampling locations. We hypothesized that fungal communities would be influenced by both region and soybean cyst nematode abundance. K-near neighbor analysis revealed that fungal communities follow regional patterns. We also found that soybean cyst nematode abundance was associated with changes in these communities regardless of the region. Two potential nematophagous fungi were found to be prevalent in Ohio through core community analysis, although they were enriched when soybean cyst nematode abundance was high. Lastly, differential network analysis showed that interactions among fungal community members change when soybean cyst nematode is present in the soil. Together these results suggest that this nematode significantly shifts the fungal community composition in field soils.

Free-Riding in Plant Health: A Social-Ecological Systems Approach to Collective Action.

Plant disease epidemics often transcend land management boundaries, creating a collective-action problem where a group must cooperate in a common effort to maximize individual and group benefits. Drawing upon the social-ecological systems framework and associated design principles, we review variables of resource systems, resource units, actors, and governance systems relevant to collective action in plant health. We identify a need to better characterize how attributes of epidemics determine the usefulness of collective management, what influences actors' decisions to participate, what governance systems fit different plant health threats, and how these subsystems interact to lead to plant health outcomes. We emphasize that there is not a single governance structure that ensures collective action but rather a continuum of structures that depend on the key system variables identified. An integrated social-ecological systems approach to collective action in plant health should enable institutional designs to better fit specific plant health challenges.

A synoptic review of plant disease epidemics and outbreaks published in 2022

This scientometric study reviews the scientific literature and CABI distribution records published in 2022 to find evidence of major disease outbreaks and first reports of pathogens in new locations or on new hosts. This is the second time we have done this, and this study builds on our work documenting and analysing reports from 2021. Pathogens with three or more articles identified in 2022 literature were: Xylella fastidiosa, Bursaphelenchus xylophilus, Meloidogyne species complexes, Candidatus Liberibacter asiaticus, Raffaelea lauricola, Fusarium oxysporum formae specialis and Puccinia graminis f. sp. tritici. Our review of CABI distribution records found 29 pathogens with confirmed first reports in 2022. Pathogens with four or more first reports were: Meloidogyne species complexes, Pantoea ananatis, grapevine red globe virus and Thekopsora minima. Analysis of the proportion of new distribution records from 2022 indicated that grapevine red globe virus, sweet potato chlorotic stunt virus and Ca. Phytoplasma vitis may have been actively spreading. As we saw last year, there was little overlap between the pathogens identified by reviewing scientific literature versus distribution records. Strikingly, too, there was also no overlap between species assessed to be actively spreading in this year’s study and those identified last year. In general, introduction of new pathogens and outbreaks of extant pathogens threaten food security and ecosystem services. Continued monitoring of these threats is essential to support phytosanitary measures intended to prevent pathogen introductions and management of threats within a country.

Kiwifruit resistance to gray mold is enhanced by yeast-induced modulation of the endophytic microbiome

The influence of endophytic microbial community on plant growth and disease resistance is of considerable importance. Prior research indicates that pre-treatment of kiwifruit with the biocontrol yeast Debaryomyces hansenii suppresses gray mold disease induced by Botrytis cinerea. However, the specific underlying mechanisms remain unclear. In this study, Metagenomic sequencing was utilized to analyze the composition of the endophytic microbiome of kiwifruit under three distinct conditions: the healthy state, kiwifruit inoculated with B. cinerea, and kiwifruit treated with D. hansenii prior to inoculation with B. cinerea. Results revealed a dominance of Proteobacteria in all treatment groups, accompanied by a notable increase in the relative abundance of Actinobacteria and Firmicutes. Ascomycota emerged as the major dominant group within the fungal community. Treatment with D. hansenii induced significant alterations in microbial community diversity, specifically enhancing the relative abundance of yeast and exerting an inhibitory effect on B. cinerea. The introduction of D. hansenii also enriched genes associated with energy metabolism and signal transduction, positively influencing the overall structure and function of the microbial community. Our findings highlight the potential of D. hansenii to modulate microbial dynamics, inhibit pathogenic organisms, and positively influence functional attributes of the microbial community.

A meta-analysis on global change drivers and the risk of infectious disease.

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors1. Studies have shown that infectious disease risk is modified by changes to biodiversity2-6, climate change7-11, chemical pollution12-14, landscape transformations15-20 and species introductions21. However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host-parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

Plant virus transmission during seed development and implications to plant defense system

Most plants produce large amounts of seeds to disperse their progeny in the environment. Plant viruses have evolved to avoid plant resistance mechanisms and use seeds for their dispersal. The presence of plant pathogenic viruses in seeds and suppression of plant host defenses is a major worldwide concern for producers and seed companies because undetected viruses in the seed can represent a significant threat to yield in many economically important crops. The vertical transmission of plant viruses occurs directly through the embryo or indirectly by getting in pollen grains or ovules. Infection of plant viruses during the early development of the seed embryo can result in morphological or genetic changes that cause poor seed quality and, more importantly, low yields due to the partial or ubiquitous presence of the virus at the earliest stages of seedling development. Understanding transmission of plant viruses and the ability to avoid plant defense mechanisms during seed embryo development will help identify primary inoculum sources, reduce virus spread, decrease severity of negative effects on plant health and productivity, and facilitate the future of plant disease management during seed development in many crops. In this article, we provide an overview of the current knowledge and understanding of plant virus transmission during seed embryo development, including the context of host-virus interaction.

Assessing the effect of sample storage time on viral detection using a rapid and cost-effective CTAB-based extraction method

BackgroundCassava leaf samples degrade quickly during storage and transportation from distant areas. Proper sampling and efficient, low-cost storage methods are critical for obtaining sufficient quality DNA and RNA for plant virus epidemiology and improving disease control understanding. This is useful when samples are collected from remote areas far from a laboratory or in developing countries where money and materials for virus diagnostics are scarce.ResultsThe effect of sample storage duration on nucleic acid (N.A.) quality on virus detection was investigated in this study. A simple, rapid, and cost-effective CTAB-based approach (M3) for single N.A. extraction was optimized and tested alongside two existing CTAB-based methods (M1 and M2) for N.A. extraction from fresh and herbarium cassava leaves stored for; 1, 8, 26, and 56 months. The amount and quality of DNA and RNA were determined using Nanodrop 2000 c U.V.–vis Spectrophotometer and agarose gel electrophoreses. The sample degradation rate was estimated using a simple mathematical model in Matlab computational software. The results show no significant difference in mean DNA concentration between M1 and M2 but a significant difference between M3 and the other two methods at p < 0.005. The mean DNA concentration extracted using M3 was higher for 1 and 8 months of leave storage. M3 and M2 produced high concentrations at 26 and 56 months of leave storage. Using a developed scale for quality score, M3 and M2 produced high-quality DNA from fresh samples. All methods produced poor-quality DNA and RNA at 8 and 26 months of leave storage and no visual bands at the age of 56 months. Statistically, there was a significant difference in the mean DNA quality between M1 and M2, but there was no significant difference between M3 and the other two methods at p < 0.005. However, Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) were readily detected by RT-PCR from RNA isolated using M3. The quality of DNA declined per storage time at 0.0493 and 0.0521/month, while RNA was 0.0678 and 0.0744/month. Compared to the existing two methods, modified CTAB extracted enough high-quality N.A. in one-third the time of the existing two methods.ConclusionOur method provides cost-effective, quick, and simple processing of fresh and dry samples, which will quicken and guide the decision on when and what type of sample to process for plant disease management and surveillance actions.

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8.