Search

Low plant density and weed infestation are major challenges for groundnut production in northern Ghana. A two‐year on‐farm study was conducted to determine the effect of plant density and variety on grain and fodder yields, incidence of leaf spot disease, weed species diversity and biomass. A factorial treatment combination of 6 varieties and 4 plant densities laid in strip plot design with 4 replications was used. The varieties were (early maturity type: Chinese, Yenyewoso, Samnut 23 and late maturity type: Azivivi, Manipinta, Samnut 22). The plants density included 9, 11, 15 and 22 plants/m2. The late maturity varieties recorded higher (p <.05) canopy cover, grain and fodder yields relative to that of the early maturity varieties. The late maturity varieties also recorded the least sedge weed species frequency, density and incidence of leaf spot disease compared with that of the early maturity varieties. The canopy cover, grain and fodder yields increased with increasing plant density. Broadleaf weed species frequency and density, weed biomass, richness, and diversity declined with increasing plant density. Grain yield showed negative and significant correlation with broadleaf weed species frequency, density and weed biomass. The results suggest that both early and late maturity groundnut varieties can be planted at a density of 22 plants/m2 to increase grain and fodder yields and reduce weed species richness, diversity and growth in northern Ghana and similar agro‐ecology in West Africa. [ABSTRACT FROM AUTHOR]

The economic return for peanut (Arachis hypogaea L.) in Ghana is often low due to limitations in the availability of inputs or their adoption, which are needed to optimize yield. Six experiments were conducted in Ghana in 2020 and 2021 to determine the impact of planting date, cultivar, fertilization, pest management practices, and harvest date on peanut yield, financial return, and pest reaction. A wide range of interactions among these treatment factors were often observed for infestations of aphids (Aphis gossypii Glover); groundnut rosette disease (Umbravirus: Tombusviridaee); millipedes (Peridontopyge spp.); white grubs (Schyzonicha spp.); wireworms (Conoderus spp.); termites (Microtermes and Odontotermes spp.); canopy defoliation as a result of early leaf spot disease caused by Passalora arachidicola (Hori) and late leaf spot caused by Nothopassalora personata (Berk. and M. A. Curtis); and the scarification and boring of pods caused by arthropod feeding. Pod yield and economic return increased for the cultivar Chitaochi and Sarinut 2 when fertilizer was applied and when fertilizer was applied at early, mid-, and late planting dates. Pod yield and economic return increased when a combination of locally derived potassium soaps was used for aphid suppression and one additional hand weeding was used in the improved pest management practice compared with the traditional practice without these inputs. Pearson correlations for yield and economic return were negatively correlated for all pests and damage caused by pests. The results from these experiments can be used by farmers and their advisors to develop production packages for peanut production in Ghana. [ABSTRACT FROM AUTHOR]

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively. [ABSTRACT FROM AUTHOR]

Background: Early leaf spot caused by Cercospora arachidicola Hori. is most destructive disease in all the groundnut growing areas of Rajasthan, under severe infestation it cause 30-50% losses in pod yield. Recently, Central Insecticide Board (CIB), Faridabade banned 27 pesticides including some important fungicides which are extensively used in plant disease management. Therefore, in the present investigation, our main emphasis was to find out some new fungicides for management of early leaf spot disease. Methods: Ten fungicides (hexaconazole 5% EC, difenconazole 25% EC, propiconazole 25% EC, tebuconazole 25.9% EC, trifloxystrobin 25% + tebuconazole 50% WG, mancozeb 50% WP, chlorothalonil 75% WP, carbendazim 50% WP, carbendazim 12% + mancozeb 63% WP and captan 70% + hexaconazole 5% WP) were evaluated against early leaf spot pathogen (C. arachidicola) both in lab as well as in field condition. The experiment was conducted at ARS, SKRAU, Bikaner during Kharif-2019 on most popular cv. HNG-69 in RBD design with the application of foliar spray of ten different fungicides at different concentrations against early leaf spot disease and compared with an untreated control. Result: Among all the fungicides used in the present investigation, Tebuconazole 25.9% EC was found most effective in inhibiting the mycelial growth of the pathogen followed by trifloxystrobin 25% + tebuconazole 50% WG. Under field condition, it gave maximum (70.73%) disease control with highest pod yield (31.5 q/ha) and net return (Rs 57,500/ha) when applied as foliar spray at 0.1% concentration followed by trifloxystrobin 25% + tebuconazole 50% WG at 0.2%. These treatments can provide an effective and economical management of early leaf spot disease for groundnut cultivators. [ABSTRACT FROM AUTHOR]

Nothopassalora personata is one of the most economically severe pathogens of peanut in the United States. The fungus primarily relies on wind and rain for dispersal, which has been documented up to 10 m from an inoculum source. Spore traps have been used in a wide variety of pathosystems to study epidemiology, document detection, develop alert systems, and guide management programs. The objective of this study was to use spore traps and N. personata-specific qPCR primers to quantitatively evaluate dispersal of N. personata conidia at distances up to 70 m from an infected peanut field and to examine relationships between quantities captured and weather variables. Impaction spore samplers were placed at 4, 10, 30, 50, and 70 m from peanut fields at the Edisto Research and Education Center (six fields) and commercial peanut fields in Barnwell and Bamberg counties (one field each) from 2020 to 2022. Following initial detection, samples were collected at a 48-, 48-, 72-h interval until harvest. N. personata conidia were detected at all locations and distances, documenting dispersal up to 70 m from an inoculum source. This result is a reminder that volunteer management is crucial when rotating peanut in nearby fields. A model for predicting log spore quantities was developed using temperature and humidity variables. Temperature variables associated with observed sampling periods had a negative correlation with N. personata quantities, whereas parameters of relative humidity and mean windspeed were positively correlated. [ABSTRACT FROM AUTHOR]

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural networks (CNNs) and LSTMs to capture spatial–temporal patterns from the data and improve the model's ability to recognize dynamic features of the disease. In addition, we introduced a Squeeze-and-Excitation (SE) Network attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epoch) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R 2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R 2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the occurrence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers make accurate management decisions and reduce disease impacts through optimal fungicide application timing. [ABSTRACT FROM AUTHOR]

Peanut (Arachis hypogaea L.) is susceptible to leaf spot diseases caused by the fungi Nothopassalora personata and Passalora arachidicola, which can decrease yield substantially. Chlorothalonil is one of the most widely used fungicides to control these diseases but was recently banned in the European Union due to toxicity to amphibians and fish. Dodine is an alternative fungicide with a similar range of activity. However, information about its impact on the peanut physiology is lacking. The objectives of this study were to assess the effect of dodine on leaf photosynthesis, yield, and pod quality of peanut and evaluate the potential of dodine as a replacement of chlorothalonil in the control of leaf spot diseases. A 3‐year field experiment was conducted using Georgia‐06G. Treatments consisted of chlorothalonil at 0.86 kg a.i. ha−1 (high rate), chlorothalonil at 0.43 kg a.i. ha−1 (low rate), dodine at 0.68 kg a.i. ha−1 (high rate), and dodine at 0.34 kg a.i. ha−1 (low rate). Photosynthetic efficiency was altered by fungicide in only a few instances, and a clear trend was not observed. The high rate of dodine resulted in the least defoliation caused by leaf spot of all fungicide treatments. Pod maturity, yield, and grading parameters were not affected by fungicide. Overall, dodine did not impact negatively the photosynthetic activity, pod quality, and yield of peanut. Moreover, this chemistry was efficient in controlling leaf spot diseases under heavy disease pressure; therefore, dodine is a potential replacement of chlorothalonil in the control of leaf spot diseases in peanut. Core Ideas: Leaf photosynthetic efficiency was generally not impacted by chlorothalonil or dodine application.Pod maturity profile, pod yield, and grading parameters did not differ among the fungicide treatments.The selection between chlorothalonil or dodine did not impact the quality and value of peanut pods.High rate of dodine resulted in the lowest defoliation caused by leaf spot diseases of all fungicide treatments.Dodine is a potential alternative to chlorothalonil in the control of leaf spot diseases in peanut. [ABSTRACT FROM AUTHOR]

