Potato Late Blight Disease Research Articles

Assessing potato cultivars for resistance against late blight under high altitudinal conditions of North Western Himalayas

Different stages in plant development significantly influence the incidence of potato late blight disease in India. In the present study, the disease progression was monitored weekly in five selected potato cultivars viz. Kufri Jyoti, Kufri Giriraj, Gulmarg Special, Shalimar Potato-I, and Gurez local, during the 2018 and 2019 cropping seasons. The severity of potato late blight, caused by Phytophthora infestans, increased with fluctuations in weather variables. Calculations for the area under the disease progress curve (AUDPC) and the rate of disease spread (r-value) at different growth stages of potatoes were used to track disease progression. Notably, the Kufri Jyoti cultivar demonstrated resistance compared to the other cultivars, with lower severity values and susceptibility scale values (48.40%; 5.6 and 4.9) in 2018 and 2019, respectively. The average rate of disease progress was also lower in Kufri Jyoti (0.064 and 0.060) compared to the local cultivar (0.076 and 0.074) in 2018 and 2019, respectively. These observations indicate that weather parameters have a strong positive impact on the incidence of potato late blight.

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.

Prediction of potato late blight disease incidence based on weather variables using statistical and machine learning models: A case study from West Bengal

Late blight is one of the most devastating diseases on potato the world over, including West Bengal, India. The economic and yield losses from outbreaks of potato late blight can be huge. In this article, application of statistical models such as autoregressive integrated moving average (ARIMA), autoregressive integrated moving average with exogenous variables (ARIMAX) in combination with machine learning models such as, neural network auto regression (NNAR), support vector regression (SVR) and classification and regression tree (CART) have been explored to predict the percentage disease index (PDI) of potato late blight in the northern part of West Bengal. Models were developed to predict PDI at 3- and 7-days interval using the weather variables viz., rainfall, maximum and minimum temperature, maximum and minimum relative humidity, and dew point temperature. Among the developed models, CART to predict PDI at 7 days interval was found to be the best fitted model on the basis of least RMSE, MAE and MAPE. The results of decision tree (CART) model showed that dew point temperature had a significant effect on PDI at 7 days interval and the incidence of potato late blight was high when dew point temperature was greater than 12 0C in the preceding week.

Harnessing Plant’s Arsenal: Essential Oils as Promising Tools for Sustainable Management of Potato Late Blight Disease Caused by Phytophthora infestans—A Comprehensive Review

Potato late blight disease is caused by the oomycete Phytophthora infestans and is listed as one of the most severe phytopathologies on Earth. The current environmental issues require new methods of pest management. For that reason, plant secondary metabolites and, in particular, essential oils (EOs) have demonstrated promising potential as pesticide alternatives. This review presents the up-to-date work accomplished using EOs against P. infestans at various experimental scales, from in vitro to in vivo. Additionally, some cellular mechanisms of action on Phytophthora spp., especially towards cell membranes, are also presented for a better understanding of anti-oomycete activities. Finally, some challenges and constraints encountered for the development of EOs-based biopesticides are highlighted.

Yield Loss Assessment of Potato Late Blight Disease in Central Highland Parts of Ethiopia

Yield Loss Assessment of Potato Late Blight Disease in Central Highland Parts of Ethiopia

A Novel Fuzzy C-Means based Chameleon Swarm Algorithm for Segmentation and Progressive Neural Architecture Search for Plant Disease Classification

This study proposed a novel framework for plant leaf disease identification. The proposed model consists of four steps including pre-processing, segmentation, feature extraction, and classification. At first, the unwanted noise and overfitting are removed, and also image contrast level is enhanced. Secondly, the Fuzzy C-Means (FCM) based Chameleon Swarm Algorithm (CSA) named as (FCM-CSA) is used for plant leaf diseased part segmentation. In the third stage, the feature extraction is performed using a fast GLCM feature extraction model. Finally, the Progressive Neural Architecture Search (PNAS) is used for plant leaf disease identification. The experimental investigations are carried out using MATLAB software with the Mendeley database. From this dataset, we have used Apple Cedar Apple Rust (ACAR), Cherry Powdery Mildew (CPM), Corn Common Rust (CCCR), Apple Healthy (AH), Grape Black Rot (GBR), Pepper Bell Bacterial Spot (PBBS), Potato Late Blight (PLB) and Tomato Leaf Mold (TLM) disease images. Different measures such as precision, recall, sensitivity, specificity, and accuracy results are used to validate the performance of the proposed model.

