Identification and Classification of Botrytis Disease in Pomegranate with Machine Learning

Sánchez, Magdalena; Miramontes-Varo, Veronica; Chocoteco, J. Abel; Vidal, Vicente

doi:10.1007/978-3-030-52246-9_43

Cited by 7 publications

(1 citation statement)

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“…ere are numerous image processing methods to achieve this and are used for processing the images and identifying the disease. Here, different leaf disease classification is performed, taking, for example, leaves of pomegranate [1], grapes [2], tomato [3], and custard apple [4] for disease analysis. Figure 1 shows the basic image processing steps for classification.…”

Section: Introductionmentioning

confidence: 99%

A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

Patil¹,

Burkpalli²

2021

Advances in Human-Computer Interaction

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Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.

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Section: Introductionmentioning

confidence: 99%