Background: Groundnut (Arachis hypogaea L.) is one of the most nutritious crops having versatile use. Its yield is mostly exaggerated by two foliar fungal diseases i.e., early and late leaf spot. In present investigation 96 germplasm lines were screened for early and late leaf spot diseases along with diverse biochemical parameters including protein, oil, fatty acids, proline and sugar contents. Methods: Early and late leaf spot diseases have been screened under field condition as well as employing SSR molecular markers. Nutritional profiling has also been done for protein, oil, fatty acids viz., oleic acid, linoleic acid, stearic acid and palmitic acid, proline and sugar contents. Result: In correlation analysis, oleic acid is negatively correlated with linoleic acid (r= -0.911) and palmitic acid (r= -0.688) at 1% level of significance, whereas stearic acid is positively and highly significantly correlated with oil percentage (r= 0. 0.381) at 1% significance level. The gene diversity value varied between 0.1866 to 0.4837 for markers ipahm103 and GM1536 with an average of 0.4014 and polymorphic information content (PIC) values ranged between 0.1692 to 0.3855 for the markers pahm103 to GM1536 respectively with a mean value of 0.3183. [ABSTRACT FROM AUTHOR]

The work aim was to analyze the agronomic performance of peanut genotypes and cultivars in the northwest region of Parana. The study was carried out in the field, in the experimental area of the Technical Center for Irrigation (CTI), at the State University of Maringá (UEM), located in the municipality of Maringá-PR. The design used was in randomized blocks, with six genotypes (cultivars: BRS 421 OL, BRS 423 OL and BRS 425 OL, lines: 1253 OL, 2101 OL and 2010 OL) and four repetitions. The experimental units were composed of plots with 4 lines of 3.0 m, spaced at 0.9 m. The characterization of the phenological stages was carried out, and the time to start the harvest, the incidence of early leaf spot during development, grain mass and productivity were analyzed. Data were submitted to analysis of variance and the means compared by the Tukey test at 5%. The peanut yield in the northwest region of Parana varied between 5.435,8 and 7.354,2 kg ha-1. The most productive materials were the 2010 OL line and the BRS 425 OL cultivar. [ABSTRACT FROM AUTHOR]

In the present study, F4 and F5 populations were evaluated to study the genetic variability for traits related to per cent disease incidence (PDI) for late leaf spot (LLS) reaction, pod yield and its component characters. Higher estimates of PCV and GCV were recorded for PDI at all the intervals, number of pods per plant, kernel yield per plant and pod yield per plant. High heritability accompanied with high GAM was observed for pods per plant, kernel yield per plant, pod yield per plant while plant height showed moderate GAM. Pods per plant, kernel yield per plant, sound mature kernel per cent and shelling per cent exhibited strong positive association with pod yield, whereas PDI@60 DAS, PDI@75 DAS, PDI@90 DAS and PDI@105 DAS exhibited significant negative association. Kernel yield exerted highest positive direct effect on pod yield. PDI at all the intervals recorded negative direct effect on pod yield. Indirect effect of number of pods per plant and kernel weight were high for pod yield. Principal component analysis (PCA) indicted that first and second component justified 58.4 per cent of variations among different traits. Biplot analysis of PCA results aided in identifying 15 LLS resistant lines. Kernel weight per plant has direct effect on pod weight per plant. Therefore, selecting this trait will increase pod yield. The identified lines can be evaluated in varied rainfed environments to exploit their disease resistance and yield potential and the validated lines can be utilised as improved cultivars. [ABSTRACT FROM AUTHOR]

Late leaf spot (LLS) is the most important foliar disease of peanuts in Argentina and in other peanut production areas, causing defoliation and yield losses. In this study, six isolates obtained from symptomatic leaflets collected in Córdoba, Argentina, were characterized based on the morphology of the colony and conidia, as well as molecularly using a combination of the 28S nrRNA gene (LSU), the internal transcribed spacer regions and the intervening 5.8S nrRNA gene (ITS) of the rDNA operon, and the second largest subunit of RNA polymerase II (RPB2). Pathogenicity was confirmed using the detached leaf method, inoculating a spore suspension. Mycelial growth was evaluated on four culture media: peanut oatmeal agar (POA), potato dextrose agar (PDA), peanut leaflet extract agar (PLA) and peanut seed extract agar (PSA), with PSA and PDA showing the greatest radial growth. This is the first study of N. personata obtained in the peanut-producing region of Argentina. [ABSTRACT FROM AUTHOR]

Leaf spot disease is a dangerous disease that affects peanut growth, and its severity can significantly impact peanut yield. Hyperspectral-based disease detection technology is a popular non-destructive technique due to its high efficiency, objectivity, and accuracy. In this study, peanut leaf spectra at different levels of severity of leaf spot disease were collected in Liaoning Province, China, in mid-August. This study analyzed the differences in wavelengths using mean spectral reflectance and sensitivity. Using improved principal component analysis loading (I-PCA loading) based on the contribution weight assignment approach, we identified three feature wavelengths of 570 nm, 671 nm, and 750 nm. We evaluated the ability of these feature wavelengths to detect the severity of leaf spot disease using k-nearest neighbor (KNN), support vector machine (SVM), and back-propagation (BP) neural network classifiers. Our experimental results showed that our improved PCA loading method achieved higher classification accuracy with fewer wavelengths than the seven commonly used feature selection methods. Among these classifiers, the SVM achieved the highest accuracy, with an overall accuracy (OA) of 96.88% and a Kappa of 95.81%. Therefore, our proposed method can accurately detect the severity of peanut leaf spot disease and provide scientific and technical support for accurately managing peanut crops. [ABSTRACT FROM AUTHOR]

Introduction: Fungal foliar diseases can severely affect the productivity of the peanut crop worldwide. Late leaf spot is the most frequent disease and a major problem of the crop in Brazil and many other tropical countries. Only partial resistance to fungal diseases has been found in cultivated peanut, but high resistances have been described on the secondary gene pool. Methods: To overcome the known compatibility barriers for the use of wild species in peanut breeding programs, we used an induced allotetraploid (Arachis stenosperma x A. magna)4x, as a donor parent, in a successive backcrossing scheme with the high-yielding Brazilian cultivar IAC OL 4. We used microsatellite markers associated with late leaf spot and rust resistance for foreground selection and high-throughput SNP genotyping for background selection. Results: With these tools, we developed agronomically adapted lines with high cultivated genome recovery, high-yield potential, and wild chromosome segments from both A. stenosperma and A. magna conferring high resistance to late leaf spot and rust. These segments include the four previously identified as having QTLs (quantitative trait loci) for resistance to both diseases, which could be confirmed here, and at least four additional QTLs identified by using mapping populations on four generations. Discussion: The introgression germplasm developed here will extend the useful genetic diversity of the primary gene pool by providing novel wild resistance genes against these two destructive peanut diseases. [ABSTRACT FROM AUTHOR]

