Phytophthora Infestans Research Articles

Management Strategies for Early Blight in Potatoes: Assessment of the TOMCAST Model, Including the Aerobiological Risk Level and Critical Phenological Period

The use of pesticides is an efficient approach for pest management. However, their increasing application in recent decades has come under the spotlight of world policies. In this context, this study addresses the usefulness of a forecasting model (TOMCAST) combined with aerobiological information and a plant development model (physiological days, PDays) for the control of early blight in potatoes in Northwest Spain. Control plots were compared to treated plots, according to the original TOMCAST model and the daily Alternaria spp. concentration, meteorological factors, and phenological and epidemiological observations were monitored for better adjustment of the TOMCAST model to the weather conditions of the geographical area during three crop seasons. The results of the linear regression analysis showed a strong relationship between the parameters included in TOMCAST (leaf wetness and temperature) and the Alternaria spp. conidia concentration. In addition, an unbalanced pattern of trapped conidia was detected throughout the growing season, with an increase near the flowering stage. The epidemiological parameters (infection period, r-AUDPC, maximum severity value, and total and commercial yields) showed significant differences between the cultivars in the control and the TOMCAST plots in terms of r-AUDPC and the maximum severity value. Given the study’s results, the original TOMCAST model was improved with aerobiological and phenological information. The improved model recommends a first spray on a day when the following three requirements are met: Ten accumulated disease severity values (DSVs) according to the TOMCAST model, two days with an aerobiological level greater than 10 conidia/m3, and a PDays value greater than 200. This will reduce the number of fungicide treatments used to control early blight in potato crops, promoting the principles of sustainable agriculture.

Assessment of Resistance to Late Blight (Phytophthora infestans (Mont.) de Bary) in Chilean Native Potatoes (Solanum tuberosum Chilotanum group)

Assessment of Resistance to Late Blight (Phytophthora infestans (Mont.) de Bary) in Chilean Native Potatoes (Solanum tuberosum Chilotanum group)

Effect of chitosan conjugates with oxycinnamic acids and <i>Bacillus subtilis</i> bacteria on the activity of protective proteins and resistance of potato plants to <i>Phytophthora infestans</i>

The effect of chitosan conjugates with caffeic (ChCA) and ferulic (ChFA) acids in combination with Bacillus subtilis bacteria on the transcriptional activity of PR protein genes and proteome changes in potato plants during infection with Phytophthora infestans (Mont.) de Bary was studied. Plants grown from mini tubers of the Luck variety were sprayed with solutions of ChCA and ChFA, suspension of B. subtilis bacteria strains 26D and 11 VM, composites of ChCA of ChFA together with bacteria. 3 days after treatment, some of the plants were infected with P. infestans. A decrease in the degree of development of the pathogen of late blight on potato leaves in all treatment options was revealed. The maximum protective effect was manifested when plants were treated with bacteria B. subtilis strain 26D in combination with conjugates of chitosan and oxycinnamic acids. The mechanisms of increasing the resistance of potato plants to P. infestans were associated with the activation of transcriptional activity of genes encoding the main protective protein (PR‑1), chitinase (PR‑3), thaumatin-like protein (PR‑5), protease inhibitor (PR‑6), peroxidase (PR‑9), ribonuclease (PR‑10). The revealed activation of the expression of marker genes of systemic acquired resistance and induced systemic resistance under the influence of joint treatment of plants with B. subtilis and chitin conjugates with oxycinnamic acids indicate the synergistic development of protective reactions in potato plants in this variant. By the method of two-dimensional electrophoresis of S. tuberosum leaf proteins followed by MALDI-TOF analysis, 12 proteins were identified, the presence of which in the leaves differed depending on the variant of the experiment. In all treatment variants, suppression of serine-threonine protein phosphatase activity was observed, reflecting the development of the hypersensitivity reaction. Different variants of the experiment formed weakly expressed clusters, which indicates multiple mechanisms of regulation of the synthesis of protective proteins involved in the reaction to treatment with bacteria, chitosan conjugates and infection with P. infestans.

