Potato Disease Research Articles

Sclerotinia sclerotiorum on bean and potato in Sinaloa: Etiology, epidemiology and alternatives for management

White mold (Sclerotinia sclerotiorum) is the main disease of bean and potato in Sinaloa. In the present review, the symptoms and signs of the disease as well as cultural and morphological characteristics of the teleomorph of the pathogen, its ecology and the epidemiology of the disease are addressed. The implementation of a prediction system which includes the carpogenic germination of the sclerotia and the phenology of both bean and potato for the management of the disease is described. This system considers soil temperature ranging from 13 to 19 °C a at depth of 2.5 cm in the soil and the flowering stage in both bean and potato to do the first spray application of synthetic fungicide to prevent the disease. In vitro studies indicated that Trichoderma harzianum, T. viride and T. atroviride reduced mycelial growth rate of S. sclerotiorum. The same antagonistic species exerted control of white mold under field conditions, where an increment of 40% of yield was observed in the treated plots, with respect to those treated with fungicide fluazinam. Future lines of research focusing on the ecology of the pathogen and management of the disease including the antagonistic fungi in the prediction system are suggested.

Management of late blight (Phytophthora infestans) of potato (Solanum tuberosum) through efficient spray schedule of fungicides in the north-eastern Himalayan region of India

Phytophthora infestans (Mont.) de Bary, belongs to order Peronosporales is one of the most destructive phytopathogen causing late blight disease of potato (Solanum tuberosum L.). The disease symptoms appear on leaf, petiole, stem, and tubers at any stage of crop growth and may cause severe disease epidemics leading to complete defoliation and extensive yield loss. The field experiments were conducted during 2020–21 and 2021–22 at Chesrimai GP, Charilam block, Sepahijala, Tripura to find out the effective management strategies with fungicides to mitigate the losses caused by the devastating disease. The experiment was laid out in a randomized block design (RBD) in 3 replications with 8 treatments having different fungicides in different combinations for determining the best spray schedule against late bight disease. Results revealed that one spraying of Mancozeb 75% @0.25% at 35 days after sowing (DAS), second spraying of Cymoxanil 8% + Mancozeb 64% @0.25% at 42 DAS, and third spraying of Mancozeb 75% @0.25% at 49 DAS and fourth spraying of Cymoxanil 8% + Mancozeb 64% @ 0.25% at 56 DAS recorded the best performing treatment, with the least mean disease severity (10.54 and 7.30%), the least mean disease incidence (15.00 and 12.75%), the least coefficient of disease index (CODEX) value (1.58% and 0.93%), and highest tuber yield (21.27 and 21.68 t/ha) during 2020–21 and 2021–22, respectively. In addition, there were 26.6 and 27.7% increase in yield over control during 2020–21 and 2021–22, respectively and the pooled incremental cost benefit ratio (ICBR) of 1:16.09 was found to be highest among all other treatments. Efficient spray schedule of prophylactic/ preventive spray with contact fungicide followed by spraying with translaminar/systemic + contact fungicide can effectively manage the late blight disease of potato under field conditions.

Pectobacterium punjabense Causing Blackleg and Soft Rot of Potato: The First Report in the Russian Federation.

Phytopathogenic bacteria of the genus Pectobacterium are responsible for several diseases that affect potato (Solanum tuberosum L.) production worldwide, including blackleg and tuber soft rot. These bacteria are highly diverse, with over 17 different species currently identified. However, some of the recently described species, such as Pectobacterium punjabense, are still poorly understood. In this study, we focused on P. punjabense isolates collected from diseased potato tubers in Russia in 2021. Whole-genome sequencing was used to characterise the genomic diversity of the pathogen and determine the biochemical profiles of the isolated bacteria. The ability of these isolates to cause soft rot symptoms was tested. A comparative assessment of the potential pathogenicity of the Pectobacterium isolates was conducted by infecting potato tubers and measuring the accumulation of biomass in a liquid medium during cultivation at different temperatures. A TaqMan qPCR assay was developed for the highly sensitive and specific characterisation of P. punjabense strains, which can be used in diagnostic systems. This is the first report on P. punjabense causing potato disease in the Russian Federation.