In Australia, peanuts are mainly grown in Queensland with tropical and subtropical climates. The most common foliar disease that poses a severe threat to quality peanut production is late leaf spot (LLS). Unmanned aerial vehicles (UAVs) have been widely investigated for various plant trait estimations. The existing works on UAV-based remote sensing have achieved promising results for crop disease estimation using a mean or a threshold value to represent the plot-level image data, but these methods might be insufficient to capture the distribution of pixels within a plot. This study proposes two new methods, namely measurement index (MI) and coefficient of variation (CV), for LLS disease estimation on peanuts. We first investigated the relationship between the UAV-based multispectral vegetation indices (VIs) and the LLS disease scores at the late growth stages of peanuts. We then compared the performances of the proposed MI and CV-based methods with the threshold and mean-based methods for LLS disease estimation. The results showed that the MI-based method achieved the highest coefficient of determination and the lowest error for five of the six chosen VIs whereas the CV-based method performed the best for simple ratio (SR) index among the four methods. By considering the strengths and weaknesses of each method, we finally proposed a cooperative scheme based on the MI, the CV and the mean-based methods for automatic disease estimation, demonstrated by applying this scheme to the LLS estimation in peanuts. [ABSTRACT FROM AUTHOR]

Background: Groundnut (Arachis hypogaea L.) is the fourth most important oil seed crop in the world. The early and late spots are a foliage disease more common and more destructive of groundnut which cause severe yield losses up to 70% and reduces the quality of the pod and fodder. Recent efforts have targeted on developing resistant source against leaf spot disease which are helpful for discarding the hazardous toxic chemical compounds used for controlling the disease. The current study aimed to identify the sources of resistance to leaf spot from the newly identified genotypes as well as from wild species and to study the way in which these were responding to the to the disease development. Methods: Seventy three groundnut genotypes were screened against late leaf spot disease under artificial epiphytotic conditions in field at AICRP on Groundnut, MPKV, Rahuri during kharif 2015-16. The observations of disease intensity and rate of infection were recorded. Result: Among the Seventy three groundnut genotypes, 10 genotypes showed resistant and 33 genotypes showed the moderately resistant reaction to leaf spot disease. The 25 entries showed the susceptible and 05 genotypes showed the highly susceptible reaction to the leaf spot disease. All the susceptible cultivars belong to A. hypogaea sub. sp. fastigiata var. fastigiata to as Spanish bunch. These 10 resistant genotypes of groundnut including two wild species viz., RHRG 6083, KDG 128, GPBD 4, ICGV 94118, ICGV 4983, ICG 12672, ICGV 13160 (A. batizocoi), ICGV 13165 (A. cardenasi), ICG 11426 and ICGV8193 were selected for intensive artificial screening under glasshouse conditions and also confirmed resistance. The resistant lines will be useful to develop introgression lines using marker-assisted backcrossing approach to improve foliar fungal disease resistance in popular groundnut varieties. [ABSTRACT FROM AUTHOR]

Peanut early leaf spot caused by Passalora arachidicola is serious fungal disease, which occurs in all production areas in China. Chemical control is the main method to prevent this disease. Nevertheless, it is easy to cause resistance, resurgence, residue problem (3R). Therefore, biological control is becoming the research hotspot for peanut early leaf spot control. 60 healthy peanut leaves were collected from major peanut production areas and were got for potential bacterial isolates. A total of 465 isolates were obtained. 22 strains showed differing levels of resistance against P. arachidicola. Among the antagonistic isolates, strain TL6 showed strongest inhibitory activity. It was identified as Bacillus amyloliquefaciens based on morphological characteristics, physiological and biochemical reactions as well as comparative analysis of its 16S rDNA sequence. The field control effect was above 69.17% after spraying the fermentation liquid of strain TL6. The field control effect of the strain TL6 fermentation liquid diluted 200 times and including the addition of 500 g/L carbendazim diluted 1000 times inhibited P. arachidicola by 81.33%. The combination of strain TL6 and carbendazim had a significant synergistic effect. Overall, strain TL6 showed promising potential commercial development and application in controlling peanut early leaf spot disease. [ABSTRACT FROM AUTHOR]

Early leaf spot (ELS) and late leaf spot (LLS) diseases are the two most destructive groundnut diseases in Ghana resulting in = 70% yield losses which is controlled largely by chemical method. To develop leaf spot resistant varieties, the present study was undertaken to identify single nucleotide polymorphism (SNP) markers and putative candidate genes underlying both ELS and LLS. In this study, six multi-locus models of genome-wide association study were conducted with the best linear unbiased predictor obtained from 294 African groundnut germplasm screened for ELS and LLS as well as image-based indices of leaf spot diseases severity in 2020 and 2021 and 8,772 high-quality SNPs from a 48 K SNP array Axiom platform. Ninety-seven SNPs associated with ELS, LLS and five image-based indices across the chromosomes in the 2 two sub-genomes. From these, twenty-nine unique SNPs were detected by at least two models for one or more traits across 16 chromosomes with explained phenotypic variation ranging from 0.01 - 62.76%, with exception of chromosome (Chr) 08 (Chr08), Chr10, Chr11, and Chr19. Seventeen potential candidate genes were predicted at ± 300 kbp of the stable/prominent SNP positions (12 and 5, down- and upstream, respectively). The results from this study provide a basis for understanding the genetic architecture of ELS and LLS diseases in African groundnut germplasm, and the associated SNPs and predicted candidate genes would be valuable for breeding leaf spot diseases resistant varieties upon further validation. [ABSTRACT FROM AUTHOR]