Portable Diffuse Reflectance Spectroscopy of Potato Leaves for Pre-Symptomatic Detection of Late Blight Disease.

We report on the use of leaf diffuse reflectance spectroscopy for plant disease detection. A smartphone-operated, compact diffused reflectance spectrophotometer is used for field collection of leaf diffuse reflectance spectra to enable pre-symptomatic detection of the progression of potato late blight disease post inoculation with oomycete pathogen Phytophthora infestans. Neural-network-based analysis predicts infection with >96% accuracy, only 24 h after inoculation with the pathogen, and nine days before visual late blight symptoms appear. Our study demonstrates the potential of using portable optical spectroscopy in tandem with machine learning analysis for early diagnosis of plant diseases.

Field evaluation of combination fungicides against late blight disease in potato (Solanum tuberosum)

Potato is commercially important vegetable crop in hills of Uttarakhand and grown as an off season. Farmer’s get remunerative return of their potato because of the off season. It was observed that incidence of late blight disease hampered the production and majority of the farmers used mancozeb fungicide which did not provide expected relief from the menace of late blight disease. Accordingly, interventions were made on combination of fungicides to find out their field efficacy against late blight disease of potato on farmer’s field. The result of the experimentation revealed that two spraying of azoxystrobin + tebuconazole @1 ml/litre of water at moderate severity stage of late blight disease proved to be most effective treatment on reduction of incidence, increase in yield and net income over all other treatments in all the three consecutive years. The T3 has been investigated as best treatment in all the respects among various treatments. The summary of the investigation advocates that two spraying of modern combination of fungicides namely, azoxystrobin + tebuconazole even at moderate severity stage of late blight disease gave tremendous impact on its management throughout the crop season. Considering its impact of three years investigation against late blight disease in off season potato, this novel combination of fungicide may be recommended to the farmer's of the country.

Effects of Combination of Copper Fungicide and Plant Activator on Late Blight and Quality Criteria of Potato

Late blight caused by Phytophthora infestans is one of the most important fungal diseases that cause damage to both the green parts and tuber of the potato plant and limits potato cultivation. Systemic fungicides are mostly preferred by farmers in the control of potato late blight. The fact that systemic fungicides cause health problems in terms of human and environmental health has revealed the need for research on control methods that do not threaten environmental health. In recent years, successful results have been obtained in the control of many diseases with plant activators known as Induced Systemic Resistance (ISR) promoters. This study was carried out in the spring season of 2016-2017 to observe the effect of copper fungicide and plant activator (Lactobacillus acidophilus fermentation product) combination on the control of potato late blight disease and some quality criteria of potato tubers in the experimental plots of Ege University Ödemiş Vocational School in Ödemiş district of İzmir province. The experiment was planned as two separate experiments with Marabel and Melody potato cultivars. The experiment was established according to a split-plot experimental design with four replications. In this study, the effect of the treatments on potato quality and yield criteria such as tuber number, tuber width, tuber length, single tuber weight, yield per decare, and the effects of the pesticides on potato late blight disease and disease severity parameters were evaluated. When the data obtained as a result of the study were evaluated, it was determined that the combination application had a positive effect on the control of potato late blight disease in both potato cultivars compared to the control plots. When the quality criteria of potato tubers were evaluated, the highest number of tubers was obtained from the plots where copper fungicide and plant activator (Isr-2000) combination was applied. Significant increase was obtained in potato quality and yield criteria such as tuber width, tuber length, average tuber weight and yield per decare when the combination plots were compared with the control plots. In conclusion, the use of contact fungicides and plant activator combinations in the control of late blight in the spring period within the scope of sustainable agriculture is promising as an application alternative in the control of plant diseases in organic agriculture.