Using in silico analysis to investigate the false positive potential of qPCR systems for potato disease diagnosis

Using in silico analysis to investigate the false positive potential of qPCR systems for potato disease diagnosis

Impact of biopreparations on the phytopathological state of potato plants

Given the frequent use of chemical agents for the control of potato diseases, it is crucial to explore protective measures that consider agroecological aspects. This study aimed to determine the impact of biological preparations on the development of late blight and alternariosis in potatoes. Research on the effects of biopreparations on the main diseases of potatoes was conducted over three years under field conditions at the Volyn State Agricultural Research Station of the Institute of Potato Growing of the National Academy of Agrarian Sciences of Ukraine. The experimental design, disease diagnosis, and assessment of the technical efficacy of biological preparations against potato diseases were performed according to established methodologies. The experiments revealed that the application of biopreparations under various conditions leads to a reduction in the intensity of late blight and alternariosis in potatoes, as well as contributing to the preservation of yield. Lower disease development and higher efficacy of biopreparations were observed in the context where the biodestructor Ekostern (1.2 L/ha) and the biopreparation MycoHelp (2.0 L/ha) were applied, alongside three rounds of plant spraying with various biological preparations – Agat-25K, Regoplant, PhytoHelp, and Stimpo. Among these, PhytoHelp, with an application rate of 1.0 L/ha, exhibited the highest technical efficacy. The use of biological preparations in different contexts increased potato tuber yield. Specifically, with the application of MycoHelp at 2.0 L/ha and the spraying of plants with various biological agents, the preserved yield ranged from 1.71 to 2.02 t/ha. The application of biopreparations alongside the use of Ekostern at 1.2 L/ha and MycoHelp at 2.0 L/ha contributed to yield increases across various treatments, ranging from 1.6 to 2.21 t/ha. The obtained results are deemed useful for enhancing the effectiveness of the integrated protection system for potatoes against specific diseases and for developing organic cultivation technologies for table potatoes on sod-podzolic soils

Impact of biopreparations on the phytopathological state of potato plants

Given the frequent use of chemical agents for the control of potato diseases, it is crucial to explore protective measures that consider agroecological aspects. This study aimed to determine the impact of biological preparations on the development of late blight and alternariosis in potatoes. Research on the effects of biopreparations on the main diseases of potatoes was conducted over three years under field conditions at the Volyn State Agricultural Research Station of the Institute of Potato Growing of the National Academy of Agrarian Sciences of Ukraine. The experimental design, disease diagnosis, and assessment of the technical efficacy of biological preparations against potato diseases were performed according to established methodologies. The experiments revealed that the application of biopreparations under various conditions leads to a reduction in the intensity of late blight and alternariosis in potatoes, as well as contributing to the preservation of yield. Lower disease development and higher efficacy of biopreparations were observed in the context where the biodestructor Ekostern (1.2 L/ha) and the biopreparation MycoHelp (2.0 L/ha) were applied, alongside three rounds of plant spraying with various biological preparations – Agat-25K, Regoplant, PhytoHelp, and Stimpo. Among these, PhytoHelp, with an application rate of 1.0 L/ha, exhibited the highest technical efficacy. The use of biological preparations in different contexts increased potato tuber yield. Specifically, with the application of MycoHelp at 2.0 L/ha and the spraying of plants with various biological agents, the preserved yield ranged from 1.71 to 2.02 t/ha. The application of biopreparations alongside the use of Ekostern at 1.2 L/ha and MycoHelp at 2.0 L/ha contributed to yield increases across various treatments, ranging from 1.6 to 2.21 t/ha. The obtained results are deemed useful for enhancing the effectiveness of the integrated protection system for potatoes against specific diseases and for developing organic cultivation technologies for table potatoes on sod-podzolic soils

A novel genus of Pectobacterium bacteriophages display broad host range by targeting several species of Danish soft rot isolates

