Efficacy and cost study of green fungicide formulated from crude beta-glucosidase

Khalili, Elham; Javed, Muhammad Arshad; Huyop, Fahrul; Wahab, Roswanira Abdul

doi:10.1007/s13762-018-2084-1

Cited by 6 publications

(8 citation statements)

References 40 publications

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“…The second strategy could be the presence of β-glucosidase activity, which has been well studied as a potential cause of fungal wall damage [33]. In particular, after enzymatic lytic evaluation, M. pulcherrima strain DiSVA 269 was selected to be more effective in controlling B. cinerea.…”

Section: Discussionmentioning

confidence: 99%

Biocontrol of Non-Saccharomyces Yeasts in Vineyard against the Gray Mold Disease Agent Botrytis cinerea

et al. 2022

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Background: Botrytis cinerea (B. cinerea) is responsible for grape infection and damage to the winemaking and table grape sectors. Although anti-Botrytis chemicals are available, they are considered unsustainable for resistance phenomenon and adverse effects on the environment and human health. Research is focused on developing alternative approaches, such as exploiting biological control agents (BCAs). In this context, 19 yeasts of the genera Cryptococcus, Aureobasidium, Metschnikowia, Kluyveromyces and Wickerhamomyces were tested as antimicrobial agents against B. cinerea development. Methods: A combination of in vitro tests based on dual-culture methods, volatile organic compound production assay, laboratory tests on grape berries (punctured and sprayed with yeasts) and field experiments based on yeast treatments on grapes in vineyards allowed the selection of two potential BCAs. Results: M. pulcherrima DiSVA 269 and A. pullulans DiSVA 211 exhibited the best ability to contain the development of B. cinerea, showing the severity, the decay and the McKinney index lower than a commercial biological formulation consisting of a mixture of two different A. pullulans strains, which were used as positive controls. Conclusions: The results indicated that the selected strains were effective BCA candidates to counteract B. cinerea in the field, applying them in the partial or total replacement of conventional treatments.

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Section: Discussionmentioning

confidence: 99%

Biocontrol of Non-Saccharomyces Yeasts in Vineyard against the Gray Mold Disease Agent Botrytis cinerea

et al. 2022

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“…P LANT diseases and infestations are leading to major ecological and economical losses in the agricultural industry. 14 % of global crop yields are lost due to plant diseases, weeds and insects, which not only leads to smaller revenues for the agricultural industry but also causes expenses for crop treatment [1]- [4]. Furthermore, it is necessary to develop efficient controls to enhance crop production to meet the increasing demands of the growing world's population [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it is necessary to develop efficient controls to enhance crop production to meet the increasing demands of the growing world's population [5], [6]. It is estimated that the production of global crops will have to be doubled by 2050 making it more important to reduce losses [1]. Besides the economic impact, especially the treatment of plant infestations, also affect the environment [1], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

Farah,

Drack,

Dawel

et al. 2023

IEEE Access

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We address the soybean leaves infection problem by proposing a robust classification model that can reliably detect infests by Diabrotica speciosa and caterpillars. Our transfer-learning based model uses a VGG19 convolutional neural network to classify the soybean leaves and we achieve balanced accuracies between 93.71 % and 94.16 % on unseen testing data, what sets a new benchmark and outperform previous work using the same dataset. Our work has theoretical and practical implications. The soybean plays a crucial role in the agricultural industry. Infestation of soybeans leads to enormous economic and environmental losses. With our model presented here, an early and accurate detection to control the spread of plant pests is possible, what reduces economic and ecological damages.

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“…The production of global crops has to be doubled by 2050 to meet the increasing needs of the world’s population ( Khalili et al, 2019 ). Plant diseases are the lead causes of extensive economic losses in the agricultural industry around the world.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, fumigants and fungicides are used to control M. phaseolina infections in crops that can be ineffective and inefficient due to different environmental factors as reported in Abbas et al (2019) . In a work presented by Khalili et al (2019) , a higher dose of fungicides was suggested for an economical yield. An increased dose of these chemicals leads to concerns over the long-lasting harmful impacts of pesticides on human health and ecology as agricultural run-offs contain pesticides which pollute the water resources ( Chamorro et al, 2015 ; Pastrana et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

et al. 2020

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Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.

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Efficacy and cost study of green fungicide formulated from crude beta-glucosidase

Cited by 6 publications

References 40 publications

Biocontrol of Non-Saccharomyces Yeasts in Vineyard against the Gray Mold Disease Agent Botrytis cinerea

Biocontrol of Non-Saccharomyces Yeasts in Vineyard against the Gray Mold Disease Agent Botrytis cinerea

A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

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