Potato Disease Research Articles

Pectinesterase activity and gene expression correlate with pathogenesis of Phytophthora infestans

Late blight caused by Phytophthora infestans is the most devastating disease of potato. Phytophthora infestans produces many secondary metabolites and effector proteins, involved in the pathogenesis, which compromise host defense mechanisms. Pectinesterase (PE) is a cell wall degrading enzyme secreted by P. infestans to infect the host. To examine the role of PE in P. infestans, 15 strains of P. infestans were isolated from infected potato leaves in Yunnan, China. We analyzed the biological effects of exogenously added PE on P. infestans and its activity and gene expression after infection of potato using quantitative real-time polymerase chain reaction (RT-PCR). It was found that PE significantly promotes the growth of P. infestans, increases the weight of mycelium and the number of sporangia, and promotes the sporangial germination. PE accelerated the infection process of P. infestans on potato. The pathogenicity of P. infestans was positively correlated with PE activity and gene expression. PE is a key to the virulence difference of potato late blight.

Evaluation of genetic diversity and genome-wide association studies of resistance to bacterial wilt disease in potato.

The development of novel improved varieties adapted to unstable environmental conditions is possible through the genetic diversity of breeding materials. Potato is among the most important food crops worldwide, however, there are still significant hindrances to breeding gains attributed to its autotetraploid and highly heterozygous genome. Bacterial wilt caused by the Ralstonia solanacearum species complex (RSSC) is an important disease affecting potato among many economically important crops worldwide. No cultivated potato genotypes have shown a satisfactory level of resistance to bacterial wilt. Nevertheless, resistance can play a crucial role in effective integrated disease management. To understand the genetic landscape of bacterial wilt resistance in cultivated potato, we evaluated the diversity of 194 accessions from the International Potato Centre (CIP) using 9,250 single nucleotide polymorphisms (SNPs) and their associations to the response to bacterial wilt disease evaluated over two independent trials. Twenty-four accessions showed high resistance throughout both trials. Genetic diversity analysis revealed three major clusters whose subgroups were mostly represented by CIP clones derived from common parents. Genome-wide association analyses have shown six major hits: two on chromosome 8, and one on each chromosome 2, 4, 5, and 9. These results facilitate genetic dissection of bacterial wilt resistance and marker-enabled breeding in elite genotypes for potato breeding initiatives.

Potato disease identification using transfer learning approaches

Potato crop is one of the prominent consumed foods by human beings. When potato crops are infected by diseases it affects farmers negatively and to run in a loss. Therefore, early detection of the potato crop disease can play a vital role in minimizing the loss of the farmers. Nowadays, artificial intelligence technologies, more specifically deep learning techniques, provide solutions to many crops disease-related problems. However, training deep learning models requires a high computational power and huge amount of data as they are data hungry models. Also, designing a custom CNN models a difficult task and there are some variations to be considered. To avoid these difficulties, we adopted two pretrained models of DenseNet121 and VGG19 through transfer learning approaches. The achieved accuracy for DenseNet121 and VGG19 models are 82.6% and 98.56% respectively. DenseNet121 model obtained the average precision, sensitivity, and F1-score of 88.19%, 82.53, and 82.04%, respectively. Whereas VGG19 yields 98.39% of precision, 98.39% of sensitivity, and 97.26% of F1-score in ternary 3-class classification (early blight vs healthy vs late blight).