The bacterial diseases black leg and soft rot in potatoes cause heavy losses of potatoes worldwide. Bacteria within the genus Pectobacteriaceae are the causative agents of black leg and soft rot. The use of antibiotics in agriculture is heavily regulated and no other effective treatment currently exists, but bacteriophages (phages) have shown promise as potential biocontrol agents. In this study we isolated soft rot bacteria from potato tubers and plant tissue displaying soft rot or black leg symptoms collected in Danish fields. We then used the isolated bacterial strains as hosts for phage isolation. Using organic waste, we isolated phages targeting different species within Pectobacterium. Here we focus on seven of these phages representing a new genus primarily targeting P. brasiliense; phage Ymer, Amona, Sabo, Abuela, Koroua, Taid and Pappous. TEM image of phage Ymer showed siphovirus morphotype, and the proposed Ymer genus belongs to the class Caudoviricetes, with double-stranded DNA genomes varying from 39 kb to 43 kb. In silico host range prediction using a CRISPR-Cas spacer database suggested both P. brasiliense, P. polaris and P. versatile as natural hosts for phages within the proposed Ymer genus. A following host range experiment, using 47 bacterial isolates from Danish tubers and plants symptomatic with soft rot or black leg disease verified the in silico host range prediction, as the genus as a group were able to infect all three Pectobacterium species. Phages did, however, primarily target P. brasiliense isolates and displayed differences in host range even within the species level. Two of the phages were able to infect two or more Pectobacterium species. Despite no nucleotide similarity with any phages in the NCBI database, the proposed Ymer genus did share some similarity at the protein level, as well as gene synteny, with currently known phages. None of the phages encoded integrases or other genes typically associated with lysogeny. Similarly, no virulence factors nor antimicrobial resistance genes were found, and combined with their ability to infect several soft rot-causing Pectobacterium species from Danish fields, demonstrates their potential as biocontrol agents against soft rot and black leg diseases in potatoes.

Chitosan as an Antimicrobial, Anti-Insect, and Growth-Promoting Agent for Potato (Solanum tuberosum L.) Plants.

A growing trend in plant protection is replacing chemical preparations with environmentally friendly biological compositions. Chitosan, due to its biocompatibility, biodegradability, and bioactivity, is an effective agent against plant diseases. The purpose of the study was to evaluate chitosan as a potential biopesticide for potato plants. Three variants of chitosan were tested: high (310-375 kDa, >75% deacetylated), medium (190-310 kDa, 75-85% deacetylated), and low (50-190 kDa, 75-85% deacetylated) molecular weight. The chitosan variants were dissolved in lactic and succinic acids and tested for antibacterial and antifungal properties against eight strains of mould and two strains of bacteria responsible for potato diseases. The possible cytotoxicity of chitosan was evaluated against different cell lines: insect Sf-9, human keratinocyte HaCaT, and human colon carcinoma Caco-2. The bioprotective activities of the chitosan were also evaluated in situ on potato tubers. Chitosan inhibited the growth of almost all the selected phytopathogens. The most active was medium molecular chitosan in lactic acid. This formula was characterized by low toxicity towards human cells and high toxicity towards Sf-9 cells. It was also found to have positive effects on the growth of stems and roots, gas exchange, and chlorophyll index in potato plants. Selected chitosan formulation was proposed as a functional biopesticide for potato protection against phytopathogens.

Early Detection of Potato Disease Using an Enhanced Convolutional Neural Network-Long Short-Term Memory Deep Learning Model

Early Detection of Potato Disease Using an Enhanced Convolutional Neural Network-Long Short-Term Memory Deep Learning Model

In Vitro Assay of Lytic Bacteriophage to Suppress the Growth of Ralstonia syzygii subsp. indonesiensis, the Causal Pathogen of Potato Wilt Disease

