Peanut leaf spot caused by Nothopassalora personata

Giordano, Damián Francisco; Pastor, Nicolás; Palacios, Sofia Alejendra; Oddino, Claudio; Torres, Adriana Mabel

doi:10.1007/s40858-020-00411-3

Cited by 8 publications

(3 citation statements)

References 80 publications

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“…Cercospora arachidicola is a fungus that produces brown spot disease mostly on leaves, whereas Cercospora personata causes black spot disease on peanut stems and leaves. When plants are infected, the color of their leaves changes to yellow, and they fall, reducing photosynthesis, growth, and yield [25]. White grubs often occur after 60 DAS and infect the root and pod from the beginning pod to full seed stages.…”

Section: A Ik In Peanut Cultivation Of the Khmer 1) History Of Peanut...mentioning

confidence: 99%

Indigenous Knowledge of Peanut Cultivation of the Ethnic Khmer Resident in the Mountainous Area in An Giang Province, Vietnam

Phung¹,

Hòa²,

Phong³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Indigenous knowledge (IK) can provide useful information on local people's activities within environmental contexts. For countries vulnerable to climate change as Vietnam, research on IK might assist in revealing information about local people's responses to environmental stresses and potentially support decision-making. To enrich the study context in this field, this study aims at determining the IK of peanut cultivation of the Khmer ethnic groups. The study site is in the mountainous area in An Giang province, Vietnam, where the Khmer people are located. The methods of documentary collection, focus group discussion, semi-structured interviews, and experiments were applied in this study. The investigation results showed the innovation of local people in the cultivation. For instance, applying cow manure to a sandy soil can reduce chemical fertilizers and improve peanut yield. The IK reported that the application of the broadcasting method could achieve the cultivation economic profit approximately 2.5 times higher than the dibbling method. The former method is mostly preferred due to its time/labor saving and the growing aged population in the study location. The experimental results indicated that the combination of indigenous and scientific knowledge in peanut cultivation, such as chemical fertilizer application, crop calendar, crop rotation, and varieties, could bring high economic efficiency, improve soil nutrients, and ability to adapt to climate change. The research approaches in this study comprehensively revealed the IK, which can be potentially applicable to similar studies.

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Section: A Ik In Peanut Cultivation Of the Khmer 1) History Of Peanut...mentioning

confidence: 99%

Indigenous Knowledge of Peanut Cultivation of the Ethnic Khmer Resident in the Mountainous Area in An Giang Province, Vietnam

Phung¹,

Hòa²,

Phong³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…This widespread affliction, causing necrosis in peanut leaves and stalks, poses a substantial risk to the global peanut industry. Accurately predicting the onset and severity of this disease is essential for implementing effective control strategies and mitigating crop loss [1,2].…”

Section: Introductionmentioning

confidence: 99%

Construction and Validation of Peanut Leaf Spot Disease Prediction Model Based on Long Time Series Data and Deep Learning

Guo,

Chen,

et al. 2024

Agronomy

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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 R2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R2 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.

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“…& Curt.) U. Braun, C. Nakash, Videira and Crous] ( Giordano et al, 2021 ) and Groundnut rosette disease (GRD) (caused by groundnut rosette virus (GRV)) are the most important biotic constraints to groundnut production in Uganda and SSA ( Deom and Okello, 2018 ). These two diseases often occur simultaneously in farmers’ fields causing up to 100% yield loss depending on the variety and management ( Mugisa et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding

Chapu

Okello²,

Okello³

et al. 2022

Front. Plant Sci.

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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 (H2 > 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.

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Peanut leaf spot caused by Nothopassalora personata

Cited by 8 publications

References 80 publications

Indigenous Knowledge of Peanut Cultivation of the Ethnic Khmer Resident in the Mountainous Area in An Giang Province, Vietnam

Indigenous Knowledge of Peanut Cultivation of the Ethnic Khmer Resident in the Mountainous Area in An Giang Province, Vietnam

Construction and Validation of Peanut Leaf Spot Disease Prediction Model Based on Long Time Series Data and Deep Learning

Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding

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