An effective unsupervised domain adaptation for in-field potato disease recognition

Accurate disease recognition through computer vision is crucial for the intelligent management of potato production. Popular data-driven classification methods face challenges including limited labelled data and poor model portability. Unsupervised Domain Adaptation (UDA) addresses these challenges with a novel learning strategy. However, the complex field environment introduces a significant domain shift problem due to varying conditions. Existing UDA methods usually concentrate on aligning global data distribution and employ a single structure for disease feature extraction, thereby limiting their efficacy in true field environment. To tackle this challenge of potato disease recognition, the Multi-Representation Adaptive Network (MRSAN) based on subdomain alignment is presented. MRSAN effectively aligns feature distributions across diverse data by minimising distribution differences among relevant subdomains. Simultaneously, the multi-representation extraction method captures finer details from various perspectives in the disease images. The combination of these two approaches efficiently mitigates the adverse effects caused by various interference factors in field environment. Based on the acquisition conditions of light variation and disease progression, two field potato disease image datasets are created, containing five and six kinds of potato leaf disease, respectively. Extensive transfer experiments are conducted on the two datasets. MRSAN achieves average classification accuracies of 87.03% and 80.06% on the datasets for the corresponding transfer tasks, outperforming the other compared methods. This not only validates the effectiveness of MRSAN but also demonstrates its robust ability to generalise across changes in regard to light variation and disease progression.

Potato and Maize Plant Disease Detection Using Leaf Images

Plant diseases represent a serious threat to national productivity and global food security. Effective therapy for multiple diseases requires a precise and useful differentiation of them. In this work, a computerized system for the identification and categorization of diseases in potato and maize crops is developed using convolutional neural networks. The demonstration was created with the ResNet50V2 model and tested on a combined collection of images of leaves. The system achieved an astounding accuracy of 85.19. Enhancing model execution through exchange learning, fine-tuning, and information augmentation were all part of the process. With the use of another dataset, the trained model was verified and produced positive results, almost exactly differentiating between the disease-causing leaf type (potato or maize). This technology helps ranchers adopt sustainable and knowledgeable disease management methods by promoting timely mediations, which in turn advances disease discovery.

Potato Leaf Disease Detection Based on a Lightweight Deep Learning Model

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management.

A CARS-SPA-GA Feature Wavelength Selection Method Based on Hyperspectral Imaging with Potato Leaf Disease Classification.

Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (CARS) and the successive projection algorithm (SPA), respectively. Then, the two sets of wavelengths were reorganized and duplicates were removed, and secondary feature selection was conducted with genetic algorithm (GA). Finally, the feature wavelengths were fed into different classifiers and the parameters were optimized using a real-coded genetic algorithm (RCGA). The experimental results show that the feature wavelengths selected by the CARS-SPA-GA method accounted only for 9% of the full band, and the classification accuracy of the RCGA-optimized support vector machine (SVM) classification model reached 98.366%. These results show that it is feasible to classify early blight and ladybug beetle infestation in potato using visible/near-infrared spectral data, and the CARS-SPA-GA method can substantially improve the accuracy and detection efficiency of potato pest and disease classification.

A multi-source domain feature adaptation network for potato disease recognition in field environment.

Accurate identification of potato diseases is crucial for reducing yield losses. To address the issue of low recognition accuracy caused by the mismatch between target domain and source domain due to insufficient samples, the effectiveness of Multi-Source Unsupervised Domain Adaptation (MUDA) method in disease identification is explored. A Multi-Source Domain Feature Adaptation Network (MDFAN) is proposed, employing a two-stage alignment strategy. This method first aligns the distribution of each source-target domain pair within multiple specific feature spaces. In this process, multi-representation extraction and subdomain alignment techniques are utilized to further improve alignment performance. Secondly, classifier outputs are aligned by leveraging decision boundaries within specific domains. Taking into account variations in lighting during image acquisition, a dataset comprising field potato disease images with five distinct disease types is created, followed by comprehensive transfer experiments. In the corresponding transfer tasks, MDFAN achieves an average classification accuracy of 92.11% with two source domains and 93.02% with three source domains, outperforming all other methods. These results not only demonstrate the effectiveness of MUDA but also highlight the robustness of MDFAN to changes in lighting conditions.

