First Draft Genome Sequence Resource of a Strain of Pseudocercospora fijiensis Isolated in North America

Black Sigatoka disease, caused by the fungus Pseudocercospora fijiensis, is one of the most devastating diseases of banana around the world. Fungicide applications are the primary tool used to manage black Sigatoka, but fungicide resistance in P. fijiensis, as in other fungal pathogens, is one of the major limitations in the efficient management and prevention of this disease. In the current study, we present the draft genome of P. fijiensis strain IIL-20, the first genomic sequence published from a strain of this fungus isolated in North America. Bioinformatic analysis showed putative genes involved in fungus virulence and fungicide resistance. These findings may lead us to a better understanding of the molecular pathogenesis of this fungal pathogen and also to the discovery of the mechanisms conferring fungicide resistance.

Essential Tea Tree Oil As a Tool to Combat Black Sigatoka in Banana

Black Sigatoka disease, caused by Mycosphaerella fijiensis, is considered the most damaging and costly disease of commercial banana and plantain and is mainly controlled by intensive sprays of synthetic fungicides. Essential tea tree oil derived from Melaleuca alternifolia plant was found to be effective against a wide range of plant pathogenic fungi including black Sigatoka in conventional production systems and was as effective as synthetic fungicides such as tridemorph, difenoconazole, trifloxystrobin and azoxystrobin. This paper provides evidence that tea tree oil offers an attractive alternative for controlling black Sigatoka in banana plantations.

Challenge Faced in K-Means Clustering for the Choice of Automatic K-Value for Segmentation of Black Sigatoka Disease in Banana Leaves

Clustering is defined as grouping similar items . The three types of machine learning techniques are supervised, unsupervised and semi-supervised. In unsupervised technique, there are no class labels given to the input data. Clustering is a type of unsupervised learning technique. Recently clustering is applied in many fields such as medicine, agriculture, biology, computers, finance and robotics. Black sigatoka is a bacterial disease occurring commonly in banana plants .The research currently focuses on segmenting the disease area from non-diseased area.The segmentation class training is done via Trainable Weka Segmentation and we also do segmentation using k-means algorithm. In this paper we propose a novel approach for extraction of the black sigatoka diseased area on banana leaves from images using pixel color values and grouping them into their respective clusters accordingly. This is a segmentation cum clustering algorithm. The novel approach has been proposed to overcome the shortfall of k-means clustering when segmenting using automatic value selection for k-means by using silhouette values.Using this novel approach its easy to cluster and segment at the same time. The segmented image from this algorithm can be used in disease classification tasks.

Prediction of Black Sigatoka Disease in Banana Plants By Data Mining Classification Techniques using Scikit for Python

Agriculture has been evolving since humans started cultivating plants for food consumption. As the agriculture field evolves, the disease control measures too have evolved. Now in this modern era, disease in plants can be easily identified using computers. Data mining is the process of obtaining the useful information from the data. Before the electronic era, diseases in plants are identified just by seeing the symptoms of the plants. Similarly, we can identify the diseases in plants using data mining by supplying the disease symptoms data and classify them accordingly. The purpose of this paper is focusing on the prediction of the diseases from images of black sigatoka disease and uses the following methods: MultilayerPerceptrons, SVM,KNeighborsClassifier,K-NeighborsRegressor, Gaussian Process Regressor, Gaussian Process Classifier, GaussianNB, Decision Tree Classifier, Decision Tree Regressor, linear models such as Linear Regression, RidgeCV, Lasso, ElasticNet, Logistic RegressionCV, SGD Classifier, Perceptron and Passive Aggressive Classifier and ensemble models of the above classifiers. The results are compared, and multilayer perceptron model is seen to give better results for individual classifiers and ensemble of week classifiers gives better results when ensembled. In future, a new hybrid algorithm would be used from the above algorithms for attaining better accuracy. The scikit is a library used for classification, clustering, regression, dimensionality reduction,model selection and preprocessing. Our paper discusses various classifiers used in scikit-learn library for Python and their ensembling is done. This can be applied to all the classification tasks. Classification is done for classifying the black sigatoka disease in banana from healthy leaves.This disease is the most vulnerable one among banana plants.

Inelastic scattering imaging system for Pseudocercospora fijiensis detection

This work sought to develop an inelastic scattering imaging system based on Raman spectroscopy for the detection of the fungal phytopathogen, Pseudocercospora fijiensis, which causes Black Sigatoka disease in banana crops, very important in Colombian agro-industrial economy. This system consists of a modified stereoscope with an optical setup able to simultaneously capture spectral images together with its Raman spectra. The camera has two different bandpass filters attached, centered in the spectral region of C=O stretching of Chitin and the equatorial bending vibration of beta-1,3-glucan, molecules of the fungal cell wall. In this way, the system can get images with unique spectral features, suitable for training a convolutional neural network in order to get a recognition pattern of the fungal strain growing in the PDA agar. As a result, the instrument was able to detect the presence of P.fijiensis over the culture media.