'Naronut 1R' (Reg. no. CV‐150, PI 693985) is a high‐yielding, Spanish‐type groundnut (Arachis hypogaea L. subsp. fastigiata var. vulgaris) cultivar with a red seed coat and two seeds per pod. The cultivar was selected from a cross between International Crops Research Institute for the Semi‐arid Tropics advanced lines 'ICGV‐SM 90704' and 'ICGV‐SM 86715'. Naronut 1R was developed for groundnut rosette disease resistance, late leafspot resistance, yield, red seed coat, and early maturity. Naronut 1R matures in 75–80 d after planting, compared with 90 d for the widely grown and popular landrace Erudu red (Spanish‐type groundnut). Naronut 1R has much higher yields (1,473 kg ha−1) than Erudu red (554 kg ha−1). The overall groundnut rosette disease rating was 44% lower in Naronut 1R (2.8 disease rating) compared with Erudu red (5.0 disease rating). Similarly, the overall late leafspot disease rating was 28% lower in Naronut 1R (3.6 disease rating) compared with Erudu red (5.0 disease rating). Similar to Erudu red, Naronut 1R seed testa is red, but the seed is larger. The sound mature kernel weight was 9.42% higher for Naronut 1R than for Erudu red. The shelling percentage for Naronut 1 was 49.3%, whereas Erudu red was 60%. Naronut 1R has lower protein (18.1 vs. 21.3%) and oil content (36.0 vs. 44.3%) than Erudu red. Naronut 1R will allow for broad adoption of a high yielding, early maturing, red seed coat cultivar that carries resistance to groundnut rosette disease and late leaf spot disease. Core Ideas: Naronut 1R is a high‐yielding, very early‐maturing peanut cultivar.Naronut 1R is resistant to groundnut rosette disease and late leaf spot disease.Naronut 1R is broadly adaptable across sub‐Saharan Africa. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Peanut foliar diseases and disorders can be difficult to rapidly diagnose with little experience because some abiotic and biotic symptoms present similar symptoms. Developing algorithms for automated identification of peanut foliar diseases and disorders could potentially provide a quick, affordable, and easy method for diagnosing peanut symptoms. To examine this, images of peanut leaves were captured from various angles, distances, and lighting conditions using various cameras. Color space data from all images was subsequently extracted and subjected to logistic regression. Separate algorithms were developed for each symptom to include healthy, hopperburn, late leaf spot, Provost injury, tomato spotted wilt, paraquat injury, or surfactant injury. The majority of these symptoms are not included within currently available disease identification mobile apps. All of the algorithms developed for peanut foliar diagnostics were ≥ 86% accurate. These diagnostic algorithms have the potential to be a valuable tool for growers if made available via a web-accessible platform, which is the next step of this work. [ABSTRACT FROM AUTHOR]

Background: Groundnut is an important edible leguminous oilseed crop and stem rot caused by Sclerotium rolfsii is one of the major soil borne disease affecting groundnut severely in India. The current study aimed to get an understanding regarding the incidence level and pattern of prevalence of the disease in the agro-ecological condition of Andhra Pradesh. Methods: Roving survey was taken up from major groundnut growing areas in Andhra Pradesh during kharif, 2019 and kharif, 2020 in five districts. From each district predominant groundnut growing areas were chosen for assessment of the disease. Five spots of one square meter area in each field were selected randomly and per cent disease incidence was recorded. Result: From the data it is observed that the stem and pod rot incidence was higher during kharif, 2020 when compared with kharif, 2019. The pooled data indicated that the mean stem rot incidence for the two years ranges from 5.39% to 23.56% with highest mean stem rot incidence in Krishnapuram (v), Kothapatnam (m) of Prakasam dist (23.56%) and lowest in Bandarupalli (v), Yerpedu (m) of Chittoor dist (5.39%). Among different districts highest mean stem rot incidence was recorded in Prakasam dist (18.71%) and lowest was recorded in Chittoor dist (7.11%). The stem rot severity was high in sandy soils with TAG24 variety where groundnut is being grown followed by red sandy loams and least in clay loam soils. The groundnut sole crop recorded high disease incidence when compared with intercrop of red gram. Siginificant positive correlation was recorded between stem rot, pod rot and leaf spot incidence. [ABSTRACT FROM AUTHOR]

Leaf spot (LS) caused by Cercosporidium personatum is one of the most harmful peanut diseases in the late growth stage and severely affects the yield of peanuts. Hyperspectral disease detection technology is efficient, objective, and accurate and is suitable for large-scale crop management practices. To establish a multi-scale spectral index (SI) with high accuracy and stability for the detection of peanut LS disease, the spectral reflectance of different disease severity levels at leaf, plant, and field scales was collected, and the difference in wavelength caused by disease severity was analyzed using the mean, variance, and dispersion matrix of hyperspectral reflectance. Meanwhile, the feature weights at different scales were obtained using Relief-F, and the average feature weights identified 540, 660, and 770 nm as multi-scale sensitive wavelengths. Three new SIs were constructed by combining single, ratiometric, and normalized wavelengths. The new SIs were compared and analyzed with 35 commonly used SIs by correlation analysis and M-statistic values, and 6 SIs were significantly correlated with disease severity levels and had good separability. Finally, k-nearest neighbor (KNN) and multinomial logistic regression (MLR) were used to evaluate the ability of the above SIs to detect LS severity. The results showed that the leaf spot multi-scale spectral index (LS-MSSI) constructed in this study was superior to the other SIs and obtained high accuracy at different scales simultaneously. At the leaf and plant scales, the MLR obtained high accuracy, with the overall accuracy (OA) reaching 93.77% and 92.50% and Kappa reaching 91.59% and 89.97%, respectively. At the field scale, the KNN obtained high accuracy, with the OA and Kappa reaching 90.29% and 87.04%, respectively. The LS-MSSI proposed in this study has high accuracy, stability, and robustness in the detection of LS severity at multiple scales, providing a technical basis and scientific guidance for the detection and precise management of peanuts. [ABSTRACT FROM AUTHOR]

Stem rot in groundnut is one of the major diseases significantly affecting the crop yields leading to 10 to 25 per cent yield loss annually. Favourable weather parameters influence development and spread of the disease and the present study was conducted to quantify their role in disease development. Study conducted at Banavasi village of Yemmiganur in Kurnool district, Andhra Pradesh during rabi 2020 and 2021 to know the influence of weather parameters on stem rot disease progression and the relation between leaf spot and stem rot disease development using correlation and regression analysis. A positive correlation of the stem rot with weather parameters like T max, Rainfall, Relative Humidity in rabi, 2020, Relative humidity during rabi, 2021 and negative correlation with Tmin and Wind speed in rabi, 2020, and Tmax, Tmin and rainfall in rabi, 2021 was observed. A regression analysis of the data inferred that the weather parameters as a whole contribute about 66.00% and 60.00% to disease incidence in rabi 2020 and 2021 respectively, of these Tmax (62.37%) and Rainfall (26.12%) in rabi 2020 and Tmin and rainfall during rabi, 2021. Highest stem rot incidence was recorded in 4th standard week (22nd to 28th January) with a per cent disease incidence of 6.78 during rabi, 2020 and in 52nd standard week (24th to 31st December) with a percent disease incidence of 3.6 during rabi, 2021. Initiation of leaf spot has started early i.e., 48th standard week during rabi, 2020 and 49th standard week during rabi, 2021. A non-significant positive correlation between the stem rot and leaf spot in both the years reveal that the diseases developed were solely due to influence of weather parameters on the individual disease and both the diseases are not interrelated or interdependent in causing the other. [ABSTRACT FROM AUTHOR]