Activity of OSBPI fungicide fluoxapiprolin against plant-pathogenic oomycetes and its systemic translocation in plants

Fluoxapiprolin, a novel piperidinyl thiazole isoxazoline fungicide, was developed by Bayer Crop Science in 2012. Despite its well-documented inhibitory activity against plant pathogenic oomycetes such as Phytophthora capsici and Phytophthora infestans, limited information regarding its antifungal spectrum and protective and curative activity is available. Fluoxapiprolin exhibited strong inhibitory activity against Phytophthora spp. and several Pythium spp., with EC50 values ranging from 2.12 × 10−4 to 2.92 μg/mL. It was much more effective against P. capsici in inhibiting mycelial growth, sporangium production, and cystospore germination than at reducing zoospore release. Moreover, fluoxapiprolin displayed both protective and curative activity against P. capsici infection in pepper plants under greenhouse conditions, with systemic translocation capability confirmed by High Performance Liquid Chromatography (HPLC) analysis. The results demonstrated the strong inhibitory activity of fluoxapiprolin against economically important plant oomycete pathogens, including Phytophthora spp. and several Pythium spp., and its certain translocation activity in pepper plants.

Evaluation of Fungicide Frequency and Rotation for Tomato Late Blight Management in Rain-Fed and Irrigated Conditions

Evaluation of Fungicide Frequency and Rotation for Tomato Late Blight Management in Rain-Fed and Irrigated Conditions

Opportunities to Narrow Potato Yield Gaps and Increase Resource Use Efficiency in West Java, Indonesia

AbstractConsumer demand for potato in Indonesia increases while production stagnates. Government policy recognised this discrepancy and prioritised efforts to increase potato yields through farmer support programs with moderate effects to date. This study aimed to decompose potato yield gaps and identify agronomic yield constraints in West Java. Actual yield and management practices of 178 farm registrations over five consecutive growing seasons were analysed with stochastic frontier analysis. The potential yield (Yp) for each registration was simulated with the LINTUL-POTATO-DSS crop model. The overall yield gap was decomposed into its efficiency, resource and technology components. The potato yield gap was 21.5 t ha−1, corresponding to 54% of Yp. The efficiency and technology yield gaps were both ca. 10 t ha−1, corresponding to 26% of Yp, and the resource yield gap was negligible. All fields received sufficient inputs for potential production, but most inputs were used inefficiently. An early first fungicide spray after emergence and frequently spraying to control late blight increased yields significantly. The seed generation planted was critical to potato yield gaps as yield decreased significantly with older generations. Some high-yielding fields, however, were also planted with older seed generations, so other production factors contributed to the yield gap as well. Improving potato yield and reducing current yield gaps in West Java will require well-performed late blight control, the use of high-quality seed and improved management practices, which include reduced input rates.

Anti‐Phytopathogenic and Phytotoxic Effects of Annona glabra Lin. and Annona muricata Lin. Seed Extracts: In vitro and In vivo Assessment, Bioactive Compound Quantification, and Mechanism Involved

AbstractBuilding upon prior research demonstrating the excellent antifungal and nematicidal efficacy of Annona squamosa Lin. seed extracts, this study extends its scope to investigate similar properties in seed extracts from two other Annonaceae species: Annona muricata Lin. and Annona glabra Lin. The focus is on assessing their effectiveness against tomato late blight (TLB) and wheat leaf rust (WLR) diseases, as well as their broader antifungal activity against Colletotrichum sp. and Fusarium sp. The A. glabra seed extract exerted a superior control efficacy against TLB (85 % at 62.5 μg/mL) and WLR (75 % at 125 μg/mL) in vivo. The two Annonaceous extracts also exhibited in vitro antifungal inhibition against Colletotrichum sp. and Fusarium sp., as well as toxicology against pine wilt nematodes and Artemia salina. Besides, they showed phytotoxicity against Raphanus sativus seed germination. Squamocin G, a potent bioactive acetogenin, was isolated from A. glabara, which was found to occur at high contents in the following ascending order in A. glabra, A. muricata and A. squamosa dichloromethane extracts. To investigate the antifungal mechanism, molecular docking and molecular dynamic of squamocin G ligand were also performed for the first time at the active site of succinate dehydrogenase and β‐tubulin.