SCREENING OF IMPROVED AND LOCAL CULTIVARS OF POTATO FOR RESISTANCE TO POTATO LATE BLIGHT DISEASE (Phytophthora Infestans)

Potato late blight disease caused by Phytophthora infestans has become an economic disease in the world and particularly in potato growing regions of Cameroon. Four improved (Pamina, Sassy, Desiree, Diaman) and two local (Belo, Banso) cultivars of potatoes were screen in the field and screen house in Regional College of Agriculture and Catholic University laboratory for resistance to potatoes late blight disease (PLBD). These potatoes cultivars were planted on four ridges per plot in the field in randomized complete block design with three replicate. These cultivars were monitored for disease incidence and severity and data was collected. Potatoes cultivars were planted in a screen house and inoculated with spore suspension of P. infestans of 2.5×104 spores / ml of distilled water to evaluate the resistance of the various potatoes cultivars. Screen house and field screening showed significant varietal difference (P≤0.05) in sensitivity to the fungus. The improved and local cultivars of potatoes from field and screen house show varying levels of resistance to (PLBD). All the improved potatoes cultivars (Pamina, Sassy, Desiree and Diaman) were highly susceptible to (PLBD) compared to the local cultivars (Belo, Banso) and improved cultivar Desiree that were susceptible. The highest mean disease severity of (PLBD) of 5.0 % was recorded on cultivars, Pamina, Sassy and Diaman at 12 weeks after planting. Diaman recorded the highest mean area of (PLBD) growth of 11.45 m2 in the screen house experiment. The local and improved potatoes cultivars that were susceptible could be planted in the field by farmer to reduced disease incidence and severity and increase productivity of the crop.

Potato late blight disease detection using convolutional neural network

Potato late blight disease detection using convolutional neural network

In-field classification of the asymptomatic biotrophic phase of potato late blight based on deep learning and proximal hyperspectral imaging

Effective detection of potato late blight (PLB) is an essential aspect of potato cultivation. However, it is a challenge to detect late blight in asymptomatic biotrophic phase in fields with conventional imaging approaches because of the lack of visual symptoms in the canopy. Hyperspectral imaging can capture spectral signals from a wide range of wavelengths also outside the visual wavelengths. Here, we propose a deep learning classification architecture for hyperspectral images by combining 2D convolutional neural network (2D-CNN) and 3D-CNN with deep cooperative attention networks (PLB-2D-3D-A). First, 2D-CNN and 3D-CNN are used to extract rich spectral space features, and then the attention mechanism AttentionBlock and SE-ResNet are used to emphasize the salient features in the feature maps and increase the generalization ability of the model. The dataset is built with 15,360 images (64x64x204), cropped from 240 raw images captured in an experimental field with over 20 potato genotypes. The accuracy in the test dataset of 2000 images reached 0.739 in the full band and 0.790 in the specific bands (492 nm, 519 nm, 560 nm, 592 nm, 717 nm and 765 nm). This study shows an encouraging result for classification of the asymptomatic biotrophic phase of PLB disease with deep learning and proximal hyperspectral imaging.