Effects of Paecilomyces lilacinus and Bacillus pumilus on stem nematode and rhizosphere bacterial communities of sweet potato

Stem nematode (Ditylenchus destructor Thorne) is considered one of the most economically devastating species affecting sweet potato production. Biocontrol offers a sustainable strategy for nematode control. This study conducted a pot experiment to evaluate the biocontrol efficacy of Paecilomyces lilacinus CS-Z and Bacillus pumilus Y-26 against the stem nematode, as well as to examine their influence on the bacterial communities in the sweet potato rhizosphere. The findings indicated that B.pumilus Y-26 and P.lilacinus CS-Z exhibited respective suppression rates of 82.9% and 85.1% against the stem nematode, while also stimulating sweet potato plant growth. Both high-throughput sequencing and Biolog analysis revealed distinct impacts of the treatments on the bacterial communities. At the phylum level, B.pumilus Y-26 enhanced the abundance of Actinobacteria but reduced the abundance of Cyanobacteria, with P.lilacinus CS-Z exhibiting similar effects. Additionally, the treatment with B.pumilus Y-26 resulted in increased abundances of Crossiella, Gaiella, Bacillus, and Streptomyces at the genus level, while the treatment with P.lilacinus CS-Z showed increased abundances of Crossiella and Streptomyces. In contrast, the abundance of Pseudarthrobacter was reduced in the treatment with B.pumilus Y-26. Conversely, the application of the nematicide fosthiazate exhibited minor influence on the bacterial community. The findings indicated that the application of P.lilacinus CS-Z and B.pumilus Y-26 led to an increase in the relative abundances of beneficial microorganisms, including Gaiella, Bacillus, and Streptomyces, in the rhizosphere soil. In conclusion, P.lilacinus CS-Z and B.pumilus Y-26 demonstrated their potential as environmentally friendly biocontrol agents for managing stem nematode disease of sweet potato.

Potato disease detection and control measure recommendation using CNN

Potato cultivation will continue to be a crucial agricultural endeavor, significantly enrich to global food supply. and economic prosperity. However, the ongoing vulnerability of potatoes to diseases, particularly named early Blight and late Blight, will persist as a substantial threat to crop yield and quality. Traditional disease identification methods, reliant on expert visual inspection, are expected to remain time-consuming, error-prone, and inadequate for timely intervention. This paper will outline to find Potato diseases and recommend effective control measures using convolutional Neural-Networks (CNN). The project will adopt an object-oriented methodology to ensure modularity and maintainability, leveraging TensorFlow for comprehensive dataset collection, data cleaning, and model training. TensorFlow Lite will be applied to optimize and quantify the developed model, enhancing the efficiency of the deployment process. The frontend will be crafted with React Native to facilitate user accessibility and seamless interaction. The resulting deep learning technique is anticipated to exhibit high accuracy in identifying early-blight and late-blight in potato plants, enabling users to receive prompt and reliable disease predictions and actionable control suggestions. This innovative approach aims to downgrade crop losses, improve productivity, and foster sustainable agricultural practices, thereby bolstering global food security in the future.

New isolate of sweet potato virus 2 from Ipomoea nil: molecular characterization, codon usage bias, and phylogenetic analysis based on complete genome

BackgroundViral diseases of sweet potatoes are causing severe crop losses worldwide. More than 30 viruses have been identified to infect sweet potatoes among which the sweet potato latent virus (SPLV), sweet potato mild speckling virus (SPMSV), sweet potato virus G (SPVG) and sweet potato virus 2 (SPV2) have been recognized as distinct species of the genus Potyvirus in the family Potyviridae. The sweet potato virus 2 (SPV2) is a primary pathogen affecting sweet potato crops.MethodsIn this study, we detected an SPV2 isolate (named SPV2-LN) in Ipomoea nil in China. The complete genomic sequence of SPV2-LN was obtained using sequencing of small RNAs, RT-PCR, and RACE amplification. The codon usage, phylogeny, recombination analysis and selective pressure analysis were assessed on the SPV2-LN genome.ResultsThe complete genome of SPV2-LN consisted of 10,606 nt (GenBank No. OR842902), encoding 3425 amino acids. There were 28 codons in the SPV2-LN genome with a relative synonymous codon usage (RSCU) value greater than 1, of which 21 end in A/U. Among the 12 proteins of SPV2, P3 and P3N-PIPO exhibited the highest variability in their amino acid sequences, while P1 was the most conserved, with an amino acid sequence identity of 87-95.3%. The phylogenetic analysis showed that 21 SPV2 isolates were clustered into four groups, and SPV2-LN was clustered together with isolate yu-17-47 (MK778808) in group IV. Recombination analysis indicated no major recombination sites in SPV2-LN. Selective pressure analysis showed dN/dS of the 12 proteins of SPV2 were less than 1, indicating that all were undergoing negative selection, except for P1N-PISPO.ConclusionThis study identified a sweet potato virus, SPV2-LN, in Ipomoea nil. Sequence identities and genome analysis showed high similarity between our isolate and a Chinese isolate, yu-17-47, isolated from sweet potato. These results will provide a theoretical basis for understanding the genetic evolution and viral spread of SPV2.