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.

Evolution of Phytophthora infestans on its potato host since the Irish potato famine

Phytophthora infestans is a major oomycete plant pathogen, responsible for potato late blight, which led to the Irish Potato Famine from 1845–1852. Since then, potatoes resistant to this disease have been bred and deployed worldwide. Their resistance (R) genes recognize pathogen effectors responsible for virulence and then induce a plant response stopping disease progression. However, most deployed R genes are quickly overcome by the pathogen. We use targeted sequencing of effector and R genes on herbarium specimens to examine the joint evolution in both P. infestans and potato from 1845–1954. Currently relevant effectors are historically present in P. infestans, but with alternative alleles compared to modern reference genomes. The historic FAM-1 lineage has the virulent Avr1 allele and the ability to break the R1 resistance gene before breeders deployed it in potato. The FAM-1 lineage is diploid, but later, triploid US-1 lineages appear. We show that pathogen virulence genes and host resistance genes have undergone significant changes since the Famine, from both natural and artificial selection.

Effect of foliar application of plant growth regulators on incidence of insect-pests and diseases in autumn planted seed potato (Solanum tuberosum)

A field experiment was conducted during 2019–20 and 2020–21 at Punjab Agricultural University, Ludhiana, Punjab to study the effect of foliar application of plant growth regulators on insect-pest and disease incidence in potato (Solanum tuberosum L.) under subtropical conditions. A total of 11 treatments including control were tested in a randomized complete block design (RCBD) with three replications on the potato cv. Kufri Pukhraj. The results indicated that plant growth regulators, like GA3, IBA, NAA, ethrel, jeevamrit and waste decomposer were not producing any significant effect on the incidence of aphid, jassid and whitefly population in comparison to untreated control. However, diseases such as scab and scurf did not reveal any significant differences among the plant growth regulator’s treatments except in late blight. Foliar application of gibberellic acid (200 ppm) at 45 and 60 days after sowing (DAS) significantly reduced the late blight incidence and severity in seed potato cv. Kufri Pukhraj.

Population Structure of Phytophthora infestans in Israel Changes Frequently Due to the Import of Asymptomatic Late Blight-Infected Potato Seed Tubers from Europe.

Late blight, caused by the oomycete Phytophthora infestans, is a devastating disease of potato worldwide. In Israel, potatoes are grown twice a year, in autumn and spring, with late blight causing extensive damage in both seasons. While tuber seeds for the autumn planting are produced locally, seed tubers for the spring planting are imported from Europe due to dormancy of local tubers. Here, we demonstrate that seed tubers imported from Europe for the spring season carry asymptomatic infection with EU genotypes of P. infestans, which alters the population structure of the pathogen each spring. The proportion of imported tubers carrying asymptomatic infections ranged between 1.2 and 3.75%, varying by year and cultivar. Asymptomatic tubers produced late blight-infected sprouts about one month after planting. The sporangia produced on these sprouts served as primary inoculum, causing intensive foliage attacks on neighboring plants. When sprout-infected plants were uprooted and the mother tuber was washed, sliced, and placed in moistened dishes at 18 °C, profuse sporulation of P. infestans developed on the slices' surfaces within 1-2 days. The dominant genotype of P. infestans in the autumn season in Israel is 23A1, but genotypes in the following spring season changed to include 13A2 or 36A2. Surprisingly, genotype 43A1, which might be resistant to CAA and OSBPI fungicides and appeared in Europe in 2022, emerged in Israel in spring 2024. The immigrating genotypes do not persist in the country, allowing 23A1 to regain predominance in the following autumn. Long-term monitoring data suggest that the population structure of P. infestans changes yearly but temporarily due to the import of new genotypes from Europe.