Late leaf spot (LLS) disease is an omnipresent peanut (Arachis hypogaea L.) foliar disease that causes significant yield loss. Integrating host resistance to reduce yield loss and management costs from this disease is highly desirable. In addition to disease resistance, market demand for high‐oleic peanut is on the rise due to its improved oxidative stability and health benefits. Previously, a recombinant inbred population from Florida‐07 × GP‐NC WS 16 (crossing number C1801) segregating for both LLS resistance and high oleic traits was used to perform genetic mapping for LLS resistance by quantitative trait loci (QTL)‐seq analysis. Three QTL regions on chromosomes B03, A05, and B05 were identified, and Kompetitive allele‐specific polymerase chain reaction markers flanking these regions were validated through a field test on genotypically selected sister lines. Two recombinant inbred lines, TifGP‐5 (Reg. no. GP‐246, PI 700330) and TifGP‐6 (Reg. no. GP‐247, PI 700331), with top levels of field resistance to LLS, were selected from this population for release. Both lines possess the resistance alleles of all the genetic markers. One line has high oleic acid content, and the other is normal oleic. Releasing these recombinant inbred lines packaged with molecular markers provides the peanut‐breeding community with genetic resources that can be utilized through marker‐assisted breeding. Core Ideas: TifGP‐5 and TifGP‐6 harbor QTL regions conditioning late leaf spot resistance.TifGP‐5 and TifGP‐6 have high oleic and normal oleic chemistries, respectively.KASP markers delineate the borders of these QTL regions and can facilitate marker‐assisted breeding. [ABSTRACT FROM AUTHOR]

Early Leaf Spot (ELS) caused by the fungus Passalora arachidicola and Late Leaf Spot (LLS) also caused by the fungus Nothopassalora personata, are the two major groundnut (Arachis hypogaea L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73-33, Gha-GAF 1723, Zam-ICGVSM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa. [ABSTRACT FROM AUTHOR]

Background: Groundnut (Arachis hypogaea L.) is an important legume crop of tropical and sub-tropical areas of the world. This crop suffers from several diseases like early leaf spot, late leaf spot, rust, groundnut bud necrosis, peanut clump and collar rot etc. But collar rot caused by Aspergillus niger van Teighem is one of the most important seed and soil borne diseases causing huge economic (40-50 per cent) loss in India and abroad. Methods: A roving survey was conducted to assess the incidence of collar rot in eight major groundnut growing districts of western Rajasthan i.e. Bikaner, Jodhpur, Churu, Jalore, Jaipur, Sikar, Nagaur and Dausa during Kharif 2018. A field experiment was carried out for two consecutive years (2019 and 2020) to manage the disease through altering micro-climate under canopy with six levels of seed rates (80, 85, 90, 95, 100 and 105 kg/ha). Result: Our investigations cleared that this disease is prevalent in all the 200 fields surveyed in eight districts of Rajasthan with 22.99 per cent overall disease incidence which varied from 17.84 to 32.38 per cent with monetary losses. Maximum disease incidence was recorded in Jaipur (28.85%) district while it was lowest in Nagaur district (20.15%). Higher disease reduction (12.97%) with increased pod yield (27.65%) was observed with higher seed rate of 105 kg/ha as compared to standard recommended seed rate (80 kg/ha) as this pathogen is favoured by higher temperature and low soil moisture, dense canopy provides shade that increases humidity under the cropped area and lowers temperature. As a result of this, plant mortality can be compensated by increasing plant population through seed rate, as it modifies the microclimate and generate an environment which is less favourable for developing collar rot disease of groundnut. [ABSTRACT FROM AUTHOR]

Groundnut is an important oilseed crops with high nutritional values. Yield of groundnut is constrained owing to diverse biotic and abiotic factors. Amongst biotic stresses two foremost foliar fungal diseases v iz., late leaf spot and rust are universal and sparingly significant. Both conventional and modern breeding methods may be applied for improvement of groundnut. The current investigation was carried out to recognize genetic variability present among different groundnut germplasm with the use of morphological characters and SSR markers with the screening of resistant to leaf spot and rust resistant genotypes. In morphological examination, the analysis of variance divulged highly substantial differences among 48 germplasm lines. Initial screening of Germplasm was tried with the use of 125 SSR markers. But, based on banding pattern, 21 markers were found to be the best and selected for amplification of whole set of genotypes. Out of 21 markers, 16 were found to be highly polymorphic. Highest PIC value (0.8196) was detected for marker S021 representing 81% diversity. SSR Markers viz., PM42, PM204, PM377, S052, S076, S078 and S078 have PIC value more than 0.5, confirms their superiority in detection of polymorphism among studied germplasm lines. UPGMA analysis assemblage all the germplasm lines into three major clusters. Most of the foliar disease resistant genotypes were grouped together with higher genetic resemblance. Polymorphic markers identified in the study may be utilized further for molecular diversity analysis and the identified resistant genotypes may be employed further for the improvement of the crop. [ABSTRACT FROM AUTHOR]

'IAC Sempre Verde' is a medium-seeded runner-type peanut highly resistant to late leaf spot and rust, the main diseases in peanut cultivation in Brazil. The newly released cultivar showed remarkable yield advantage over controls when diseases were not controlled and represents the first possibility for peanut cultivation under "Organic" management. [ABSTRACT FROM AUTHOR]

Peanut (Arachis hypogaea L.) is an important crop used for oil production, and oleic acid is a major factor in determining oil quality. Alterations in the oleic acid content can improve the nutritional quality and oxidative stability and prolong the shelf life of peanut products. The objective of this study was to develop a peanut variety with a high-oleic-acid content and high yield. One elite variety, "huayu22," was hybridized with the high-oleic-acid "KN176" donor and backcrossed for four generations as the recurrent parent using fad2 marker-assisted backcross selection. Based on the Kompetitive allele-specific PCR (KASP) screening of fad2 markers, the oleic acid content of advanced generations derived by selfing was assessed by near-infrared reflectance spectroscopy and gas chromatography. The genetic background recovery rate of four BC4F4 lines showed an average of 92.34% and was confirmed by genotyping using the Axiom_Arachis 58 K SNP array. Across these superior lines in BC4F6 generations, one line with a high-oleic-acid content and high yield was detected and named "YH61." In particular, yield comparison experiments showed that YH61 exhibited high and stable yield at three different locations and was moderately resistant to leaf spot disease. The distinctness, uniformity and stability (DUS) testing for two consecutive years suggested that YH61 reached the standard for variety rights application. The use of the peanut variety YH61 contributed to the expansion of the cultivation area due to its high value in the oleic acid market and the proven economic benefits in China. This study demonstrated that the marker-assisted backcross strategy based on a cost-effective KASP assay and SNP array for the detection of mutations in fad2 and genetic background evaluation can be used to create efficient peanut breeding programs and contribute to oil quality and high-yield stability. [ABSTRACT FROM AUTHOR]

Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = –0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = –0.80), and b * (r = –0.75) were highly correlated with LLS visual scores. NDVI (r = –0.72), v* (r = –0.71), b * (r = –0.64), and GA (r = –0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H 2 > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding. [ABSTRACT FROM AUTHOR]

Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group-16 (VGG16), visual geometry group-19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants. [ABSTRACT FROM AUTHOR]