Analysis of soft rot Pectobacteriaceae population diversity in US potato growing regions between 2015 and 2022.

Soft rot Pectobacteriaceae (SRP) bacteria are globally dispersed pathogens that cause significant economic loss in potato and other crops. Our understanding of the SRP species diversity has expanded in recent years due to advances and adoption of whole-genome sequence technologies. There are currently 34 recognized SRP species that belong to the Dickeya and Pectobacterium genera. We used whole-genome sequencing based analysis to describe the current distribution and epidemiology of SRP isolated from diseased potato samples obtained from commercial potato cropping systems in the United States. Our primary objectives in the present study were to: (1) identify the species of these SRP isolates recovered from potato samples across 14 states in the US, (2) describe the variation among SRP isolates from various US locations and track their temporal changes, and (3) evaluate the evolutionary relationships among these SRP isolates to deduce their source. We collected 118 SRP strains from diseased potato plants and tubers in 14 states between 2015 and 2022. We identified three Dickeya and eight Pectobacterium species from diseased potato samples. Dickeya dianthicola, Pectobacterium parmentieri, P. carotovorum, and P. versatile appeared to be the predominant species, constituting 83% of the isolates. Furthermore, all D. dianthicola strains studied here as well as 90% of US D. dianthicola isolates sequenced to date exhibit significant clonality. The prevalence of this specific group of D. dianthicola, temporally and geographically, aligns with the occurrence of blackleg and soft rot outbreaks in the northeastern US after 2014. The genomic diversity observed in P. parmentieri implies multiple introductions to the US from at least four distinct sources, earlier than the arrival of the predominant group of D. dianthicola. In contrast, P. carotovorum and P. versatile appear to be widespread, long-term endemic strains in the US.

Phytocytokine StPep1-Secreting Bacteria Suppress Potato Powdery Scab Disease.

Powdery scab is an important potato disease caused by the soilborne pathogen Spongospora subterranea f. sp. subterranea. Currently, reliable chemical control and resistant cultivars for powdery scab are unavailable. As an alternative control strategy, we propose a novel approach involving the effective delivery of a phytocytokine to plant roots by the rhizobacterium Bacillus subtilis. The modified strain is designed to secrete the plant elicitor peptide StPep1. In our experiments employing a hairy root system, we observed a significant reduction in powdery scab pathogen infection when we directly applied the StPep1 peptide. Furthermore, our pot assay, which involved pretreating potato roots with StPep1-secreting B. subtilis, demonstrated a substantial decrease in disease symptoms, including reduced root galling and fewer tuber lesions. These findings underscore the potential of engineered bacteria as a promising strategy for safeguarding plants against powdery scab.

Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection

Potato crop has become integral part of our diet due to its wide use in variety of dishes, making it an important food crop. Its importance also stems from the fact that it is one of the cheapest vegetables available throughout the year. This makes it crucial to keep potato prices affordable for developing countries where the majority of the population falls under the middle-income bracket. Consequently, there is a need to develop a robust, effective, and portable technique to detect diseases in potato plant leaves. In this work, an attention-based disease detection technique is proposed. This technique selectively focuses on specific parts of an image which reveal the disease. This technique leverages transfer learning combined with two attention modules: the channel attention module and spatial attention module. By focusing on specific parts of the images, the proposed technique is able to achieve almost similar accuracy with significantly fewer parameters. The proposed technique has been validated using four pre-trained models: DenseNet169, XceptionNet, MobileNet, and VGG16. All of these models are able to achieve almost the same level of training and validation accuracy, around 90–97%, even after reducing the number of parameters by 40–50%. It shows that the proposed technique effectively reduces model complexity without compromising performance.

Выявление патогенов картофеля с использованием различных методов диагностики

Abstract. Potato diseases have far reaching implications for the agricultural industry as they not only reduce productivity and quality of potato crop but also negatively affect the livelihood of farmers. The purpose of the studies detection and identification of potato phytopathogens by visual assessment, real time polymerase chain reaction (PCR) and molecular genetic screening for phytopathogen resistance genes. Methods. Visual evaluation of each potato variety for major fungal, viral and bacterial diseases was carried out during the period of full sprouting, at plant height of 15–20 cm and in the phase of full flowering. The percentage of plants affected by diseases in relation to the total number of inspected plants was calculated. Diagnosis of resistance of potato samples to diseases was carried out on the basis of PCR. DNA markers of phytopathogen resistance genes YES3-3A, RYSC3, Ry186, TG 689, Gro1-4-1, Gpa2-2, PVX were used for molecular screening of potato. Infection of samples by PCR-RV (real-time) was determined using reagent kits of Syntol LLC Potato Virus X and Potato Virus Y, Potato spindle tuber viroid-PB, Clavibacter michiganensis subsp. sepedonicus-PB, Dickeya spp-PB. Results. According to the results of visual evaluation, the absence of symptoms of wrinkle mosaic in 20% of hybrids, mottle mosaic in 40%, mosaic leaf curl in 96 %, leaf curl in 72 hybrids, black leg in 100%, phytophthorosis in 88% of hybrids, alternaria in 84 %, rhizoctoniosis in 96 % of hybrids were observed. According to the results of PCR-RV analysis in 21 samples detected virus Y. The scientific novelty consists in the diagnosis of potato viruses using traditional visual assessment with the use of molecular markers that allow to identify correlation relationship between trhis methods.

Morphological and molecular identification of Fusarium incarnatum as the causal agent of potato dry rot disease

Morphological and molecular identification of Fusarium incarnatum as the causal agent of potato dry rot disease

Synergistic effects of mycorrhiza, effective microorganisms, compost tea, and mineral fertilizers on potato growth and disease management

Synergistic effects of mycorrhiza, effective microorganisms, compost tea, and mineral fertilizers on potato growth and disease management

Effects of application methods of microbial mixture on early blight disease (Alternaria solani) and growth of potato

Early blight (Alternaria solani) is one of important diseases in potato in Indonesia. In previous study, a microbial consortium (Trichoderma harzianum, Bacillus subtilis and Pseudomonas sp.) suppressed the bacterial wilt disease in potato. For application, the microbial consortium was mixed with compost (10%). As the microbial consortium is originally intended for controlling soil-borne diseases, this study examined the ability of the microbial consortium to suppress early blight disease and the effects of application methods on its efficacy. The application methods examined were the methods that targeted for soil-borne as well as airborne diseases. The experiment was conducted in a greenhouse at the Department of Plants and Diseases, Faculty of Agriculture, Universitas Padjadjaran, Sumedang, West Java, Indonesia. The experiment used Randomized Complete Block Design with 10 treatments and three replications. The treatments included application of microbes mixed with compost in planting hole solely or in combination with soaking the potato tubers in suspension of the microbes, drenching water extract of the mixture at 2, 4, 6 weeks after planting (WAP) with or without foliar application of the water extract. The result showed that application method influenced the abilities of the microbial mixture in suppressing early blight disease and supporting the potato growth. Even though the application of microbe-compost in planting medium inhibited the development of early blight disease by 37.8-39.5%, the regular foliar spray of the water extract of the mixture enhanced the disease suppression. Combination between application of the microbe-compost in the planting hole, drenching its water extract at 2, 4, 6 WAP and weekly foliar spray of the water extract suppressed early blight disease by 80.8%. This combination of application methods also supported better plant height, numbers of potato leaflets, roots fresh weight and production of potato tuber.

