Disease Of Mango Research Articles

Dimethyl trisulfide reduces postharvest anthracnose and enhances mango quality, and a potential molecular mechanism against Colletotrichum gloeosporioides

Mango anthracnose, mainly caused by Colletotrichum gloeosporioides, is the major destructive postharvest disease of mango during storage and transport. Dimethyl trisulfide (DMTS), an organic volatile found in some microorganisms or plants, inhibited growth of C. gloeosporioides in vitro, but its effects on mango anthracnose and its molecular mechanisms of action have not been well characterized. In this study, the EC50 of DMTS against Colletotrichum spp. from mango mainly ranged from 2.3 to 20.0 μL/L. In vivo, the fumigation rates of 20 μL/L of DMTS for 24 h, or 80 μL/L for 3 h or 6 h could effectively reduce severity of anthracnose (natural inoculum) on postharvest mangoes with inhibitory effects of 61.7 %, 65.7 %, and 69.4 %, respectively, as observed 10 days after treatment. Furthermore, there was no detectable DMTS residue in mango skin or flesh, and an overall improvement in the quality of the fruit with higher soluble solids, total sugars, vitamin c, and β-carotene, and lower titratable acidity than the non-treated control. In addition, DMTS could significantly reduce ergosterol content in mycelia of C. gloeosporioides, and gene expression analysis showed DMTS significantly suppressed expression of ergosterol biosynthesis-related genes Cgerg6 and Cgerg11 after mycelia were exposed to DMTS. Knock-out mutants for each of these two genes showed reduced sensitivity to DMTS. After gene complementation in situ, the sensitivity of complementary transformants to DMTS was restored to that of the parental strain. Therefore, we concluded that the genes Cgerg6 and Cgerg11 are involved in an interaction with the antifungal activity of DMTS. This is the first study to demonstrate a control effect of DMTS on mango postharvest anthracnose resulting in reduced disease severity and enhanced fruit quality. Transformant studies also revealed some potential molecular mechanisms of the antifungal activity of DMTS that may lead to improved management of mango postharvest anthracnose.

PREVALANCE AND MANAGEMENT OF SOME MAJOR DISEASES OF MANGO AT AFGOYE AND BALAD DISTRICT IN SOMALIA

The experiments were conducted in different mango orchards of Afgoye and Balad districts in Somalia under the faculty of Agriculture, Zamzam Universityof Science and Technology during February to April, 2020 on prevalence and management of mango diseases. Altogether, seven different diseases viz. Anthracnose, die-back, powdery mildew, sooty mold, red rust, bacterial leaf blight and bacterial leaf spot were recorded. All the diseases were common in both regions but these varied from orchard to orchard. Among the diseases, severity of Anthracnose (35.3%), die-(20.67%), powdery mildew (45.17%), red rust (13.75%) and bacterial leaf sight (54.33%) were found major problem in Balad districts. In Afgoye , Anthracnose (16.033%), sooty mold (18.90%), bacterial leaf blight( 19.833%) and bacterial leaf spot (8.927%) were found the highest severity. fungicides Caroozate 45, Carbazim, respectively, were evaluated and found that Carbazim were good effect in controlling Anthracnose, sooty mold, red rust and bacterial leaf spot. Caroozate 45 was also effective in controlling Anthracnose, die-back, powdery mildew, red rust al leaf spot disease. like similarly weused botanicals fungcide, Such as Neemand Garlic well cake.respectively,were evaluated and found that Neem with Garlic were good effect in controlling Anthracnose, sooty mold, red rust and bacterial leaf spot. Neem was also effective in controlling Anthracnose, die-back, powdery mildew, red rust al leaf spot disease.

