Medicinal and poisonous plants classification from visual characteristics of leaves using computer vision and deep neural networks

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102683 - 102683

Опубликована: Июнь 12, 2024

Poisonous plants are the third largest category of poisons known globally, which pose a risk poisoning and death to humans. Currently, identification medicinal poisonous is done by humans using experimental methods, not accurate associated with many errors, also use laboratory methods requires experts this method very costly time-consuming. Therefore, distinguishing between important emerging, non-destructive, fast such as computer vision artificial intelligence. In study, we propose robust generalized model spatial attention (SA) channel (CA) modules for classification different plants. A dataset containing 900 confirmed images three plant classes (oregano, weed) was used. The mechanisms enhance efficiency deep learning (DL) networks allowing them precisely focus on all relevant input elements. order performance proposed model, CA implemented based four pooling operations including global average pooling-based (GAP-CA), mixed (Mixed-CA), gated (Gated-CA), tree (Tree-CA) operations. results showed that DL Tree-CA had promising outperformed other state-of-the-art models, achieving values 99.63%, 99.38%, 99.52%, 99.74%, 99.42%, accuracy, precision, recall, specificity, F1-score, respectively. findings support our model's success in identifying from similar Recent advancements computer-based technologies intelligence enable automatic detection plants, revolutionizing traditional methods.

Язык: Английский

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A forestry investigation: Exploring factors behind improved tree species classification using bark images

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102932 - 102932

Опубликована: Дек. 1, 2024

Язык: Английский

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Multiscale feature fusion and enhancement in a transformer for the fine-grained visual classification of tree species

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103029 - 103029

Опубликована: Янв. 1, 2025

Язык: Английский

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HerbSimNet: Deep Learning -Based Classification of Indian Medicinal Plants with High Inter-Class Similarities

Procedia Computer Science, Год журнала: 2025, Номер 258, С. 765 - 774

Опубликована: Янв. 1, 2025

Язык: Английский

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PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines

Big Data and Cognitive Computing, Год журнала: 2024, Номер 8(12), С. 176 - 176

Опубликована: Дек. 1, 2024

In computer vision, recognizing plant pictures has emerged as a multidisciplinary area of interest. the last several years, much research been conducted to determine type in each image automatically. The challenges identifying medicinal plants are due changes effects light, stance, and orientation. Further, it is difficult identify factors like variations leaf shape with age changing color response varying weather conditions. proposed work uses machine learning techniques deep neural networks choose appropriate features if or non-medicinal plant. This study presents network design based on PSR-LeafNet (PSR-LN). single that combines P-Net, S-Net, R-Net, all intended for feature extraction using minimum redundancy maximum relevance (MRMR) approach. PSR-LN helps obtain features, venation leaf, textural features. A support vector (SVM) applied output achieved from PSR network, which classify name model named PSR-LN-SVM. advantage designed suits more considerable dataset processing provides better results than traditional models. methodology utilized achieves an accuracy 97.12% MalayaKew dataset, 98.10% IMP 95.88% Flavia dataset. models surpass existing models, having improvement accuracy. These outcomes demonstrate suggested method successful accurately leaves plants, paving way advanced taxonomy medicine.

Язык: Английский

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Almond Quality Assessment using Deep Learning Techniques

Опубликована: Июль 27, 2024

Язык: Английский

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PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation

Ecological Informatics, Год журнала: 2024, Номер 84, С. 102899 - 102899

Опубликована: Ноя. 17, 2024

Язык: Английский

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