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

Ávila-George, Himer; Valdez-Morones, Topacio; ́erez-Espinosa, Humberto P; ́arez, Brenda Acevedo-Ju; Castro, Wilson

doi:10.14569/ijacsa.2018.090873

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Tobacco is one of the largest agricultural products that is widely traded throughout the world, including in Indonesia [1]. The part of tobacco that is widely used in tobacco leaves, these leaves are used as raw material for cigarettes [2]. Indonesia itself has several tobacco-producing regions including Palembang, Central Java, East Java, North Sumatra, and West Sumatra.…”

Section: Introductionmentioning

confidence: 99%

“…Internal factors are more directed to the sense of smell (human sensory), testing with smoking tests, and chemical analysis. While external factors are more directed to the grader's sense of sight which is determined based on color, the level of maturity of the tobacco leaves, the texture of the surface, the size, and the shape of the tobacco leaves [2]. However, the sense of smell and the sense of sight cannot be used to fully classify tobacco.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN)

Rachmad¹,

Hapsari²,

Setiawan³

et al. 2023

Advances in Intelligent Systems Research

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Tobacco is one of the largest agricultural products and is widely traded in the world market, including in Indonesia. In Indonesia, tobacco leaves are used as raw material for cigarettes which are mostly produced by cigarette companies. The quality of tobacco leaves greatly affects the quality of cigarettes, this is because the condition of tobacco leaves is influenced by several factors including pests, diseases, and climate. This study uses the Gray Level Co-Occurrence Matrix (GLCM) method for texture feature extraction, while for classification uses the K-Nearest Neighbor (KNN) method to classify the quality of tobacco leaves. The data used in this study is the image of tobacco leaves taken directly in TonDowulan Village, Plandaan District, Jombang Regency at the age of the leaves of approximately 2 months. Tobacco leaf images used were 300 images consisting of 3 classes, namely Normal, Perforated, and Withered based on the level of leaf damage. The GLCM features used are Contrast, Correlation, Energy, Homogeneity, and Entropy which will then be classified using the KNN method where before performing feature extraction the data must be processed first at the preprocessing stage. The result of the training using GLCM and K-NN feature extraction produces the highest accuracy value when the neighbor value 1, pixel distance 3, and k-fold 2 are 83.33%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN)

Rachmad¹,

Hapsari²,

Setiawan³

et al. 2023

Advances in Intelligent Systems Research

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show abstract

“…There are variations of artificial neural networks to perform classification tasks, pattern recognition and prediction [6]. Artificial neural networks stand out as an automatic learning technique, due to their ease of use and comprehension compared to statistical methods, and their good performance in different automatic learning tasks [7] [8].…”

Section: Introductionmentioning

confidence: 99%

The Use of ICTs in the Digital Culture for Virtual Learning of University Students Applying an Artificial Neural Network Model

Rocha¹,

Zela²,

Torres³

et al. 2022

IJACSA

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Artificial neural networks are mathematical models of artificial intelligence that intend to reproduce the behavior of the human brain and whose main objective is the construction of systems that are capable of demonstrating certain intelligent behavior. The purpose of the investigation is to determine the influence of the use of Information and Communication Technologies (ICTs) in the digital culture in the learning process of university students in Peru and Bolivia in the context of the Coronavirus -COVID 19 sanitary emergency, through the application of artificial neural network models. The investigation has a quantitative focus, the applied type, with a correlational level and a non-experimental design. Data was recollected by means of a digital questionnaire, applied to students of two universities. The population is composed of 3980 students of the Universidad Privada Domingo Savio (UPDS, Tarija, Bolivia) and 1506 of the Universidad Nacional de Moquegua (UNAM, Moquegua, Peru). The sample consists of 496 students. The hypothetical-deductive and the artificial intelligence methods were used. It was determined that the ability to install software and data protection programs, the use of mobile devices for academic purposes and the command of specialized software are the most influential factors in the digital culture of the students at UNAM and UPDS.

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Food fraud detection in Octopus mimus using hyperspectral imaging and machine learning techniques

Vera,

Avila-George,

Mogollón

et al. 2024

Neural Comput & Applic

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

Cited by 5 publications

References 13 publications

Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN)

Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN)

The Use of ICTs in the Digital Culture for Virtual Learning of University Students Applying an Artificial Neural Network Model

Food fraud detection in Octopus mimus using hyperspectral imaging and machine learning techniques

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