Bacterial wilt disease, the one of the major diseases of potatoes, caused by Ralstonia syzygii subsp. indonesiensis (Rsi). Many efforts have been made to control bacterial wilt disease, including physical control, chemicals, and the use of bacteriophages. Previous studies have shown that bacteriophage application in controlling plant diseases is a fast-expanding area and has great potential to replace chemical methods. This study aims to determine the potential of lytic bacteriophage in suppressing the growth of Rsi in vitro. This study used a Non-Factorial, Completely Randomized Design with 3 replications and 6 treatment levels: R1 (Rsi isolate 1 without bacteriophage), R2 (Rsi isolate 1 with bacteriophage), R3 (Rsi isolate 2 without bacteriophage), R4 (Rsi isolate 2 with bacteriophage), R5 (Rsi isolate 3 without bacteriophage), and R6 (Rsi isolate 3 with bacteriophage). The results showed that bacteriophage could reduce the Rsi population at 24 hours by looking at the optical density (OD) value of 600 nm wavelength and growing on NA medium using the spread-plate method. The best treatment was at R6 with a population of 1 x 108cfu/ml Rsi. This research suggests that bacteriophage has the potential to suppress the growth of Rsi, which causes potato bacterial wilt disease, in vitro.

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}: 95.50%, and BreakHis 100×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.

Unveiling the frontiers of potato disease research through bibliometric analysis.

Research on potato diseases had been widely reported, but a systematic review of potato diseases was lacking. Here, bibliometrics was used to systematically analyze the progress of potato disease. The publications related to "potato" and "disease" were searched in the Web of Science (WOS) from 2014 to 2023. The results showed that a total of 2095 publications on potato diseases were retrieved, with the annual publication output increasing year by year at a growth rate of 8.52%. The main countries where publications were issued were the United States, China, and India. There was relatively close cooperation observed between China, the United States, and the United Kingdom in terms of international collaboration, while international cooperation by India was less extensive. Based on citation analysis and trending topics, potential future research directions include nanoparticles, which provides highly effective carriers for biologically active substances due to their small dimensions, extensive surface area, and numerous binding sites; machine learning, which facilitates rapid identification of relevant targets in extensive datasets, thereby accelerating the process of disease diagnosis and fungicide innovation; and synthetic communities composed of various functional microorganisms, which demonstrate more stable effects in disease prevention and control.

Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation

Recently, every aspect of human existence has been affected by modern technologies including agriculture. The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. This work uses a deep learning algorithm to investigate the classification of potato diseases as a case study which can be leveraged on by other agricultural products for reference. Potatoes play a vital role in global agriculture, constituting a significant portion of fresh produce consumption. In southern Africa, potatoes hold particular importance, comprising 39% of total fresh produce consumption. However, the industry faces challenges from diseases such as early blight and late blight, resulting in substantial yield losses. Early detection is crucial, prompting the exploration of Convolutional Neural Networks (CNNs) for their disease classification capabilities. CNNs have shown excellent results in plant disease classification based on image data set. This study proposes the potential of aligning existing software-based Central Processing Units (CPUs) and Graphic Processing Units (GPUs) with FPGA-based potato disease classification using CNNs. To this end, five CNN models were trained on the Plant Village dataset for analysis. The models were compared using various performance metrics such as sensitivity, F1-Score, precision and recall. Based on the analysis the most suitable CNN model for FPGA implementation was selected. The chosen model was optimized for size using quantization technique. Subsequently the model was converted to Hardware Description Language (HDL) using Vitis High level synthesis (HLS) tool performance in terms of resources required to implement the model in System on Chip (SoC) was analysed.

Limestone rate affects potato bacterial wilt disease and <i>Ralstonia solanacearum</i>’s population in the soil in the Western Highlands zone of Cameroon

Limestone rate affects potato bacterial wilt disease and <i>Ralstonia solanacearum</i>’s population in the soil in the Western Highlands zone of Cameroon

Harnessing Soil Diversity: Innovative Strategies for Potato Blight Management in Central-Eastern Poland

