Recent advances in postharvest technology of Asia pears fungi disease control: A review

Oyom, William; Prusky, D.; Zhang, Zhong; Bi, Yang; Tahergorabi, Reza

doi:10.1016/j.pmpp.2021.101771

Cited by 20 publications

(11 citation statements)

References 49 publications

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“…Penicillium expansum, Botrytis cinerea, Mucor piriformis, Phialophora malorum, Alternaria spp., Cladosporium herbarum and Neofabrea spp. are common pathogenic fungi found in pears [2][3][4][5]. During postharvest storage, pathogens may invade and cause postharvest fruit decay when conditions are suitable [6].…”

Section: Introductionmentioning

confidence: 99%

Fruit bagging reduces the postharvest decay and alters the diversity of fruit surface fungal community in ‘Yali’ pear

Gao

Zhang

et al. 2022

BMC Microbiol

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Background Fruit bagging is an effective technique for fruit protection in the orchard management. Bagging can create a micro-environment for fruit growth and affect fruit quality during storage, in which the diversity of microorganisms may play an important role. Therefore, various methods including biochemistry, analytical chemistry, and bioinformatics methods were used to reveal the influences of fruit bagging on postharvest fruit quality, physiological characters, decay and surface fungal community of ‘Yali’ pear fruit were investigated in this study. Results Fruit bagging significantly decreased the postharvest decay after 15 days of ambient storage. There were no significant differences in fruit firmness, titratable acid and ethylene production rate between the fruit-bagging and non-bagging group after 15 days of storage, while the soluble solids contents (SSC) and respiration rate in non-bagging fruit was significantly higher than that in fruit-bagging after 15 days of storage. Furthermore, the surface microbes of pear were collected and determined by the new generation sequencing technology. The alpha diversity of fungi in non-bagging fruit decreased significantly after 15 days of storage, while there were no significant changes in bagging fruit. Ascomycota and Basidiomycota were the two major phyla detected in the bagging fruit, and the dominant fungal genera were Alternaria (23.7%), Mycosphaerella (17.25%), Vishniacozyma (16.14%), and Aureobasidium (10.51%) after 15 days of storage. For the non-bagging pear, Ascomycota was the only phylum detected, and the dominant genera was Pichia (83.32%) after 15 days of storage. The abundance of Pichia may be regarded as the biomarker to indicate the degree of fruit decay. Conclusions This study showed that fruit bagging could significantly reduce postharvest fruit decay and respiration rate of ‘Yali’ pear. Significant differences were found in fungal composition between bagging and non-bagging pear after storage for 0 or 15 days. Fruit bagging maintained the diversity of fungi on the fruit surface, increased the abundance of non-pathogenic fungi, and even antagonistic fungi such as Aureobasidium, Vishniacozyma, and Mycosphaerella. A reduction in the abundance of pathogenic fungi and incidence of postharvest decay during the storage of ‘Yali’ pear were also recorded. In conclusion, fruit-bagging changed the fungal diversity on fruit surface of ‘Yali’ pear, which had significant effect on reducing postharvest fruit decay, and thus prolong the storage period of ‘Yali’ pears. The future thrust of this study will focus on the isolation of fungi or bacteria from pear fruit surface and identify their roles in causing fruit decay and changing fruit quality during storage.

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Section: Introductionmentioning

confidence: 99%

Fruit bagging reduces the postharvest decay and alters the diversity of fruit surface fungal community in ‘Yali’ pear

Gao

Zhang

et al. 2022

BMC Microbiol

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“…Currently, to preserve crop yield and quality, the strategies used are almost entirely based on the use of synthetic pesticides to prevent, kill, or inhibit phytopathogens, providing a low-cost and temporary solution [ 10 , 11 , 12 ]. However, the excessive use of these compounds increases contaminations and risks for the environment [ 12 , 13 ] and the appearance of new resistant fungi or new endangered hosts, such as animals or even humans [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…A sustainable agriculture promotes greener and safer practices, including the use of natural compounds to reduce the extensive use of synthetic fungicides [ 7 ]. In this context, several studies have been carried out to find alternatives, including biological control agents, formulations based on natural extracts, disinfecting agents, physical methods, among others [ 11 , 14 ]. However, despite the efforts made to combat postharvest fungal infections using an ecofriendly approach, none have been robust, effective, and cost-effective enough to replace the current solutions in pome fruit [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Seaweed Extracts to Control Postharvest Phytopathogenic Fungi in Rocha Pear