The MiTGA1 stimulates MiPR1 expression and H2O2 production to enhance mango disease resistance in response to Bacillus siamensis treatment

Postharvest disease caused by fungi is a major issue that leads to quality decline and economic losses in mango fruit during storage and distribution. TGA transcription factors and pathogenesis-related proteins (PR) play crucial roles in modulating plant tolerance to pathogens. However, the roles of TGAs in the disease resistance of mango fruit remain unclear. Here, we investigated the impact of applying the antagonist Bacillus siamensis (N-1) on disease occurrence and the expression levels of MiTGA1 and MiPR1 genes in “Tainong No. 1” mango. We also explored the molecular mechanism of MiTGA1 interaction with MiPR1 gene. Results demonstrated that N-1 treatment significantly increased the level of hydrogen peroxide (H2O2) and effectively suppressed disease expansion during mango storage at 25 °C. Analyses of transcriptome data and qRT-PCR revealed the obvious up-regulation of MiTGA1 and MiPR1 genes in response to the N-1 treatment. Further subcellular localization identified the MiTGA1 protein as being located in the nucleus. Yeast one-hybrid (Y1H) and dual-luciferase reporter (DLR) assays confirmed that MiTGA1 could bind to the MiPR1 promoter and activate its transcription. Furthermore, transient over-expression of MiTGA1 in mango was found to enhance the accumulation of disease-resistant substances such as H2O2 by modulating MiPR1 expression, thereby bolstering the disease resistance of mango fruit. Our study suggests that MiTGA1 is a promising target gene and its interaction with MiPR1 may contribute to disease resistance induction and decay mitigation in postharvest mango fruit.

Importance, etiology and management of the bacterial black spot disease of mango

Importance, etiology and management of the bacterial black spot disease of mango

Classification of mango disease using ensemble convolutional neural network

Mango is a highly significant fruit crop that thrives in a variety of agro-ecologies around the world. Mangoes are rich in vitamins and minerals. However, its yield is currently severely constrained due to disease and pest infestations. Thus, in order to improve mango fruit quality and productivity, illnesses and insect pests must be detected early on. In this study, we conceived and constructed a mango leaf disease detection mechanism utilizing an ensemble convolutional neural network approach. Healthy and diseased mango leaf images were manually obtained from main producing locations in Amhara Region for Merawi fruit and vegetable research identification. To improve the datasets, several pre-processing procedures (such as image resizing, noise reduction, and image augmentation) were used. To improve classification performance and meet the study's purpose, various segmentation approaches such as k means and Mask R-CNN were applied. Furthermore, following pre-processing and segmentation, features of mango leaf images were retrieved using CNN to obtain important features. The classification model was then constructed using fully-connected layer classifiers on the retrieved features of mango leaf images. The ensemble proposed GoogLeNet and VGG16 based CNN model in the study encompasses various operations, including dataset collection, image preprocessing, noise removal, segmentation, data augmentation, feature extraction, and classification. Upon testing, the model demonstrated impressive performance with 99.87 % training classification accuracy, 99.72 % validation accuracy, and 99.21 % testing accuracy. This indicates the effectiveness of the ensemble approach in achieving high accuracy in image classification tasks.

Field efficacy in controlling mango diseases of antagonist Talaromyces tratensis KUFA 0091 in fresh and dry formulations

Field efficacy in controlling mango diseases of antagonist Talaromyces tratensis KUFA 0091 in fresh and dry formulations

Postharvest diseases of mangoes in Fiji

ABSTRACT Despite considerable research globally on postharvest diseases of mango, virtually no work has been done to determine the occurrence of these diseases and their causal agents in Fiji. This study is the first major field and market survey of postharvest diseases of mango in Fiji. For the field survey, fruits from five local mango cultivars were harvested at each of five locations. Fruits from five imported cultivars were also harvested from one location in Nadi. For the market survey, vendors were selected at five municipal markets for fruit collection, along with ten roadside stalls. For field and market surveys, fruits were incubated at 23°C and assessed for postharvest disease (incidence and severity of body rots and stem end rots) when ripe. Isolations were made from disease lesions and fungal cultures were identified using multilocus sequence typing. High incidences of body and stem end rots were recorded across all surveys. In the field survey of local cultivars, ‘Salusalu’ fruits were found to have the lowest severity of body rot and stem end rot on average, while for imported cultivars, ‘Nam Doc Mai’ had the lowest average severity of these diseases. The market survey showed that ‘Salusalu’ had a lower incidence of anthracnose on the body of fruit compared to all other cultivars, although it did have a surprisingly high incidence of stem end rot at some localities. Fungal isolates from anthracnose lesions in the field and market surveys were identified as Colletotrichum asianum, C. simmondsii and C. fructicola. C. asianum was the predominant species associated with anthracnose symptoms, accounting for 97% of isolations. Lasiodiplodia theobromae and Neofusicoccum parvum were the predominant species found in association with mango stem end rot symptoms. Other species isolated from mango stem end rot in lesser numbers included L. brasiliensis, N. umdonicola and N. kwambonambiense. All of these fungi represent new reports for Fiji.

