Evaluation of Expert Systems Techniques for Classifying Different Stages of Coffee Rust Infection in Hyperspectral Images

Castro, Wilson; Oblitas, Jimy; Maicelo, Jorge L.; Ávila-George, Himer

doi:10.2991/ijcis.11.1.8

Cited by 20 publications

(6 citation statements)

References 22 publications

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“…The research methodology, which was based on [42], is shown in Figure 1. In general, data were first extracted from image samples to obtain feature vectors organized by each class and vector space.…”

Section: Methodsmentioning

confidence: 99%

Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces

et al. 2019

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is a growing interest in the use of non-contact techniques to automate this process. Machine learning techniques, such as artificial neural networks, support vector machines (SVMs), decision trees, and K-nearest neighbor algorithms, have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, the combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a fivefold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of increased complexity. INDEX TERMS Cape gooseberry, artificial neural networks, support vector machines, decision trees, K-nearest neighbors, color spaces, PCA, multiclass confusion matrix.

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Section: Methodsmentioning

confidence: 99%

Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces

et al. 2019

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“…In the results of this paper, the classification accuracy of PLSDA (all above 94.08%) is also very high, and it can also be applied to the classification of young and old leaves of T. sinensis . Castro et al (2018) used hyperspectral images to classify different stages of coffee rust infection. They used three classifiers (DT, SVM, and K-nearest neighbor), and finally the SVM-based classifier provided the best performance.…”

Section: Discussionmentioning

confidence: 99%

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

Song

Niu

et al. 2022

Front. Plant Sci.

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Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis. In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild.

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“…2) Feature extraction: Similar to that presented by Castro et al [14], a visual evaluation scale was established and three levels of tobacco leaf damage caused by blue mold are proposed. In Table II, the three levels of damage are described, from a healthy stage, through initial damage, to advanced damage showing large necrotic areas.…”

Section: ) Preprocessingmentioning

confidence: 99%

Using Artificial Neural Networks for Detecting Damage on Tobacco Leaves Caused by Blue Mold

Ávila-George¹,

Valdez-Morones²,

́erez-Espinosa³

et al. 2018

ijacsa

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Abstract-Worldwide, the monitoring of pests and diseases plays a fundamental role in the agricultural sustainability; making necessary the development of new tools for early pest detection. In this sense, we present a software application for detecting damage in tobacco (Nicotiana tabacum L.) leaves caused by the fungus of blue mold (Peronospora tabacina Adam). This software application processes tobacco leaves images using a pattern recognition technique known as Artificial Neural Network. For the training and testing stages, a total of 40 images of tobacco leaves were used. The experimentation carried out shows that the developed model has accuracy higher than 97% and there is no significant difference with a visual analysis carried out by experts in tobacco crop.

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Evaluation of Expert Systems Techniques for Classifying Different Stages of Coffee Rust Infection in Hyperspectral Images

Cited by 20 publications

References 22 publications

Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces

Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

Using Artificial Neural Networks for Detecting Damage on Tobacco Leaves Caused by Blue Mold

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