Apple microbial communities and differences between two main Chinese producing regions

Microbes on fresh apples are closely associated with fruit disease, preservation and quality control. Investigation into the microbial communities on apples from different producing regions could reveal the microbial specificity and help disease prevention and quality control. In this paper, the apple surface microbes of forty-four samples from two main Chinese apple-producing regions, Bohai Bay (BHB) and the Loess Plateau (LP), were investigated by sequencing fungal internal transcribed spacer (ITS) and bacterial 16S rRNA hypervariable sequences. BHB and LP apples contained significantly different bacterial and fungal communities. BHB apples had a higher fungal diversity than LP apples. A total of 102 different fungal and bacterial taxonomies were obtained between apples from the two regions, in which 24 genera were predominant. BHB apples had higher phytopathogenic fungal genera, such as Tilletiopsis, Acremonium, Candida and Phoma, indicating the higher phytopathogenic risks of apples from the humid climate of the BHB region. LP apples contained more bacterial genera identified as gut microbes, indicating the potential risks of contaminating apples with foodborne pathogens in the arid environment of the LP. This study highlighted the environment-oriented microbial specificity on apples from two main apple-producing regions, and provided a basis for further investigation.

A ROBUST DEEP LEARNING APPROACH TO ENHANCE THE ACCURACY OF POMEGRANATE FRUIT DISEASE DETECTION UNDER REAL FIELD CONDITION

Pomegranate fruits are infected by various diseases and pests, which negatively affect food security, productivity, and quality. Recent advancements in deep learning with Convolutional Neural Networks (CNNs) have significantly improved the accuracy of fruit disease detection and classification. The main objective of this investigation is to find the most suitable deep-learning architecture to enhance fruit disease detection and classification accuracy. The current study proposed an efficient deep learning-based approach to detect the most prominent diseases of pomegranate such as bacterial blight, anthracnose, fruit spot, wilt, and fruit borer. For experimentation, a total of 1493 stagewise diseases development images of fruits and leaves are captured via a camera of an interval of 25 days for a total of six months duration. Additionally, extensive data augmentation was performed to increase the dataset, data diversity and to achieve a more robust model for disease detection. For this, the performance of three CNN-based architectures i.e., ResNet50, ResNet18, and Inception-V3 on a real field environment dataset was measured. Experimental results revealed that the proposed CNN-based ResNet50 architecture has effectively detected and classified five different types of diseases whose symptoms are not well defined and with the capability to deal with complex backgrounds. The optimized ResNet50 model achieved 97.92 % test accuracy over ResNet18 (87.5 %) and Inception-V3 (78.75 %) on learning rate 0.001. The multiclass cross-entropy loss function is applied for determining the error rate. To deal with CNN ‘Black Box’ problem Grad-CAM model can be used in the future. The proposed method will help the agricultural industry in detecting the most prominent diseases of pomegranate, which are likely to cause a decrease in productivity, thereby avoiding economic loss.

Antagonistic activity of bacteria from wild honey against Colletotrichum musae, and testing of wild honey as biopesticide spray to control banana anthracnose

Anthracnose is a foliar and fruit disease caused by Colletotrichum spp. affecting a wide range of crops. Infection occurs early followed by quiescence in fruits, such as in banana, where chemical-based pesticides are used as a dependable fungal control for many years. There is an increasing need for a safe control and as implicated in the Organic Agriculture Act of 2010 (RA 10068) in the Philippines. This scenario drove the use of alternative pest control such as the use of biologicals and natural products. In this study, seven bacteria were isolated from wild honey, produced by Apis mellifera, wherein four (BC2, BC3, BC6 and BC7) were found to be an effective antagonist against Colletotrichum musae in in vitro conditions. These bacteria were identified to belong to the genus Lactobacillus spp. (BC2, BC3, BC7) and Bacillus spp. (BC6) based on sugar utilization tests, morphological and cultural growth in PDPA. For the in vivo test, different dilutions of wild honey were used and it was found out that lower concentrations were effective as biopesticide spray to prevent anthracnose infection. Lastly, we report herewith the first isolation of bacteria with biological control potential from wild honey, and to apply the raw or natural product as biopesticide in postharvest fruits.