An overview on major diseases, damage pattern, and management of Mango (Mangifera indica L.) fruit and tree

Mango is one of the most important tropical fruits grown throughout the world particularly in Asia with high economic and nutritional value. However, several diseases and pests cause significant damages to different parts of mango trees and mango fruits, resulting in severe economic losses to farmers and the agricultural sector. Some of the major mango diseases are anthracnose, powdery mildew, die back, and mango malformation. These diseases affect various parts of mango tree, including leaves, flowers, fruits, stems, and roots. The causative organisms of mango diseases include fungi, bacteria, and viruses. Of these, the majority is caused by fungi. Some of the common fungal diseases of mango include anthracnose, powdery mildew, stem-end rot, and sooty mould. Therefore, it is a pressing need to accurately identify the causative organisms for determination of the best management strategy for an effective control. To manage mango diseases, farmers and researchers use several approaches, including cultural practices, chemical control, biological control, and genetic resistance. In this article, the major mango diseases, damage patterns, and management strategies for sustainable mango production has been critically reviewed and presented.

IBSHC: Improved BIRCH‐based segmentation and hybrid classification model for Mango Disease Detection

AbstractMangoes are referred to as the ‘king of fruits’ and are in great demand; yet, in order to optimize profitability, disease control is crucial. Automatic leaf disease segmentation and detection remains challenge due to the variability of symptoms. The basic necessity for any computer‐aided system is an accurate approach for identifying diseases. This paper intends to propose a four‐stage Mango Disease Detection model, IBSHC (Improved BIRCH‐based Segmentation and Hybrid Classification model for mango disease classification) that covers preprocessing, segmentation, feature extraction, and classification to address this detection problem. Initially, preprocessing phase is performed by using the Gaussian filtering process. Then the improved Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) process‐based segmentation is carried out in the segmentation phase. Subsequently, the Colour histogram, shape index histogram, improved significant Local Binary Patterns (LBP), and Resnet are the features extracted during the feature extraction phase. Finally, a hybrid classification model is developed for identifying the healthy and diseased mangoes. The optimized Dilated Residual Networks (DRN) and optimized Long Short‐Term Memory (LSTM) classifiers are employed in this hybrid model, and the PMRFO (Proposed Manta‐Ray‐Foraging Optimization) algorithm is used to optimize the weights of these two classifiers. Finally, the efficiency of the suggested PMRFO is contrasted over other conventional methods, and also the disease detection of the proposed model has better accuracy in each learning percentage like 60th LP = 92.5%, 70th LP = 93%, 80th LP = 94.02%, and 90th LP = 96.5%, respectively.

Effects of Delayed Precooling on the Postharvest Quality and Anthracnose Incidence of “Irwin” Mangoes