Utilization of Secondary Raw Materials from Rice and Buckwheat Processing for the Production of Enriched Bread: Optimization of Formulation, Physicochemical and Organoleptic Properties, Structural and Mechanical Properties, and Microbiological Safety.

Pursuing enhanced nutritional value in bakery products through technological advancements and new recipes is a promising facet of the food industry. This study focuses on incorporating rice and buckwheat brans, additional raw materials rich in biologically active substances, into bakery products. Utilizing a second-order rotatable plan, optimal ratios were determined-5% rice bran and 10% buckwheat bran. The application of these brans influenced dough and bread quality, reducing sugar content by 5% in dry form and 29% in the fermented brew, potentially aiding in diabetes prevention and cholesterol control. Introducing brans, especially in fermented brew, positively impacted microbiological stability, reducing the risk of mold and potato disease. The developed bread technology using rice and buckwheat brans in fermented brew significantly increased nutritional value, satisfying adult daily protein needs by 31.2%, fats by 15%, and dietary fibers by 18.4%. This innovative approach ensures a sufficient intake of essential vitamins and minerals, showcasing a promising avenue for creating healthier and more nutritious bakery products.

The Influence of Chitosan Derivatives in Combination with Bacillus subtilis Bacteria on the Development of Systemic Resistance in Potato Plants with Viral Infection and Drought.

Viral diseases of potatoes are among the main problems causing deterioration in the quality of tubers and loss of yield. The growth and development of potato plants largely depend on soil moisture. Prevention strategies require comprehensive protection against pathogens and abiotic stresses, including modeling the beneficial microbiome of agroecosystems combining microorganisms and immunostimulants. Chitosan and its derivatives have great potential for use in agricultural engineering due to their ability to induce plant immune responses. The effect of chitosan conjugate with caffeic acid (ChCA) in combination with Bacillus subtilis 47 on the transcriptional activity of PR protein genes and changes in the proteome of potato plants during potato virus Y (PVY) infection and drought was studied. The mechanisms of increasing the resistance of potato plants to PVY and lack of moisture are associated with the activation of transcription of genes encoding PR proteins: the main protective protein (PR-1), chitinase (PR-3), thaumatin-like protein (PR-5), protease inhibitor (PR-6), peroxidase (PR-9), and ribonuclease (PR-10), as well as qualitative and quantitative changes in the plant proteome. The revealed activation of the expression of marker genes of systemic acquired resistance and induced systemic resistance under the influence of combined treatment with B. subtilis and chitosan conjugate indicate that, in potato plants, the formation of resistance to viral infection in drought conditions proceeds synergistically. By two-dimensional electrophoresis of S. tuberosum leaf proteins followed by MALDI-TOF analysis, 10 proteins were identified, the content and composition of which differed depending on the experiment variant. In infected plants treated with ChCA, the synthesis of proteinaceous RNase P 1 and oxygen-evolving enhancer protein 2 was enhanced in conditions of normal humidity, and 20 kDa chaperonin and TMV resistance protein N-like was enhanced in conditions of lack of moisture. The virus coat proteins were detected, which intensively accumulated in the leaves of plants infected with potato Y-virus. ChCA treatment reduced the content of these proteins in the leaves, and in plants treated with ChCA in combination with Bacillus subtilis, viral proteins were not detected at all, both in conditions of normal humidity and lack of moisture, which suggests the promising use of chitosan derivatives in combination with B. subtilis bacteria in the regulation of plant resistance.