Toledo

Félix

Vicente

et al. 2023

JoF

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Fungal infections cause losses amounting to between 20 and 25% of the fruit industry’s total outcome, with an escalating impact on agriculture in the last decades. As seaweeds have long demonstrated relevant antimicrobial properties against a wide variety of microorganisms, extracts from Asparagopsis armata, Codium sp., Fucus vesiculosus, and Sargassum muticum were used to find sustainable, ecofriendly, and safe solutions against Rocha pear postharvest fungal infections. Alternaria alternata, Botrytis cinerea, Fusarium oxysporum, and Penicillium expansum mycelial growth and spore germination inhibition activities were tested in vitro with five different extracts of each seaweed (n-hexane, ethyl acetate, aqueous, ethanolic, and hydroethanolic). An in vivo assay was then performed using the aqueous extracts against B. cinerea and F. oxysporum in Rocha pear. The n-hexane, ethyl acetate, and ethanolic extracts from A. armata showed the best in vitro inhibitory activity against B. cinerea, F. oxysporum, and P. expansum, and promising in vivo results against B. cinerea using S. muticum aqueous extract were also found. The present work highlights the contribution of seaweeds to tackle agricultural problems, namely postharvest phytopathogenic fungal diseases, contributing to a greener and more sustainable bioeconomy from the sea to the farm.

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“…Several species of pears are valued for their edible fruit and juices, while others are cultivated as trees. China is the world’s largest producer and consumer of pears, and its pear cultivation area and output rank first in the world ( Oyom et al., 2022 ). ‘Huangguan’ pear is a mid-early mature pear variety cultivated by China.…”

Section: Introductionmentioning

confidence: 99%

Appearance quality classification method of Huangguan pear under complex background based on instance segmentation and semantic segmentation

Zhang

Shi²,

Zhang³

et al. 2022

Front. Plant Sci.

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The ‘Huangguan’ pear disease spot detection and grading is the key to fruit processing automation. Due to the variety of individual shapes and disease spot types of ‘Huangguan’ pear. The traditional computer vision technology and pattern recognition methods have some limitations in the detection of ‘Huangguan’ pear diseases. In recent years, with the development of deep learning technology and convolutional neural network provides a new solution for the fast and accurate detection of ‘Huangguan’ pear diseases. To achieve automatic grading of ‘Huangguan’ pear appearance quality in a complex context, this study proposes an integrated framework combining instance segmentation, semantic segmentation and grading models. In the first stage, Mask R-CNN and Mask R-CNN with the introduction of the preprocessing module are used to segment ‘Huangguan’ pears from complex backgrounds. In the second stage, DeepLabV3+, UNet and PSPNet are used to segment the ‘Huangguan’ pear spots to get the spots, and the ratio of the spot pixel area to the ‘Huangguan’ pear pixel area is calculated and classified into three grades. In the third stage, the grades of ‘Huangguan’ pear are obtained using ResNet50, VGG16 and MobileNetV3. The experimental results show that the model proposed in this paper can segment the ‘Huangguan’ pear and disease spots in complex background in steps, and complete the grading of ‘Huangguan’ pear fruit disease severity. According to the experimental results. The Mask R-CNN that introduced the CLAHE preprocessing module in the first-stage instance segmentation model is the most accurate. The resulting pixel accuracy (PA) is 97.38% and the Dice coefficient is 68.08%. DeepLabV3+ is the most accurate in the second-stage semantic segmentation model. The pixel accuracy is 94.03% and the Dice coefficient is 67.25%. ResNet50 is the most accurate among the third-stage classification models. The average precision (AP) was 97.41% and the F1 (harmonic average assessment) was 95.43%.In short, it not only provides a new framework for the detection and identification of ‘Huangguan’ pear fruit diseases in complex backgrounds, but also lays a theoretical foundation for the assessment and grading of ‘Huangguan’ pear diseases.

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Recent advances in postharvest technology of Asia pears fungi disease control: A review

Cited by 20 publications

References 49 publications

Fruit bagging reduces the postharvest decay and alters the diversity of fruit surface fungal community in ‘Yali’ pear

Fruit bagging reduces the postharvest decay and alters the diversity of fruit surface fungal community in ‘Yali’ pear

Seaweed Extracts to Control Postharvest Phytopathogenic Fungi in Rocha Pear

Appearance quality classification method of Huangguan pear under complex background based on instance segmentation and semantic segmentation

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