PFDINN: Comparison between Three Back-propagation Algorithms for Pear Fruit Disease Identification

The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Scaled conjugate gradient (SCG-BP), Resilient (R-BP) and Bayesian regularization (BR-BP), was used in the identification process. Pear fruit was taken as the experiment case during this work with three classifications of diseases, namely fire blight, pear scab, and sooty blotch, as compared to healthy pears. PFDINN framework was trained and tested using 2D pear fruit images collected from the Fruit Crops Diseases Database (FCDD). The presented framework achieved 94.6%, 97.3%, and 96.3% efficiency for SCG-BP, R-BP, and BR-BP, respectively. An accuracy value of 100% was achieved when the R-BP learning algorithm was trained for identification.

An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh

Bangladesh, being a densely populated country, hinges on agriculture for the security of finance and food to a large extent. Hence, both the fruits’ quantity and quality turn out to be very important, which can be degraded due to the attacks of various diseases. Automated fruit disease recognition can help fruit farmers, especially remote farmers, for whom adequate cultivation support is required. Two daunting problems, namely disease detection, and disease classification are raised by automated fruit disease recognition. In this research, we conduct an intense investigation of the applicability of automated recognition of the diseases of three available Bangladeshi local fruits, viz. guava, jackfruit, and papaya. After exerting four notable segmentation algorithms, [Formula: see text]-means clustering segmentation algorithm is selected to segregate the disease-contaminated parts from a fruit image. Then some discriminatory features are extracted from these disease-contaminated parts. Nine noteworthy classification algorithms are applied for disease classification to thoroughly get the measure of their merits. It is observed that random forest outperforms the eight other classifiers by disclosing an accuracy of 96.8% and 89.59% for guava and jackfruit, respectively, whereas support vector machine attains an accuracy of 94.9% for papaya, which can be claimed good as well as attractive for forthcoming research.

Control of Papaya Rot Disease by Using Exophytic and Endophytic Fungi Which is Environmentally Friendly

Papaya fruit disease has not been known with certainty the cause of the disease, and until now the appropriate control strategy has not been determined. The results of microscopic identification of rot disease in papaya fruit caused by the fungus Lasiodiplodia theobromae. The most exophytic fungi found in the study were Rhizopus sp. as many as 69 isolates, followed by Aspergillus niger as many as 6 isolates, only Actinomyces israelii only 6 isolates while the other Actinomycetes (Actinomadura cremea, Streptomeces sp., and Micromonospora sp.) each had one isolate. While the endophytic fungi were found Rhizopus sp. as many as 30 isolates, followed by A. niger with 18 isolates, and finally Agromyces ramosus (Actinomycetes) and Trichoderma sp, each with 3 isolates. The highest prevalence was obtained from the fungus Rhizopus sp. The diversity index and the dominance index on exophytic microbes were 2.45 and 0.4078, respectively. The index of diversity and dominance of endophytic microbes were 1.876 and 0.58, respectively. The results of the analysis of the inhibitory power of exophytic and endophytic microbes in vitro, it turns out that almost all have competitive inhibition as well as Trichoderma sp. which has a zone of inhibition means that it is antibiotic and also competitive. Most Actinomycetes have no inhibitory power against pathogens (L.theobromae). The results of the in vivo inhibition test showed that the highest and best inhibitory power was obtained from treatment E (Trichoderma sp.).

A STUDY OF TOMATO FRUIT DISEASE DETECTION USING RGB COLOR THRESHOLDING AND K-MEANS CLUSTERING

The agriculture field plays vital role in development of smart India. To increase economic level the production of fruits, crops and vegetables can use CAD technique using image processing tools. Identifying diseases in fruits is an image processing’s big challenging task. This can done by continuous visual photos or videos monitoring system. The automated image processing research helps to control the pesticides on fruits and vegetables. In this paper we focus to detect the diseases of tomato at earlier stage. The proposed system shows how different algorithms such as color thresholding segmentation techniques and K-means clustering are used. In proposed system shows the K-means Clustering is better than RGB color based colorthresholder method for detecting tomato diseases in beginning stage.