Early leaf spot (caused by Passalora arachidicola) and late leaf spot (caused by Nothopassalora personata) are the most widespread, damaging foliar diseases of peanuts in the southeastern United States. When left nontreated, both diseases can prematurely defoliate peanuts and reduce peanut yields by up to 70%. Leaf spot diseases are primarily managed through a combination of fungicide applications and cultivar selection. In this study, six field experiments were conducted from 2021 to 2022 in Brewton, Headland, and Tallassee, Alabama, to evaluate the response of ten selected commercial peanut cultivars to various levels of leaf spot pressure as influenced by low- and high-input fungicide programs when compared to nontreated controls. The low input fungicide program included seven applications of chlorothalonil, whereas the high input fungicide program comprised of fluxapyroxad, pyraclostrobin, mefentrifluconazole, flutolanil, bixafen, flutriafol, tebuconazole, and/or chlorothalonil. Effects on leaf spot and pod yields were assessed. Leaf spot severity was higher for TUFRunner™ '297', Georgia-16HO, and FloRun '331', but was lower for AU-NPL 17, Georgia-06G, Georgia-12Y, and Georgia-14N. In terms of yield, leaf spot tolerant varieties showcased superior performance in high disease pressure, whereas susceptible varieties excelled in low disease pressure. Fungicides significantly enhanced the performance of susceptible cultivars under high leaf spot pressure, with marginal impact in low leaf spot pressure. These results highlight the importance of selecting cultivars and fungicide spray programs based on disease pressure to maximize profits. • Leaf spot pressure highly influences peanut cultivar and fungicide program choices. • Tolerant cultivars can reduce leaf spot inoculum and excel under high pressure. • Leaf spot susceptible cultivars are a viable option under low disease. • Fungicides increase returns under moderate to high and high leaf spot pressure. • Fungicide applications enhance performance of leaf spot susceptible cultivars. [ABSTRACT FROM AUTHOR]

Aflatoxin contaminated groundnut can cause serious health effects to both humans and livestock. Twenty nine pre-breeding genotypes derived from A, B and K genomes of groundnut along with their parents, susceptible, resistant checks, released cutlivars and advanced breeding lines were evaluated under field for various productivity parameters in addition to screening for resistance against aflatoxin in the artificial condition. Among the pre-breeding genotypes only two genotypes (ICGIL 17101 and ICGIL 17124) showed resistance to A flavus with colonization severity of 1 and very less incidence percentage (< 7%) and considered as resistant compared to known resistant check, ICG 02207 which had colonization severity of 3 with 95 % incidence. Three pre breeding genotypes, ICGIL 17107, ICGIL 17114, and ICGIL 17128 showed colonization severity of 2 and considered as moderately resistant. The late leaf spot and rust resistant released variety, GPBD 4 showed colonization severity of 3 and classified as susceptible indicating different resistant mechanism operating against different pathogens. The genotype ICGIL 17124 in addition to having resistance to A flavus, also had higher pod yield per plant and hence could be tested widely for its consistent performance before releasing as a variety. [ABSTRACT FROM AUTHOR]

Crop wild species are increasingly important for crop improvement. Peanut (Arachis hypogaea L.) wild relatives comprise a diverse genetic pool that is being used to broaden its narrow genetic base. Peanut is an allotetraploid species extremely susceptible to peanut root-knot nematode (PRKN) Meloidogyne arenaria. Current resistant cultivars rely on a single introgression for PRKN resistance incorporated from the wild relative Arachis cardenasii , which could be overcome as a result of the emergence of virulent nematode populations. Therefore, new sources of resistance may be needed. Near-immunity has been found in the peanut wild relative Arachis stenosperma. The two loci controlling the resistance, present on chromosomes A02 and A09, have been validated in tetraploid lines and have been shown to reduce nematode reproduction by up to 98%. To incorporate these new resistance QTL into cultivated peanut, we used a marker-assisted backcrossing approach, using PRKN A. stenosperma -derived resistant lines as donor parents. Four cycles of backcrossing were completed, and SNP assays linked to the QTL were used for foreground selection. In each backcross generation seed weight, length, and width were measured, and based on a statistical analysis we observed that only one generation of backcrossing was required to recover the elite peanut's seed size. A populating of 271 BC3F1 lines was genome-wide genotyped to characterize the introgressions across the genome. Phenotypic information for leaf spot incidence and domestication traits (seed size, fertility, plant architecture, and flower color) were recorded. Correlations between the wild introgressions in different chromosomes and the phenotypic data allowed us to identify candidate regions controlling these domestication traits. Finally, PRKN resistance was validated in BC3F3 lines. We observed that the QTL in A02 and/or large introgression in A09 are needed for resistance. This present work represents an important step toward the development of new high-yielding and nematode-resistant peanut cultivars. [ABSTRACT FROM AUTHOR]

‘Hae-Ol’ (Arachis hypogaea ssp. Hypogaea) -a high-oleate peanut variety-was developed at the Department of Southern Area Crop Science, National Institute of Crop Science in 2018. This variety was selected from the pedigree line crossed between short stem Virginia-type ‘HP825’ and high-oleate elite line ‘YG316’. The crude fat content of ‘Hae-Ol’ was 50.8%, and its oleic acid composition was 83.0% showing high-oleate property compared to that of ‘Daekwang’ variety (57.3%). The ratio of oleic acid to linoleic acid, which represents the oxidative stability, was 12 times higher in ‘Hae-Ol’ than in ‘Daekwang’. The increments in acid value and peroxide value were relatively slower in ‘Hae-Ol’, under the condition of accelerated oxidation. Over the three years of regional adaptation test (2016-2018) conducted at four regions, ‘Hae-Ol’ showed significantly higher grain yield (4.88 MT⋅ha-1), which was 16% more improved than that of ‘Daekwang’ (4.20 MT⋅ha-1). The seed of ‘Hae-Ol’ is brown in color and long-ellipse in shape and its 100-seed weight was 96 g. On field, ‘Hae-Ol’ demonstrated more resistance to late leaf spot (Cercosporidium personatum) and web blotch (Phoma arachidicola). It also had a shorter branch and main stem, which was related to the higher tolerance to lodging stress. The release of ‘Hae-Ol’ could contribute to the production of peanuts with enhanced quality and higher yield, and it is expected to be notably beneficial to both the producers and manufacturers (Registration No. 8407). [ABSTRACT FROM AUTHOR]