Faced with increasing climate challenges, this pioneering study introduces groundbreaking approaches to ensure the resilience of potato crops in east-central Poland. Our research was aimed at developing an innovative, cost-effective system tailored to the diverse local conditions of this region. Conducted between 2018 and 2020, the study analyzed integrated and organic production systems across different soil types and potato varieties. The experiment was conducted using a randomized block design with three replications in two locations. Integrated production systems involve the strategic use of mineral fertilization and chemical protection to optimize crop growth and health, whereas organic production systems rely solely on natural inputs and biological pest control methods. Integrated practices, enhanced by mineral fertilization and chemical protection, notably delayed the onset of late blight by 16 days and extended the critical infection period by 17% compared to their organic counterparts. Remarkably, the integrated systems resulted in a 49% increase in total production and a 52% increase in commercial yields, demonstrating their effectiveness in enhancing crop resilience. Different potato varieties exhibit varied responses to cultivation systems, influencing both yield and disease resistance. Further investigation into these varietal responses can help optimize cultivation practices, leading to improved efficiency and sustainability in potato farming. Location and soil conditions have a significant impact on potato yield and the spread of potato blight. Specific soil properties such as pH levels, organic matter content, soil texture (clay, silt, and sand composition), moisture retention capacity, and nutrient availability are crucial in determining potato productivity and disease dynamics. Further research into these soil properties and the adaptation of varieties to local conditions can contribute to increased productivity and stability in potato production. This study not only paves the way toward sustainable agriculture but also highlights the crucial role of soil diversity in shaping resilient farming practices. Potato producers in the eastern-central region of Poland should implement integrated production systems using mineral fertilization and chemical plant protection, adapted to local soil conditions and potato varieties. These practices can delay the appearance of late blight and increase the total and marketable potato yield. Further research on soil properties and variety adaptation may increase production stability and efficiency. Promoting soil diversity and modern technologies will ensure resilient and sustainable agricultural production in the face of climate change.

Alternative splicing of a potato disease resistance gene maintains homeostasis between growth and immunity.

Plants possess a robust and sophisticated innate immune system against pathogens and must balance growth with rapid pathogen detection and defense. The intracellular receptors with nucleotide-binding leucine-rich repeat (NLR) motifs recognize pathogen-derived effector proteins and thereby trigger the immune response. The expression of genes encoding NLR receptors is precisely controlled in multifaceted ways. The alternative splicing (AS) of introns in response to infection is recurrently observed but poorly understood. Here we report that the potato (Solanum tuberosum) NLR gene RB undergoes AS of its intron, resulting in 2 transcriptional isoforms, which coordinately regulate plant immunity and growth homeostasis. During normal growth, RB predominantly exists as an intron-retained isoform RB_IR, encoding a truncated protein containing only the N-terminus of the NLR. Upon late blight infection, the pathogen induces intron splicing of RB, increasing the abundance of RB_CDS, which encodes a full-length and active R protein. By deploying the RB splicing isoforms fused with a luciferase reporter system, we identified IPI-O1 (also known as Avrblb1), the RB cognate effector, as a facilitator of RB AS. IPI-O1 directly interacts with potato splicing factor StCWC15, resulting in altered localization of StCWC15 from the nucleoplasm to the nucleolus and nuclear speckles. Mutations in IPI-O1 that eliminate StCWC15 binding also disrupt StCWC15 re-localization and RB intron splicing. Thus, our study reveals that StCWC15 serves as a surveillance facilitator that senses the pathogen-secreted effector and regulates the trade-off between RB-mediated plant immunity and growth, expanding our understanding of molecular plant-microbe interactions.

A Complex Approach to Control Black Dot Disease in Potato

In recent years, skin blemish diseases of potato (including black dot (BD) caused by Colletotricum coccodes) have begun to be important for global potato marketing, since consumers often reject tubers with an imperfect appearance, which results in financial losses caused by the disposal of unwanted potatoes. Like for many non-fatal plant diseases, BD severity may depend on the immune status of plants influenced by other infectious agents. Using a set of 98 potato cultivars differing in their late blight (LB) resistance, we examined the correlation between the intensity of their infection with LB determined by their LB resistance and the occurrence of the BD disease under field conditions with a high background level of both diseases. Using LB-susceptible (Arizona) and moderately susceptible (Sante) cultivars, we also evaluated the effect of a crop protection against LB on BD development under the same field conditions. A strong negative correlation (r = −0.81, p < 0.05) between the LB resistance and the BD occurrence has been revealed. An experiment using the two cultivars, chemically protected against LB, showed a significant reduction in BD occurrence of 30% (cv. Arizona) and 20% (cv. Sante) compared to the untreated controls; the total yield and marketability of potatoes increased by 103.6 and 62.5% for cv. Arizona and by 65.9 and 43.8% for cv. Sante. The reduction in the LB affection of potato is one of the key factors improving the immune status of potato cultivars in relation to BD infection, so methods of LB protection should be included in a complex approach to BD control.