Investigation of Phytophthora Infestans Causing Potato Late Blight Disease

Phytophthora infestans (Mont.) cause potato late blight disease and can infect of roots, tubers, and shoots of the plant. The majority of significant factors in determining the spread of the potato late blight is the cultivation of infected potato tubers and their presence as agricultural plant debris in the agricultural field. Mycelium, zoospores, Oospores, and sporangia, all these structures can make infection, also Oospores with their thick wall can survive for 3 to 4 years under low temperatures. P. infestans responsible to yield losses may arrive at 100% in optimum condition. There are two types of mating patterns for A1 and A2, and this depends on the spread of each type in different environments. This fungus also contains several genetic patterns that have been identified through molecular analysis. The different genotypes in this fungus led to new challenges to make an unstable guideline for the study of virulence and aggressiveness. Different strategies or methods were used to prevent or control late blight disease, included of chemicals, crop rotation, bio-agents, and resistant plants. There are many advantages or disadvantages to each strategy, but several studies mention that using resistant plants and fungicide together on conditions in suitable times and strategies led to perfect effect. This paper investigated in important factors in the spread of late blight disease, and some challenges in this fungus.

Functional inhibition of the StERF3 gene by dual targeting through CRISPR/Cas9 enhances resistance to the late blight disease in Solanum tuberosum L.

Disease-resistant cultivars are the best solution to get their maximum yield potential and avoid fungicide application. There is no doubt about the contribution, and use of R genes (resistance genes) in resistance development in plants, while S genes (susceptibility genes) also hold a strong position in pathogenesis by resistance repression, and their loss of function contributes to enhanced resistance. Hence, we attempted to knock out the function of the StERF3 gene in potatoes through CRISPR/Cas9-based genome editing and investigated the CRISPR/Cas9 approach as strategic control against late blight disease in potato plants. The StERF3 gene was edited in late blight susceptible cv. Lady Rosetta. Full allelic edited plants were identified through DnpI, and N1aIV mediated restriction digestion and then further analyzed through Indel Detection by Amplicon Analysis. Sequence analysis of targeted plants for indel identification showed full allelic editing. The detached leaf assay of full allelic edited plants demonstrated the role of the StERF3 gene in susceptibility to late blight in potatoes. In planta disease assay also showed reduced, slowed, and delayed disease progression in StERF3-loss-of-function mutants compared to wild-type (control) plants. Less fungal biomass was quantified in knockouts through Real-time qPCR that supported less susceptibility of edited plants to late blight. Besides, relatively high expression of pathogens-related genes, StPR1, and StNPR1, were also observed in StERF3-loss-of-function mutants compared to the corresponding control. The results showed the functional inhibition of StERF3 genes using the CRISPR/Cas9 approach. The functional knockouts (StERF3 gene-edited potato plants) revealed enhanced resistance against Phytophthora infestans, thereby demonstrating the best strategic control for late blight disease in potato plants.

Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops

This article presents the application of supervised learning and image classification for the early detection of late blight disease in potato using convolutional neural network and support vector machine SVM. The study was realized in the Boyacá department, Colombia. An initial dataset is created with the acquisition of a large number of images directly from the crops. These images are pre-processed in order to extract the main characteristics of the late blight disease. A classification model is developed to identify the potato plants as healthy or infected. Several performance, efficiency, and quality metrics were applied in the learning and classification tasks to determine the best machine learning algorithms. Then, an additional data set was used for validation, image classification, and detection of late blight disease in potato crops in the department of Boyacá, Colombia. The results obtained in the AUC curve show that the CNN trained with the data set obtained an AUC equal to 0.97; and the analysis through SVM obtained an AUC equal to 0.87. Future work requires the development of a mobile application with advanced features as a technological tool for precision agriculture that supports farmers with increased agricultural productivity.

Potato late blight (Phytophthora infestans) disease forecasting using an auto-encoded long short-term memory recurrent neural networks in North-Western Algeria

Potato late blight, caused by Phytophthora infestans (Mont.) de Bary, is the most destructive disease that threatens potato producers in North-Western Algeria. In these areas, it is not rare that potato growers miss crucial treatments or apply unnecessary ones during growing seasons. To address this situation, the introduction of a scientific-based decision support system should improve the disease management strategy and reduce growing costs to farmers, consumers, and protect the environment. For this purpose, we set up a supervised deep-learning classification process (long-short-term-memory/autoencoder) that classify and predict early late blight outbreak 30 days ahead of its occurrence based on a set of determinant climatic factors. The classification results of the long short term memory-autoencoders (LSTM’s-AE) regarding our multi-annual potato late blight disease occurrence database confirms that the unimodal BlightT_LSTM-AE model that accounts for temperature is the overall best model and temperature is the key parameter to a valid prediction of early late blight disease outbreak, in all targeted regions.