Mangoes are typical climacteric tropical fruits with accelerated metabolism, degradation, and senescence after harvest due to the accumulation of field heat. Thus, removing field heat as soon as possible after harvest is essential. In addition, a severe postharvest disease of mango, anthracnose, has been reported to be caused by Colletotrichum gloeosporioides, thus decreasing the shelf life and limiting mango export. This study was performed to investigate the effect of delayed precooling on the quality and incidence of anthracnose during storage of Irwin mango at different operational times after the harvest. Therefore, in this study, Irwin was precooled for 30 min at 3, 6, 12, and 24 h postharvest and immediately stored in a cold room at 5°C. The appearance, anthracnose incidence, respiration rate, ethylene production, firmness, total soluble solids (TSS), titratable acidity (TA), and fruit overripening rate were monitored throughout the shelf life. Early precooling was found to delay anthracnose incidence during the shelf life. Furthermore, the fruit respiration rate during the shelf life was negatively correlated with the precooling delay. The respiration rate of fruits precooled for 3 h after harvest was 8.08 mg CO2 kg–1 h–1 and retained a good appearance throughout the shelf life. Their ripening, reduced firmness, and TA were significantly delayed compared with those of fruits with longer precooling delays, which had a firmness of 19 N, TA of 0.29%, and TSS of 12.6°Brix after 30 d of storage at 5°C, while subsequent warming to 20°C for 3 d, the rate of overripening was significantly lower in fruits with precooling delays of 3 and 6 h than in fruits with a precooling delay of 24 h. Thus, precooling mangoes within 3–6 h of harvest will reduce their respiration rate, delay ripening, decrease anthracnose incidence, preserve quality, and prolong storage life.

Post Harvest Anthracnose of Mango Caused by Colletotrichum gloeosporioides: A Review

Mango (Mangifera indica L.) is an edible stone fruit most widely grown in tropical and sub-tropical regions. It is been cultivated in South Asia around 4000 years. The genus Mangifera consists totally 49 species, but only 41 species are considerable. Mango is the most cultivated and important fruit around the world especially in India. Since ages, the people from ancient India describe mango tree as ‘Kalpavriksha’ which means wish granting tree. Mango is the eight most important fruit crop grown throughout the world which it contains the nutritive values, minerals, vitamins and dietary fibers. Mango is highly perishable due to climacteric nature of the fruit, it produces high amount of ethylene which is known as ‘Ripening hormone’. However, the fruit quality and commercialization were drastically reduced by the fungus anthracnose incited by Colletotrichum gloeosporioides; it causes the post-harvest losses of about 35 to 40 percentage, becaming a major post-harvest disease of mango. Many pre-harvest and post-harvest management practices have been practiced to control mango anthracnose including chemical methods. This review summarizes an overview on exhibiting of this disease, the factors influencing them and the recent management approaches to sustain the fruit quality and maintain the supply chain of mango.

Essential oils on the control of fungi causing postharvest diseases in mango.

The use of fungicides in the postharvest treatment of mangoes has been widespread due to the incidence of pathogens, but awareness of the health risks arising from their use has increased, driving the search for more sustainable treatments. This study aimed to evaluate the activity of antifungal treatment of seven essential oils (EO) against four fungi that cause postharvest diseases in mangoes and define the minimum inhibitory concentration (MIC) and chemical composition, analyzed by gas chromatography (GC-MS). The results showed that the EOs of oregano, rosemary pepper, cinnamon bark, and clove inhibited 100% of the mycelial growth of the studied pathogens, with MIC ranging from 250 to 2000 μL.L-1. The main compound found in oregano was carvacrol (69.1%); in rosemary and pepper oil, it was thymol (77.2%); cinnamaldehyde (85.1%) was the main constituent of cinnamon bark, and the eugenol (84.84%) in cloves. When evaluating the antifungal activity of these compounds, thymol and carvacrol showed greater inhibitory activity against fungi. Therefore, this study showed the great potential of oregano, clove, rosemary pepper, and cinnamon bark essential oil as alternative treatments to synthetic fungicides in controlling postharvest diseases in mangoes.