AI-PotatoGuard: Leveraging Generative Models for Early Detection of Potato Diseases

AI-PotatoGuard: Leveraging Generative Models for Early Detection of Potato Diseases

Zinc oxide and copper oxide nanoparticles as a potential solution for controlling Phytophthora infestans, the late blight disease of potatoes

Late blight, caused by Phytophthora infestans, is a major potato disease globally, leading to significant economic losses of $6.7 billion. To address this issue, we evaluated the antifungal activity of ZnO and CuO nanoparticles (NPs) against P. infestans for the first time in laboratory and greenhouse conditions. Nanoparticles were synthesized via a chemical precipitation method and characterized using various techniques. The XRD results revealed that the synthesized ZnO nanoparticles had a pure hexagonal wurtzite crystalline structure, whereas the CuO NPs had a monoclinic crystalline structure. TEM images confirmed the synthesis of quasi-spherical nanoparticles with an average size of 11.5 nm for ZnO NPs and 24.5 nm for CuO NPs. The UV–Vis Spectral Report showed peaks corresponding to ZnO NPs at 364 nm and 252 nm for CuO NPs.In an in vitro study, both ZnO and CuO NPs significantly (p < 0.05) inhibited the radial growth of P. infestans at all tested concentrations compared to the untreated control. The highest inhibitory effect of 100% was observed with ZnO and CuO NPs at 30 mg/L. A lower inhibition of 60.4% was observed with 10 mg/L CuO NPs. Under greenhouse conditions, 100 mg/L ZnO NPs was the most effective treatment for controlling potato late blight, with an efficacy of 71%. CuO NPs at 100 mg/L followed closely, with an efficacy of 69%. Based on these results, ZnO and CuO NPs are recommended as promising eco-friendly fungicides for the management and control of potato late blight after further research.Graphical abstract

Sweet Potato Virus Disease and Its Associated Vectors: Farmers’ Knowledge and Management Practices in Uganda

Effective management of sweet potato diseases such as sweet potato virus disease (SPVD) depends to a large extent on farmers&amp;rsquo; knowledge of the disease as well as on the integration of recommended management methods in their farming practices. SPVD has continued to be the most important disease constraining sweet potato (Ipomoea batatas) production in sub-Saharan Africa (SSA). Inadequate information about farmers&amp;rsquo; perception, knowledge and practices are the major impediments in developing countries and has hindered development of effective management of SPVD. This paper addresses the gap by (i) Understanding the socioeconomic characteristics of sweet potato farmers. (ii) Assessing knowledge level of the farmers about SPVD, SPVD vectors and management methods. (iii) Examining the relationship between farmers&amp;rsquo; coping strategies to control SPVD and knowledge. To achieve the study objectives, a cross sectional survey was carried out among 95 sweet potato growing households in central, eastern and western regions of Uganda during 2017. Results showed that sweet potato is valued as the second most important subsistence crop among smallholder farmers. The female farmers (54.7%) were more involved in production than their male counterpart. SPVD was perceived by the majority of farmers (63.6%) as the most important disease and a total of 70.5% of these farmers had experienced the disease in their fields. Despite of SPVD prevalence as perceived by the farmers, close to half (48.4%) of these farmers did not have good knowledge of the SPVD, 67.4% were ignorant about SPVD vectors, 85.8% did not know management methods and hence 68.4% did not use any management method. These knowledge gaps down play stability of the farmers to effectively manage the disease. Nevertheless, it was revealed that the ability to identify SPVD, month of occurrence and education level improves its management. This paper recommends gender tailed support to sweet potato farming, increased awareness and training of farmers to improve their knowledge of SPVD, and development of effective control strategies for SPVD.