Evaluation of Essential Oils Against Potato Late Blight (Phytophthora infestans (Mont.) de Bary) at Holleta, Ethiopia

This study was conducted to evaluate essential oils from Croton macrostachyus Hochst. ex Delile, Eucalyptus globulus Labill., Allium sativum L., Cymbopogon citratus (DC.) Stapf, Cymbopogon martini (Roxb.) W.Watson, Rosmarinus officinalis L. and Thymus schimperi Ronniger using four concentrations (l mL, 2 mL, 3 mL, 4 mL) and three sprays against potato (Solanum tuberosum L.) late blight disease. Two improved potato varieties were planted using randomized complete block design with factorial combinations in three replications. Analysis of disease incidence means at 53 and 60 days after planting showed highly significant differences between varieties and among sources of essential oils. Furthermore, the combined effect of essential oils and potato varieties on disease severity showed significant differences. Disease severity was maintained from 25% to 48% (Jalene); 13% to 30% (Gudene) with essential oils while it was maintained between 92% and 95% (Jalene); 37% to 38% (Gudene) for control plants. The lowest disease percentage of disease severity was obtained on plants treated with A. sativum for Jalene variety while on plants sprayed with C. macrostachyus, E. globulus and T. schimperi for Gudene variety. The lowest disease progress rate 0.198/day and 0.162/day as well as values of area under disease progress curve 228% days and 93% days were obtained for varieties Jalene and Gudene, respectively. Maximum reduction in mean disease severity was exhibited by garlic for varieties Jalene (28.3%) and Gudene (16.3%), as compared to the control (93.3% for Jalene and 37.5% for Gudene). Therefore, results indicated the potentials of essential oils to minimize effects of potato late blight disease. Communities involved in potato production could use the current results as base line information to find alternative management options for late blight disease which has no effect on human health and environment. However, further investigation is required on essential oils affordability and availability to farmers across different locations.

Genotypic and Phenotypic Structure of the Population of Phytophthora infestans in Egypt Revealed the Presence of European Genotypes.

Late blight disease of potato and tomato, caused by Phytophthora infestans, results in serious losses to Egyptian and global potato and tomato production. To understand the structure and dynamics of the Egyptian population of P. infestans, 205 isolates were collected from potato and tomato plants during three growing seasons in 2010–2012. The characterization was achieved by mating-type assay, metalaxyl sensitivity assay, and virulence pattern. Additionally, genotyping of 85 Egyptian isolates and 15 reference UK isolates was performed using 12 highly informative microsatellite (SSR) markers David E. L. Cooke and five effector (RxLR) genes. Mating-type testing showed that 58% (118 of 205) of the isolates belonged to mating type A1, 35% (71 isolates) to mating type A2, and the rest 8% (16 isolates) were self-fertile. The phenotype of metalaxyl response was represented as 45% resistant, 43% sensitive, and 12% as intermediate. Structure analysis grouped the 85 identified genotypes into two main clonal lineages. The first clonal lineage comprised 21 isolates belonging to A2 mating type and 8 self-fertile isolates. This clonal lineage was identified as Blue_13 or EU_13_A2. The second main clonal lineage comprised 55 isolates and was identified as EU_23_A1. A single isolate with a novel SSR genotype that formed a distinct genetic grouping was also identified. The effector sequencing showed good correspondence with the virulence data and highlighted differences in the presence and absence of loci as well as nucleotide polymorphism that affect gene function. This study indicated a changing population of P. infestans in Egypt and discusses the findings in the context of late blight management.