ASSESSMENT OF PRE HARVEST FRUIT BAGGING TO MANAGE PESTS OF MANGO (MANGIFERA INDICA L.) CV. BENISHAN

– Mango commonly known as king of fruits in India is reported to be affected by 188 species of insects in India. Pest and disease incidence in mango mainly starts from flowering stage and continues till harvesting. Farmers generally rely upon pesticides to control pests and diseases of mango but over a period of time, due to poor management practices and changing climatic conditions, pest incidence in mango increased rapidly which in turn increased pesticide sprayings. This resulted in increased production cost and pesticide residues on fruit which is a major hurdle for mango export and also few pests like fruit fly cannot be controlled by insecticides alone. Hence, on farm trial was conducted during summer, 2022 to assess the performance of fruit bags in reducing pests and diseases of mango against farmers practice i.e., spraying of pesticides. Fruit bagging method effectively controlled fruit fly, thrips and bacterial black spot diseases whereas in farmers practice 11.28%, 2.17% and 23.22% were recorded respectively despite of regular sprayings. There is a significant difference between fruit bagging and farmers practice and fruit bagging method found to be best in reducing pest and disease incidence. Higher yield was recorded in fruit bagging, i.e. 9 t/ha whereas in farmers practice it was 7.5t/ha. Even though cost of production is high in fruit bagging i.e., Rs 104000/- hectare due to good market price to bagged fruits, farmers got more net returns i.e., Rs 3,42,300/- hectare. Reduced sweetness in the fruit and difficulty in bagging of all the fruits in older trees are the cons in fruit bagging method. Overall, pre harvest fruit bagging found to be one of the best management practice in mango which not only reduces pest and disease incidence but also to get higher yield and quality fruits.

Deep Learning based Mango Leaf Disease Detection for Classifying and Evaluating Mango Leaf Diseases

Mango is one of the important commercial crop in the world. It provides nutritional and financial support to human life. Different diseases of leaves impact the health of the mango crops. The early and proper pest control measurement can prevent large output losses. We propose an automated inspection and classification of disease-affected mango leaves that uses Deep Learning (DL) model. Our DL model-empowered Convolutional Neural Network (CNN) architecture is trained with an extensive image dataset of mango leaves portraying a variety of disease indications at both low and high-resolution images. The objective is to be able to identify accurately the disease type on mango leaves including Bacterial Canker, Powdery mildew, Anthracnose, Gall midge, and Sooty mould. Crops can develop gradual immunity with reasonable pest control and can purposively shaped them against constantly evolving environment. The proposed system will be effective and it will definitely prove a facile system to be used as a key component of a novel precision agriculture system as will be presented in our future work. The performance of the proposed system is augmented through the utilization of transfer learning techniques and pre-trained models, including VGG-16, MobileNet, Googlenet, YoloV8, and EfficientNet. These Deep Learning models not only offer an accurate and efficient approach for classifying diseases in mango leaves but also provide valuable insights into the severity of the identified diseases. Utilizing this information to support farmers and agricultural professionals in making informed decisions pertaining to disease management and treatment strategies can significantly contribute to the sustainable growth of mango crops. The development and implementation of such automated technologies have the potential to revolutionize the monitoring of mango crop health, enabling early disease detection and enhancing crop yields.

Enhancing Disease Management in Mango Cultivation: A Machine Learning Approach to Classifying Leaf Diseases

This study explores the application of machine learning techniques in the agricultural domain, focusing on the classification of two common diseases in mango leaves: Powdery Mildew and Sooty Mould. Utilizing the MangoLeafBD dataset, the research employs a Gradient Boosting Classifier, enhanced with mean shift image segmentation and Hu moments for feature extraction. The performance of the model was rigorously evaluated through 5-fold cross-validation, yielding insights into its accuracy, precision, recall, and F1-score. The results demonstrate moderate success, with the highest accuracy and precision observed in the initial fold, indicating the model's potential for reliable disease identification. The study addresses the challenge of distinguishing between diseases with similar symptomatic appearances, offering a novel, data-driven approach for disease management in mango cultivation. This research contributes to the growing field of precision agriculture, highlighting the potential of machine learning in enhancing disease diagnosis and treatment strategies, thus supporting sustainable agricultural practices.

