Disease Of Mango Research Articles

Optimization of Mango Plant Leaf Disease Classification Using Concatenation Method of MobileNetV2 and DenseNet201 CNN Architectures

Purpose: Mango production can be severely impacted by diseases affecting mango plants. By leveraging artificial intelligence, the agricultural sector can automate the analysis of mango leaves to monitor plant health. The goal of this research is to improve the early detection of diseases in mango leaves to allow early treatment to minimize damage to the crops. Methods: This study employs an approach of combining two pre-trained CNN architectures, namely MobileNetV2 and DenseNet201 through concatenation method. To enhance the model’s generalization ability, various image augmentation techniques were applied during the training phase. Result: The model developed in this study achieved great performance in classifying mango leaf diseases with a testing accuracy of 99.25%. This result indicates the effectiveness of the concatenation method by outperforming the accuracy of either MobileNetV2 or DenseNet201 when implemented separately. Novelty: This research introduces a novel strategy by concatenating two pre-trained CNN architectures for mango leaf disease classification, a method not previously explored in this context. The model developed from this study has the potential to serve as a tool for the early detection and treatment of mango leaf diseases.

Mango fruit diseases severity estimation based on image segmentation and deep learning

Plant disease severity is the ratio between the surface area of disease symptoms and the total surface area of the plant unit (e.g. fruit, leaf). It is related to plant disease diagnosis and has several advantages for farmers. It is therefore a key element in the protection and management of plant diseases. In the literature, there are three proposed categories of plant disease severity determination solutions: solutions based on segmentation algorithms, solutions based on classical ML algorithms and those based on DL algorithms. Despite their several advantages, these solutions have a number of limitations, including i) subjectivity in data labeling, ii) loss of information on disease lesion contours during (manual) data labeling, and iii) the proposed solutions have focused on estimating plant disease severity from leaves, although diseases can also affect other parts of the plant, such as fruits. In this paper, we present a solution for estimating the severity of four mango fruit diseases, namely Alternariose, Anthracnose, Aspergillus rot and Stem rot. This solution is based on ResNet50 CNN and uses a dataset automatically labeled by a proposed algorithm based on two segmentation algorithms such as image color space segmentation and image thresholding. The solution has achieved an accuracy, a precision and a F1_score of 97.82%, 97.09% and 97.79%, respectively, on test data It is then deployed in a mobile application with a diagnostic solution we previously proposed. This mobile application will help mango growers, particularly those in Sahelian countries like Senegal, to manage their mango diseases earlier.

(E)-2-Octenal inhibits Neofusicoccum parvum growth by disrupting mitochondrial energy metabolism and is a potential preservative for postharvest mango.

(E)-2-Octenal inhibits Neofusicoccum parvum growth by disrupting mitochondrial energy metabolism and is a potential preservative for postharvest mango.

Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

Melatonin induces resistance against Colletotrichum gloeosporioides in mango fruit via regulation of defense-related genes by MiWRKY45 transcription factor.

Melatonin induces resistance against Colletotrichum gloeosporioides in mango fruit via regulation of defense-related genes by MiWRKY45 transcription factor.

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

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

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

Classification of mango disease using ensemble convolutional neural network

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.

Studies on location specific seasonal survey for digital mapping of important mango diseases Y.R.

Studies on location specific seasonal survey for digital mapping of important mango diseases Y.R.