GamaNNet: A Novel Plant Pathologist-Level CNN Architecture for Intelligent Diagnosis

Plant pathologies significantly jeopardise global food security, necessitating the development of prompt and precise diagnostic methods. This study employs advanced deep learning techniques to evaluate the performance of nine convolutional neural networks (CNNs) in identifying a spectrum of phytosanitary issues affecting the foliage of Solanum lycopersicum (tomato). Ten thousand RGB images of leaf tissue were subsampled in training (64%), validation (16%), and test (20%) sets to rank the most suitable CNNs in expediting the diagnosis of plant disease. The study assessed the performance of eight well-known networks under identical hyperparameter conditions. Additionally, it introduced the GamaNNet architecture, a custom-designed model optimised for superior performance on this specific type of dataset. The investigational results were most promising for the innovative GamaNNet and ResNet-152, which both exhibited a 91% accuracy rate, as evidenced by their confusion matrices, ROC curves, and AUC metrics. In comparison, LeNet-5 and ResNet-50 demonstrated lower assertiveness, attaining accuracies of 74% and 69%, respectively. GoogLeNet and Inception-v3 emerged as the frontrunners, displaying diagnostic preeminence, achieving an average F1-score of 97%. Identifying such pathologies as Early Blight, Late Blight, Corynespora Leaf Spot, and Septoria Leaf Spot posed the most significant challenge for this class of problem.

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

Rapid Detection of Viral, Bacterial, Fungal, and Oomycete Pathogens on Tomatoes with Microneedles, LAMP on a Microfluidic Chip, and Smartphone Device.

Rapid detection of plant diseases before they escalate can improve disease control. Our team has developed rapid nucleic acid extraction methods with microneedles and combined these with loop-mediated amplification (LAMP) assays for pathogen detection in the field. In this work, we developed LAMP assays for early blight (Alternaria linariae, A. alternata, and A. solani) and bacterial spot of tomato (Xanthomonas perforans) and validated these LAMP assays and two previously developed LAMP assays for tomato spotted wilt virus and late blight. Tomato plants were inoculated, and disease severity was measured. Extractions were performed using microneedles, and LAMP assays were run in tubes (with hydroxynaphthol blue) on a heat block or on a newly designed microfluidic slide chip on a heat block or a slide heater. Fluorescence on the microfluidic chip slides was visualized using EvaGreen and photographed on a smartphone. Plants inoculated with X. perforans or tomato spotted wilt virus tested positive prior to visible disease symptoms, whereas Phytophthora infestans and A. linariae were detected at the time of visual disease symptoms. LAMP assays were more sensitive than PCR, and the limit of detection was 1 pg of DNA for both A. linariae and X. perforans. The LAMP assay designed for early blight detected all three species of Alternaria that infect tomato and is thus an Alternaria spp. assay. This study demonstrates the utility of rapid microneedle extraction followed by LAMP on a microfluidic chip for rapid diagnosis of four important tomato pathogens.

Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn.

Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0's feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model.

Evaluation of Irish Potato Genotypes (<i>Solanum tuberosum</i> L.) Against Diseases for Adaptability in Sierra Leone

Irish Potato is an emerging crop of high economic value in Sierra Leone. Cultivation of the crop is limited, and farmers are faced with numerous problems in the production of the crop. This research was done to identify diseases resistant adaptable sweet potato genotypes under Sierra Leone condition. Six Irish potato genotypes collected from the Futa Jalon highlands in Guinea were evaluated in Kabala and Njala during 2013 and 2014 cropping season. The experiment was laid out in a randomized complete block design with three replications. Data was collected on agronomic parameters as wells as pest and disease. Data was analyzed using analysis of variance. Mean comparison was done using least significant difference (LSD) at 5% probability. Findings revealed that Kabala exhibited higher field establishment rates compared to Moyamba, with Spunta, Arnova, Nicola, and Mandola displaying significantly higher establishment percentages. Similarly, Spunta consistently exhibited the largest leaf area, vine length and highest plant vigor scores across locations. While all genotypes were susceptible to Potato Virus Disease (PVD), bacterial blight, and late blight, Spunta displayed the highest resistance to these diseases. Variations in tuber number and weight per plant were observed, with Spunta (4.4 and 3.6 t/ha) and Mandola (3.5 t/ha) showing the highest yields at both locations respectively. Notably, Spunta consistently outperformed other genotypes in terms of yield across both locations. These findings underscore the importance of genotype selection tailored to local conditions and the need for disease management strategies to enhance potato production and food security in Sierra Leone. Further research focusing on breeding programs targeting disease resistance and yield optimization is warranted to address the productivity challenges faced by Irish potato cultivation in Sierra Leone.