BACKGROUND: Increasing pesticide retention on crop leaves is a key approach for guaranteeing efficacy when products are applied to foliage. Evidently, the formulation plays an important role in this process. Microcapsules (MCs) are a promising formulation, but whether and how their adhesion to the leaf surface affects retention and efficacy is not well understood. RESULTS: In this study, we found that the incorporation of polyethylene glycol (PEG) with different molecular weights into the MC shell affects the release profile of MCs and the contact area of these MCs to leaves by changing their softness. The cumulative release rates of pyraclostrobin (Pyr) MCs fabricated with PEG200, PEG400, PEG800 and PEG1500 were 80.61%, 90.98%, 94.07% and 97.40%, respectively. Scanning electron microscopy observations showed that the flexibility of the MCs increased with increasing PEG molecular weight. The median lethal concentration (LC50) of the MCs with different PEG to the zebrafish were 12.10, 8.10, 3.90 and 1.46 mg L−1, respectively, which also indirectly reflected their release rate. Rainwater had less influence on the retention of the MCs prepared with PEG1500 than with the other PEG, which indicates a better adhesion to the target leave surfaces. MCs with the highest residual efficacy had better control efficacy on peanut leaf spot in field trials. CONCLUSION: Overall, adding PEG with an appropriate molecular weight to the MC shell can regulate the structure of the MC shell to improve the affinity between the MCs and leaves, which further improves the utilization of pesticides and reduces the environmental risks of pesticides. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Groundnut (Arachis hypogaea L.) is grown globally and it is affected by many widespread diseases and infestations in all the groundnut growing areas. In Eritrea, leaf spot diseases are prevalent that is caused by fungi (Passalora arachidicola and P. personata) and results great losses. Botanical pesticides play an important role to control fungal infections due to their non-phytotoxic and ecofriendly nature. This study intended to evaluate the efficacy of selected botanicals viz., Neem (Azadirachta indica), Melia (Melia azadirach), Lantana (Lantana camara), Datura (Datura metel) and Marigold (Tagetes patula) on plant height, intensity of leaf spots disease and yield of groundnut in Hamelmalo zoba, Eritrea, during 2017-2018. Extract of neem leaves (5.66, 14.10 and 25.38%) showed high significance in plant height after 20, 40 and 60 days, respectively, as compared to untreated control (21.07%) and mancozeb (treated control). The lowest disease intensity was recorded in the application of neem extract (15%w/v) at different days after sowing. However, mancozeb (2 g/liter) was the most effective and followed by Neem > Melia > Lantana showed improvement in plant growth and maximum reduction in disease intensity. The highest reduction rate in 'disease incidence' and 'disease severity' was found in Neem extract application (63.11 and 40.29) after mancozeb (56.52 and 36.18) at 95 DAS at 5% significant level. Among the botanicals, plants treated with Neem extract was observed with highest number of pods (89.45) and yield (98.10 q/ha) followed by Melia (5% w/v) i.e. 87.59 and 95.02 respectively. [ABSTRACT FROM AUTHOR]

The molecular chaperone complex HSP90-RAR1-SGT1 (HRS) plays important roles in both biotic and abiotic stress responses in plants. A previous study showed that wild peanut Arachis diogoi SGT1 (AdSGT1) could enhance disease resistance in transgenic tobacco and peanut. However, no systematic analysis of the HRS complex in Arachis has been conducted to date. In this study, a comprehensive analysis of the HRS complex were performed in Arachis. Nineteen HSP90 , two RAR1 and six SGT1 genes were identified from the allotetraploid peanut Arachis hypogaea , a number close to the sum of those from the two wild diploid peanut species Arachis duranensis and Arachis ipaensis. According to phylogenetic and chromosomal location analyses, thirteen orthologous gene pairs from Arachis were identified, all of which except AhHSP90-A8 , AhHSP90-B9 , AdHSP90-9 , and AiHSP90-9 were localized on the syntenic locus, and they shared similar exon-intron structures, conserved motifs and expression patterns. Phylogenetic analysis showed that HSP90 and RAR1 from dicot and monocot plants diverged into different clusters throughout their evolution. Chromosomal location analysis indicated that AdSGT1 (the orthologous gene of AhSGT1-B3 in this study) might provide resistance to leaf late spot disease dependent on the orthologous genes of AhHSP90-B10 and AhRAR1-B in the wild peanut A. diogoi. Several HRS genes exhibited tissue-specific expression patterns, which may reflect the sites where they perform functions. By exploring published RNA-seq data, we found that several HSP90 genes play major roles in both biotic and abiotic stress responses, especially salt and drought responses. Autoactivation assays showed that AhSGT1-B1 could not be used as bait for yeast two-hybrid (Y2H) library screening. AhRAR1 and AhSGT1 could strongly interact with each other and interact with AhHSP90-B8. The present study represents the first systematic analysis of HRS complex genes in Arachis and provides valuable information for functional analyses of HRS complex genes. This study also offers potential stress-resistant genes for peanut improvement. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Two elite cultivars of peanut (Arachis hypogaea L.), GPBD 4 and G 2–52, with high productivity, oil content, resistance to late leaf spot [Phaeoisariopsis personata (Berk. & Curt) V. Arx.] (LLS) and rust (Puccinia arachidis Speg.) diseases were improved for oleic acid content using marker‐assisted backcrossing. Since both the recurrent parents already possessed the mutant allele at AhFAD2A, only mutant allele at AhFAD2B was transferred from the donor 'SunOleic 95R' (oleate of 80.6%). Three rounds of backcrossing with foreground selection using allele‐specific polymerase chain reaction (PCR) and Kompetitive allele‐specific PCR (KASP) assay identified a large number of plants homozygous for the mutant allele at AhFAD2B in BCnF2 generations. Evaluation of the advanced generations could identify six and 10 lines with significantly higher oleate than GPBD 4 and G 2–52, respectively. Considering the yield, shelling percentage, oil, and oleate content, the most promising lines HOBC2GPS_7 and HOBC2G2S_5 were selected with 112 and 142% oleate recovery over GPBD 4 and G 2–52, respectively. Double digest restriction‐site‐associated DNA sequencing of these superior lines showed background genome recovery of 77.5 and 69.0%, respectively. These advanced breeding lines with high oleate (∼80%), resistance to LLS and rust and high productivity are under further trials for possible release as varieties for commercial cultivation. Core Ideas: Peanut is a rich source of oil, proteins, and carbohydrates, known as "poor man's almond".Peanut with high oleic acid content offers oil stability, flavor, and several health benefits.We have improved two elite peanut varieties for oleic acid content using MABC approach.Field evaluation of high oleate backcross lines confirmed higher productivity and disease resistance. [ABSTRACT FROM AUTHOR]

Early leaf spot (ELS) disease, caused by Cercospora arachidicola S. Hori, severely affects groundnut (Arachis hypogaea L.) production worldwide. Breeding for resistant varieties using conventional breeding is difficult because multiple genes control the resistance and heritability is moderate. Our objective was to identify marker-trait associations (MTAs) for ELS resistance in a mini-core collection of 168 accessions. The accessions were genotyped using diversity array technology (DArTseq™) and phenotyped at two locations with and without inoculation. Two types of DArTseq markers were scored; Silico DArT markers and biallelic single nucleotide polymorphism (SNP) markers. Genotype effects were significant for both ELS and pod weight (PW), with several accessions showing good levels for resistance. The heritability of ELS was moderate (0.37 to 0.64), whereas heritability for PW was low (0.10 to 0.18). The DArTseq generated 3592 biallelic SNP markers and 15,237 dominant silico markers. The distribution of the biallelic and dominant markers across the genome shows that chromosomes B04 and A04 had the highest number of markers. The MTA analysis revealed 25 significant (p < 0.001) MTAs. One MTA for ELS was found across treatments and rating dates and may be suitable for marker-assisted selection upon validation. No MTA for PW was repeatable. The germplasm and markers identified in this study can serve as useful genomic resources to initiate marker-assisted selection and trait introgression for ELS resistance in groundnut. [ABSTRACT FROM AUTHOR]

Background: The genetic yield potential of groundnut (Arachis hypogaea L.) has been continuously challenged by several diseases including early leaf spot (ELS). Methods: In the current study, we evaluated groundnut mini core collections under artificial and natural disease epiphytotics in six environments to identify stable elite sources for ELS resistance and pod yield. Mixed model analysis was done to adequately capture the variance component as a result of genotype (G), environment (E) and G x E interaction (GEI). Result: Highly significant (p <0.001) effects for G and GEI on ELS and pod weight were observed. The parametric and non-parametric stability models ranked the genotype differently for their stability to ELS. The GGE biplot identified ICG 1519 as a stable genotype for the ELS resistance. For pod weight, ICG 8896 and ICG 7897 were consistently stable from all the stability models including the GGE biplot. ICG 9449 and ICG 4540 were identified as stable genotypes for both ELS and pod weight. These elite sources of ELS resistance identified in the current study will be useful in the development and deployment of groundnut varieties with resistance to ELS and high pod yielding potentials. [ABSTRACT FROM AUTHOR]

