A Novel Approach for Automated Strawberry Fruit Varieties Classification Using Image Processing and Machine Learning

Javidan, Seyed Mohamad; Ampatzidis, Yiannis; Vakilian, Keyvan Asefpour; Mohammadzamani, Davood

doi:10.1109/qicar61538.2024.10496652

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…In order to remove the noise, the images obtained from the previous stage are smoothed using conventional methods and then to the next stage which is the same processing and selection of the best feature. Before obtaining the characteristics of the disease, two unsupervised learning methods will be used to cluster the diseases so that the disease can be distinguished from the plant [14][15][16][17][18][19]. Although there has been no mention of automatic clustering in unsupervised learning methods for diagnosing plant diseases, in this research we will try to do this automatically.…”

Section: Preprocessingmentioning

confidence: 99%

“…Manual feature extraction requires a good understanding of the background or domain in which the data was collected, and it allows for informed decisions about which features can be most useful [15][16][17][18][19][20]. In this study, after separating the disease area, meaning extracting the damaged part of the leaf from the input image, features such as color, shape and texture were extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

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Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning

Talab,

Mohammadzamani,

Parashkoohi

2024

Discov Appl Sci

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This study aimed to classify potato disease as early blight, late blight, and healthy leaves using color image processing techniques, data imbalance and feature extraction techniques. To this end, two groups of potato disease leaves with similar symptoms and one group of healthy leaves were considered. A new method based on increasing or decreasing image data using Over-Sampling and Under-Sampling techniques was used to balance them. For this purpose, after separating the diseased area from the leaf surface, the features were extracted. In total, 45 color features, 99 texture features were extracted from each of the three color spaces RGB, l*a*b and HSV, and 6 shape features were extracted from the images. Then classification was done by Random Forest. The classification accuracy results for three proposed models including classification with Original, Over-Sampling and Under-Sampling dataset were 87.89, 91.09 and 96.17%, respectively. Also, the most effective features extracted for the diagnosis of potato diseases were selected by the Relief feature selection algorithm. The results show that texture features contribute more to disease classification and data balancing techniques can increase classification accuracy. In addition, the results show that features extracted from different color spaces can improve disease diagnosis performance and help data engineers to increase the amount of features extracted and compare their performance. This study can be used in future research to classify potato diseases and other plant diseases, and its results can help researchers in their efforts.

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Section: Preprocessingmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning

Talab,

Mohammadzamani,

Parashkoohi

2024

Discov Appl Sci

Self Cite

View full text Add to dashboard Cite

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Application of microscopic image processing and artificial intelligence detecting and classifying the spores of three novel species of Trichoderma

Soltani Nezhad,

Rahnama,

Javidan

et al. 2024

Discov Appl Sci

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Trichoderma is a type of fungus genus usually used in agriculture to prevent and control various plant diseases. One of the ways Trichoderma helps control plant diseases is by producing secondary metabolites that prevent the growth of pathogens. The ability to identify Trichoderma spores is important to detect the presence of this fungus in plants and soil. However, traditional microscopic techniques used for spore detection are time-consuming and tedious, and require the intervention of highly skilled technicians, leading to delayed diagnosis and ineffective control measures. Microscopic image processing techniques have been developed to make the spore detection process intelligent, resulting in a faster and more accurate output. In this study, a microscopic image dataset has been developed, followed by identifying and classifying the spores of three Trichoderma species including T. harzianum, T. atroviride, and T. virens using microscopic image processing techniques. The genetic algorithm was used to identify the most effective visual features for spore classification, including color, texture, and shape. Then, using the identified effective features, the random forest was capable of classifying the spores with an accuracy of 95.38%. Also, the classification precision results for T. harzianum, T. atroviride, and T. virens were obtained as 100, 90.48, and 95.24% respectively. This study showed that texture is the most important visual feature for spore classification. Besides, the findings reveal that instead of deep learning-based methods which require big data for training, traditional feature extraction methods still provide promising results with low computational complexities. The use of such new techniques can help control plant diseases more effectively by providing faster and more accurate detection of Trichoderma spores in crop plants.

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Early detection and spectral signature identification of tomato fungal diseases (Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum) by RGB and hyperspectral image analysis and machine learning

Javidan,

Banakar,

Vakilian

et al. 2024

Heliyon

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A Novel Approach for Automated Strawberry Fruit Varieties Classification Using Image Processing and Machine Learning

Cited by 3 publications

References 14 publications

Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning

Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning

Application of microscopic image processing and artificial intelligence detecting and classifying the spores of three novel species of Trichoderma

Early detection and spectral signature identification of tomato fungal diseases (Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum) by RGB and hyperspectral image analysis and machine learning

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