Improvement of potato cultivation technology elements in production conditions

The results of improving potato cultivation technology by increasing the efficiency of plant protection system in the conditions of production experience are presented. The study was conducted in 2021–2023 on the lands of the OOO "Druzhba-2" (Bryansk region, Zhiryatinsky district). Two potato varieties of the first reproduction, Red Scarlett (Netherlands) and Gala (Germany), were the object of the study. The experiment scheme included two variants: 1) traditional potato protection system adopted in the farm (control); 2) improved protection system. Both protection systems are aimed at the prevention of pests (Colorado potato beetle, snap beetle larvae), weeds (cleavers, black nightshade, purple deadnettle, lamb's quarters, wild camomile, annual bluegrass, wind bent grass) and diseases (early blight, late blight, silver scurf, rhizoctoniose, various types of rots, pythium). Soils of the sites are sod-podzolic. The forecrop is winter rapeseed. The total area of the production experiment was 120 ha (60 ha under each variety). Potatoes were grown under intensive technology with conventional tillage system on a 75 × 28 cm pattern. Planting rate is 50 thousand tubers per 1 ha. The technology is designed to obtain tuber yields of 40–60 t/ha. Experimentally it has been established that the application of the improved technology with the use of a new plant protection system for the farm contributes to an increase in plant stand density and their safety at the time of harvesting by 2.5–3.0%, increase in the number of tubers from one plant by 7.5–10.0%, average weight of one tuber – by 7–11%, weight of tubers from one plant – by 14.5–23.0%, yield – by 17–26%, marketability – by 12.5–14.5%, marketable yield – by 32–45%, conditional net income – by 16.2–24.5%.

Discovery of Novel Oxathiapiprolin Derivatives as Potent Fungicide Candidates.

Oxathiapiprolin (OXA), which targets the oxysterol-binding protein (OSBP), is an outstanding piperidinyl thiazole isoxazoline (PTI) fungicide that can be used to control oomycetes diseases. In this study, starting from the structure of OXA, a series of novel OSBP inhibitors were designed and synthesized by introducing an indole moiety to replace the pyrazole in OXA. Finally, compound b24 was found to exhibit the highest control effect (82%) against cucumber downy mildew (CDM) in the greenhouse at a very low dosage of 0.069 mg/L, which was comparable to that of OXA (88%). Furthermore, it showed better activity against potato late blight (PLB) than other derivatives of indole. The computational results showed that the R-conformation of b24 should be the dominant conformation binding to PcOSBP. The results of the present work indicate that the 3-fluorine-indole ring is a favorable fragment to increasing the electronic energy when binding with PcOSBP. Furthermore, compound b24 could be used as a lead compound for the discovery of new OSBP inhibitors.

Potato Leaf Disease Classification Using Optimized Machine Learning Models and Feature Selection Techniques

AbstractThe diseases that particularly affect potato leaves are early blight and the late blight, and they are dangerous as they reduce yield and quality of the potatoes. In this paper, different machine learning (ML) models for predicting these diseases are analysed based on a detailed database of more than 4000 records of weather conditions. Some of the critical factors that have been investigated to determine correlations with disease prevalence include temperature, humidity, wind speed, and atmospheric pressure. These types of data relationships were comprehensively identified through sophisticated means of analysis such as K-means clustering, PCA, and copula analysis. To achieve this, several machine learning models were used in the study: logistic regression, gradient boosting, multilayer perceptron (MLP), and support vector machine (SVM), as well as K-nearest neighbor (KNN) models both with and without feature selection. Feature selection methods such as the binary Greylag Goose Optimization (bGGO) were applied to improve the predictive performance of the models by identifying feature sets pertinent to the models. Results demonstrated that the MLP model, with feature selection, achieved an accuracy of 98.3%, underscoring the critical role of feature selection in improving model performance. These findings highlight the importance of optimized ML models in proactive agricultural disease management, aiming to minimize crop loss and promote sustainable farming practices.