Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis

Asim, None Muhammad; Ullah, Saleem; Razzaq, Abdul; Qadri, Salman

doi:10.1080/10942912.2022.2158863

Cited by 11 publications

(5 citation statements)

References 27 publications

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“…Among these architectures (VGG-16, Inception V3, ResNet50, and EfficientNet-B3), they observed that the EfficientNet-B3 architecture exhibited superior performance, achieving an accuracy of 94.93% on the test dataset. Another recent work provided an automated approach for detecting 12 kinds of guava leaf images [11]. Multiple ML classifiers utilized in this research, namely the instant base identifier, random forest, and meta bagging classifiers.…”

Section: Related Workmentioning

confidence: 99%

Guava fruit disease identification based on improved convolutional neural network

Mahamudul Hashan,

Tariqur Rahman,

Avinash

et al. 2024

IJECE

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Guava fruit cultivation is crucial for Asian economic development, with Indonesia producing 449,970 metric tons between 2022 and 2023. However, technology-based approaches can detect disease symptoms, enhancing production and mitigating economic losses by enhancing quality. In this paper, we introduce an accurate guava fruit disease detection (GFDI) system. It contains the generation of appropriate diseased images and the development of a novel improved convolutional neural network (improved-CNN) that is built depending on the principles of AlexNet. Also, several preprocessing techniques have been used, including data augmentation, contrast enhancement, image resizing, and dataset splitting. The proposed improved-CNN model is trained to identify three common guava fruit diseases using a dataset of 612 images. The experimental findings indicate that the proposed improved-CNN model achieve accuracy 98% for trains and 93% for tests using 0.001 learning rate, the model parameters are decreased by 50,106,831 compared with traditional AlexNet model. The findings of the investigation indicate that the deep learning model improves the accuracy and convergence rate for guava fruit disease prevention.

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Section: Related Workmentioning

confidence: 99%

Guava fruit disease identification based on improved convolutional neural network

Mahamudul Hashan,

Tariqur Rahman,

Avinash

et al. 2024

IJECE

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“…In conclusion, the proposed technique provided accurate and better results than existing approaches. In [38] the study is about the feature extraction and feature fusion techniques of guava plant images. The 12 classes of guava leaf has been used for the self-oriented dataset of 12 guava varieties from the orchard of Pakistan.…”

Section: Literature Reviewmentioning

confidence: 99%

Plant leaf vein and outline feature extraction using fractal and computer vision approaches

Tabassum

2024

Research Journal Of Computer Science And Information Technology

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The study focuses on utilizing plant leaf characteristics for plant identification and disease detection. Leaves are pivotal for gathering information about plants. Leveraging computer vision and smart agricultural technologies, the proposed model discerns venation and texture features in various plant leaves. This research utilized a modified dataset derived from the Flavia leaf image dataset, comprising images of 32 different plant species. The dataset was divided into two subsets (one with 1907 images and another with 1000 images) to differentiate between tuned and untuned image processing. Techniques such as GLCM, LBP, Gabor filters, Fractal Dimension, and box counting were employed to extract leaf texture features, including venation patterns. The study conducted four experiments with training and testing splits of 70/30 and 80/20. A novel method combining SVM with fractal dimension analysis was benchmarked against six classifiers (Random Forest, KNN, DNN, Naïve Bayes, Decision Tree, and SVM), achieving an impressive accuracy of 88% and a Fractal Dimension of 1.8709. This research holds significant potential for advancing digital and modern agriculture, particularly in the early detection of plant diseases and accurate plant identification.

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“…We apply it to the trial of Malaria cell grouping, and accordingly, the model achieves 98.61% order exactness under a lower multifaceted design [ 7]. Overall, the fusion feature technique on the leaves has been discussed with an accuracy of 93% with better classification and identification results [22]. Virtualization Machines have been used with fuzzy techniques in the inference system [23].…”

Section: Related Workmentioning

confidence: 99%

Classification of Microscopic Malaria Parasitized Images Using Deep Learning Feature Fusion

Asim

2023

Research Journal Of Computer Science And Information Technology

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An infectious disease that causes a chronic and potentially life-threatening infection caused by microorganisms of the Plasmodium class, is malaria, or malarial disease. It is critical to detect the presence of Malaria parasites as early as possible to ensure that antimalarial treatment is adequate to cure the particular type of Plasmodium. This is to reduce death rates and to focus on various infections in the event of an adverse outcome. The purpose of this study was to develop an artificial intelligence approach capable of separating parasitized erythrocytes from normal basophilic erythrocytes as well as platelets overlying the red blood cells to overcome the high cost of Ma-laria diagnostic equipment. The tone and texture characteristics of erythrocyte images were extracted using histo-gram thresholds and watershed methods, and then fused with Squeeze Net and ShuffleNet algorithms. The measures included planning, preparing, approving, and testing Deep Convolution Neural Network Segmentation without preparation using a graphic processor unit. A total of 96 percent accuracy and specificity was obtained for the position of malaria in red blood cells based on the results of all of the tests. It has been demonstrated that deep learning can be effective in the field of clinical pathology. This provides new directions for development as well as increasing awareness of researchers in this field.

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Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis

Cited by 11 publications

References 27 publications

Guava fruit disease identification based on improved convolutional neural network

Guava fruit disease identification based on improved convolutional neural network

Plant leaf vein and outline feature extraction using fractal and computer vision approaches

Classification of Microscopic Malaria Parasitized Images Using Deep Learning Feature Fusion

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