Management of Late Blight of Potato caused by Phytophthora infestans through Botanical Aqueous Extracts

Phytophthora infestans causing late blight is one of the most devastating tomato and potato disease that caused considerable yield losses globally including Pakistan. Due to repeated and injudicious synthetic fungicides applications for the control of late blight of potato, the fungicide resistance in P. infestans led to persistence and surveillance late blight of potato. The study was carried out to evaluate the efficacy of Garlic (Allium sativum), Neem (Azadirachta indica), Turmeric (Curcuma longa), Mint (Mentha) at 10%, 20% and 30% concentration as bio-fungicides against late blight of potato. In-vitro effect of plant extracts of A. sativum, A. indica, C. longa and Mentha were evaluated on percent inhibition and radial growth of pathogen. A. sativum and A. indica at 30% concentration was found more effective in minimizing the mycelial growth of P. infestans with inhibition of 58.4% and 43.9% respectively as compared to control. In the greenhouse trial, overall potato late blight disease incidence was minimum 5.81% where A. sativum extract was used followed by A. indica at 30% concentration resulted 8.45% incidence as compared to control 61.18%. Furthermore, the 30% aqueous extracts of A. sativum found highly effective against late blight disease with 15.4% severity, as compared to control with 54.13% disease coverage. Application of A. sativum and A. indica aqueous plant extracts with a concentration of 30% was found most promising and effective measure against late blight pathogen. Study provides environmentally benign mileage in organic vegetable production against hazardous synthetic agro-chemicals.

Identification of potential chemical fungicides with diverse groups of active ingredient for controlling late blight of potato in Bangladesh

Potato is the third important food crop in Bangladesh and Bangladesh ranked as third and seventh in Asia and in the world, respectively. But potato production greatly affected by the late blight of potato caused by an oomycete, Phytophthora infestans (Mont.) de Bary worldwide. Application of need-based fungicides is the key factor to control this disease and to avoid development of fungicide resistance in P. infestans. Experiments were conducted in both in vitro and in field condition to evaluate some selected chemical fungicides to identify the best one to control the late blight disease of potato. The results of in vitro test revealed that all the fungicides inhibited the growth of P. infestans by 94% over control. But the results of field experiment conceded that the lowest late blight severity was found in T10 (Curzate M8) (0.49%) followed by T6 (AcrobateMz) (0.52%), T8 (Micra) (0.59%), T7 (Xtramyl) (0.61%), T3 (Daconyl) (0.84%), T5 (Secure) (1.28%), T9 (Sanoxanyl) (1.43%), T1 (Unilax) (1.83%), T2 (TemperM) (2.58%) and T4 (Amiscore) (6.34%) at 71 DAP while the maximum late blight severity (95.33%) was observed in T0 (water as control). The highest yield was obtained in T10 (Curzate M8) (25.94 t/ha) followed by T6 (AcrobateMz) (25.67 t/ha), T5 (Secure) (25.22 t/ha), T7 (Xtramil) (24.61 t/ha), T8 (Micra) (24.61 t/ha) and T9 (Sanoxanil) (23.67 t/ha) which were statistically similar and the lowest (8.39 t/ha) yield was observed in T0 (water as control). The highest Benefit Cost Ratio (BCR) was obtained from when potato plants were sprayed with T10 (Curzate M8) (1.02) following by T7 (Xtramil) (0.99), T8 (Micra) (0.99), T6 (AcrobateMz) (0.92), T9 (Sanoxanil) (0.90), T5 (Secure) (0.89), T1 (Unilax) (0.80), T3 (Daconil) (0.74), T4 (Amiscore) (0.71) and T2 (TemperM) (0.44) compared to control T0 (water as control) (-0.30). The results indicated that application of these fungicides yielded a benefit of Tk. ranged by 0.44 to 1.02 over the investment of Tk. 1.00. Therefore, identification of a number of potential chemical fungicides might be useful in the alternate use of these fungicides against late blight of potato in the field. Asian Australas. J. Biosci. Biotechnol. 2022, 7 (1), 23-35