Application of support vector classifier for mango leaf disease classification

In India, Mango is the fruit of high economic and ecological importance as it exports in large quantities. 1000 varieties of mangoes are cultivated and mostly supported commercially. Among all the Indian fruits, mangoes are highly demand. In majority of the Indian region, mango crops are suffering from several diseases that reduce both the production and the quality and parallel reduces its value on the international market. Mangoes are highly affected by number of diseases, which hamper its appearance, taste and has huge impact on the economy the Indian commercial growth rate has not raised. Manually identifying those disease is a complex task and time consuming, since lack of knowledge, poverty, infrastructure and the facilities the identification of the disease in earlier stages are not done by the farmers. In recent years, the plant pathologists apply different techniques to identify the diseases but then again these techniques are time consuming and relatively expensive for mango growers and the solutions proposed are often not very accurate and sometimes biased. The disease has to diagnosed in order to provide solution to the farmers to increase the productivity with high quality. Currently, researchers have proposed several solutions to diagnosis of mango diseases automatically to gain high returns. The use of machine learning algorithms to identify diseases of plants from leaf photos is a very exciting field for advancement and research has carried in the proposed system using Support vector machine. Using non-linear SVC, achieved the accuracy of 88% for the dataset.

Convolutional neural networks and histogram-oriented gradients: a hybrid approach for automatic mango disease detection and classification

Convolutional neural networks and histogram-oriented gradients: a hybrid approach for automatic mango disease detection and classification

Integrating case-based and rule-based reasoning for diagnosis and treatment of mango disease using data mining techniques

Integrating case-based and rule-based reasoning for diagnosis and treatment of mango disease using data mining techniques

Management of Fungal Pathogens Associated with Mango Diseases using Organic Materials

Mango (Mangifera indica) is one of the most economically important tropical fruits, but it is susceptible to various fungal pathogens that cause significant yield losses and reduce fruit quality. Traditional management strategies for these fungal diseases often involve the use of synthetic fungicides, which can have adverse effects on the environment and human health. Therefore, there is a growing need to explore sustainable and organic alternatives for managing these pathogens. This study aims to evaluate the efficacy of organic materials in controlling fungal pathogens associated with mango diseases. Various organic materials of different plant extracts were tested against common fungal pathogens. The results of the study demonstrated the potential of organic materials in managing mango fungal diseases. Plant extracts derived from neem (Azadirachta indica), garlic (Allium sativum), and ginger (Zingiber officinale) exhibited strong antifungal activity against the tested pathogens. Overall, the findings of this study suggest that organic materials can be effective tools for managing fungal pathogens associated with mango diseases. Incorporating these organic management strategies into mango production systems can contribute to sustainable agriculture practices, reduce chemical inputs, and promote the production of high-quality, disease-free mangoes.

An Automatic Non-Destructive External and Internal Quality Evaluation of Mango Fruits based on Color and X-ray Imaging with Machine Learning and Deep Learning Based Classification Models

Quality evaluation of food products, agricultural produce to be specific, has gained momentum from past few decades due to the increased awareness among consumers across the world. This has resulted in the increased emphasis on the development and use of quality assessment techniques in food industry. Moreover, there is a need to automate the quality monitoring of agricultural produce like fruits and vegetables which is otherwise done manually in developing countries hence labor intensive, time consuming and subjective in nature. This paper presents an empirical analysis to build a rapid, robust, real-time, non-destructive computer vision based quality assessment model for mango fruits. The work employs the automatic disease classification of mango fruits based on machine and deep learning models. Firstly, the dataset of colored mango fruits images with 2279 images falling into three classes and another dataset of soft X-ray images of mango fruits with 572 images belonging to two quality classes are developed for detecting external and internal defects, respectively. The multilayer perceptron neural network (MLP NN) with two hidden layers, which may be considered as the starting point for deep learning technique, is proposed as machine learning model to classify the color images of mango fruits into one of three external quality classes with 95.1% accuracy and also to classify the soft X-ray images into two internal quality classes with 97.5% accuracy. In order to step out of feature engineering, actual deep learning convolutional neural network (CNN) models, a customized CNN model and pre-trained CNN models, VGGNet (VGG16) and DenseNet121 were also explored for mango disease classification. The maximum validation accuracy of custom CNN was found to be with 91.52% and 98.7% for color and augmented X-ray images, respectively. The classification accuracy of pre-trained models were found to be reasonably good for the color images but exhibited high variability in results and made it difficult to draw a general conclusion for the proposed datasets. However, the proposed MLP NN model based on few basic intensity and geometric features and also the proposed customized CNN model were found to be the best models and they outperform the state of the art reported in the literature.