Efficacy of in-furrow application of fluopyram + imidacloprid alone or followed by fluopyram + prothioconazole at-pegging and aldicarb for managing peanut root-knot nematode populations, root and pod galling due to nematodes, and yield, along with non-target effects on leaf spot diseases and stem rot were compared on the root-knot susceptible cultivar Georgia-06G over seven years on an irrigated site with an established population of Meloidogyne arenaria race 1. In all years, fungicides were applied to all plots following a Peanut Rx application schedule. Root-knot nematode populations, nematode damage, and yield along with leaf spot severity and stem rot incidence significantly differed among years; except for nematode populations, these measured variables also differed by nematicide treatment. Averaged over years, galling and root damage was significantly reduced with aldicarb compared to the no-nematicide control but not with either of the fluopyram treatments. While similar late-season root-knot nematode populations were noted for all nematicide programs and the non-treated control, pooled over years, significant yield gains were obtained with aldicarb and fluopyram programs with the two-treatment program providing greater yields than the aldicarb treatment. Leaf spot severity, averaged over years, was similar in aldicarb and control treatments; however, both fluopyram treatments had significantly lower leaf spot severity than the aldicarb treatment. Stem rot incidence did not differ among nematicide treatments. In addition, pooled over years, there was a negative relationship between juvenile populations and yield, but this was relatively weak. Weather, particularly extreme rain conditions, impacted yield directly as well as indirectly by affecting leaf spot severity and nematode activity. • Fluopyram is a succinate dehydrogenase inhibitor fungicide with nematistatic activity. • Fluopyram treatments, compared to aldicarb, reduced severity of leaf spots of peanut. • Late season root-knot nematode densities were not reduced, but aldicarb reduced galls. • Compared to no treatment, aldicarb and fluopyram treatments improved yield. [ABSTRACT FROM AUTHOR]

In this study, the differential rankings of 36 groundnut genotypes under varying environmental conditions were studied at various levels of phenotype. Locations that are generally accepted by the crop- and soil-based research community to represent the entire Guinea and Sudan Savanna agro-ecological zones in Ghana were characterized, this time using a crop. The characterization was done based on four farmer-preferred traits (early and late leaf spot disease ratings, and haulm and pod yields) using three models (i.e., AMMI, GGE, and Finlay–Wilkinson regression). These models were used to capture specific levels of phenotype, namely, genotype-by-environment interaction (GE), genotype main effect plus GE (G+GE), and environment and genotype main effects plus GE (E+G+GE), respectively. The effect of three major environmental covariables was also determined using factorial regression. Location main effect was found to be highly significant (p < 0.001), confirming its importance in cultivar placement. However, unlike genotypes where the best is usually adjudged through statistical ranking, locations are judged against a benchmark, particularly when phenotyping for disease severity. It was also found that the locations represent one complex mega-environment, justifying the need to test new technologies, including genotypes in all of them before they can be approved for adoption nationally. Again, depending on the phenotypic level considered, genotypic rankings may change, causing environmental groupings to change. For instance, all locations clustered to form one group in 2017 for early and late leaf spot diseases and pod yield when GE was considered, but the groupings changed when G+GE was considered for the same traits in the same year. As a result, assessing genotypic performance at the various levels to arrive at a consensus decision is suggested. Genotypes ICGV-IS 141120 and ICGV-IS 13937 were found to be the best performing. [ABSTRACT FROM AUTHOR]

Peanut is an important food crop, and diseases of its leaves can directly reduce its yield and quality. In order to solve the problem of automatic identification of peanut-leaf diseases, this paper uses a traditional machine-learning method to ensemble the output of a deep learning model to identify diseases of peanut leaves. The identification of peanut-leaf diseases included healthy leaves, rust disease on a single leaf, leaf-spot disease on a single leaf, scorch disease on a single leaf, and both rust disease and scorch disease on a single leaf. Three types of data-augmentation methods were used: image flipping, rotation, and scaling. In this experiment, the deep-learning model had a higher accuracy than the traditional machine-learning methods. Moreover, the deep-learning model achieved better performance when using data augmentation and a stacking ensemble. After ensemble by logistic regression, the accuracy of residual network with 50 layers (ResNet50) was as high as 97.59%, and the F1 score of dense convolutional network with 121 layers (DenseNet121) was as high as 90.50. The deep-learning model used in this experiment had the greatest improvement in F1 score after the logistic regression ensemble. Deep-learning networks with deeper network layers like ResNet50 and DenseNet121 performed better in this experiment. This study can provide a reference for the identification of peanut-leaf diseases. [ABSTRACT FROM AUTHOR]

A set of 184 mini core genotypes along with twelve checks of groundnut were evaluated for their reaction to Spodoptera litura, late leaf spot and rust at hotspot location in addition to their studies on eight productivity traits to assess the genetic diversity during rainy season of 2017. D2 analysis based on the aforesaid traits grouped the196 genotypes into twenty-five clusters. Maximum inter cluster distance was between cluster XXIII and cluster VI (836.06) while the same for intra cluster was in cluster IV (95.94). Genotypes with resistance to different biotic stresses were distributed mainly in different solitary clusters. Cluster XIV (ICG 5051) and cluster XII (ICG 76) had genotypes with resistance to Spodoptera litura. The genotype, (ICG 12625) also belonging to solitary cluster XVIII had shown resistance to late leaf spot while the genotypes in clusters XVII (ICGV 87157) and cluster XXII (ICG 13723) had resistance to rust. Number of primary branches per plant (48.41%), hundred seed weight (13.13%), reaction to Spodoptera litura (11.33%) and yield per plant (8.33%) mainly contributed to diversity in the groundnut mini core. The genotypes, ICG 5051, ICG 76, ICG 12625, ICG 76 and ICG 4412 may serve as diverse sources for resistance to different biotic stresses in groundnut. ICG 2381, a multiple stress resistant genotype (Spodoptera litura, late leaf spot and rust) in the cluster IV can be utilised in the multiple stress resistance breeding in groundnut. [ABSTRACT FROM AUTHOR]

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. The oil obtained from the groundnut is widely used for cooking and losing body weight, and its fats are widely used for making soaps. The groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of deep convolutional neural network (DCNN) because it automatically detects the important features without any human supervision. The DCNN procedure can deeply detect plant disease by using a deep learning process. Moreover, the DCNN training and testing process demonstrate an accurate groundnut disease determination and classification result. The number of groundnut leaf disease images is chosen from the plant village dataset, and it is used for the training and testing process. The stochastic gradient decent momentum method is used for dataset training, and it has shown the better performance of proposed DCNN. From the comparison analysis, the 6th combined layer of proposed DCNN delivers a 95.28% accuracy value. Ultimately, the groundnut disease classification with its overall performance of proposed DCNN provides 99.88% accuracy. [ABSTRACT FROM AUTHOR]