CNN algorithm for plant classification in deep learning

Valarmathi, G.; Suganthi, S.U.; Subashini, V.; Janaki, R.; Sivasankari, R.

doi:10.1016/j.matpr.2021.01.847

Cited by 14 publications

(4 citation statements)

References 4 publications

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“…Any top individual node whose output exceeds the predetermined threshold becomes active and transmits information to the following network tier. If not, no data is sent to the following network layer (Valarmathi G., et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence in Mechanical Engineering

ARTKIN>¹

2022

EJOSAT

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Artificial intelligence (AI) technology, as one of the most sophisticated science and technology in today's world, is increasingly being used to production and life, particularly in the manufacturing business. it demonstrates how artificial intelligence technology is used in mechanical manufacturing, namely in defect detection, quality inspection, enhancing workplace safety, and other areas. Artificial intelligence technology is becoming increasingly important in people's lives as it becomes more widely used in people's daily lives, such as the widespread use of smart dishwashers and smart sweepers, which are the products of the fusion of artificial intelligence and the mechanical manufacturing industry. Indeed, artificial intelligence technology has been widely utilized in the mechanical manufacturing business, which not only ensures production precision, but also enhances job productivity and workplace safety. The rise of artificial intelligence has caused significant changes in the manufacturing industry as a whole. Without exception, the manufacturing industry must rely on AI technology to accomplish automation and intelligent development, as well as to improve productivity. Using artificial intelligence to categorize mechanical components, we may propose parts from a based solely on an image or CAD model. To find a necessary component in a machines we must currently browse through a catalogue and be able to discern which part you want based on the available possibilities and your understanding of the catalogue. There are serial numbers to memorize since a single digit or character change might indicate a different sort of part. The algorithm will choose which sections are the best and will significantly facilitate our search.

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Section: Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence in Mechanical Engineering

ARTKIN>¹

2022

EJOSAT

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“…The evaluation of the model was also exhibited and compared with the KNN-based neighborhood classification, Kohonen network based on a self-organizing feature mapping algorithm, and SVM-based support vector machine. A simple KNN-based classifier for extracting the surface of plant was also introduced in [9]. Unlike other works, this work focuses on the surface features of plants to use in a robotized vision framework for harvesting and farming objectives.…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Transfer Learning for Caladium bicolor Classification: Proof of Concept to Application Development

2024

KSII TIIS

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Caladium bicolor is one of the most popular plants in Thailand. The original species of Caladium bicolor was found a hundred years ago. Until now, there are more than 500 species through multiplication. The classification of Caladium bicolor can be done by using its color and shape. This study aims to develop a model to classify Caladium bicolor using a transfer learning technique. This work also presents a proof of concept, GUI design, and web application deployment using the user-design-center method. We also evaluated the performance of the following pre-trained models in this work, and the results are as follow: 87.29% for AlexNet, 90.68% for GoogleNet, 93.59% for XceptionNet, 93.22% for MobileNetV2, 89.83% for RestNet18, 88.98% for RestNet50, 97.46% for RestNet101, and 94.92% for InceptionResNetV2. This work was implemented using MATLAB R2023a.

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“…However, the mobile images were resized for further classification and analysis. (16) For extracting the leaf features, pre-processing mechanisms were applied to change the images from color to greyscale. (17) Classifying the herbal leaves along with image processing (18) and support vector machine techniques (19) does not provide accurate results for precision and recall, so the proposed model was developed to get better precision than the recall rate.…”

Section: Figure 1 Herbal Leaves Classificationmentioning

confidence: 99%

An Optimized Intelligent Deep Network for Herbal Leaf Classification

Hema,

Elango

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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In recent times, a variety of industries have made extensive use of image processing techniques for tasks including segmentation and classification. However, the traditional image processing and ensemble learning approaches face challenges in feature selection and classification. To overcome the demerits of the conventional image processing and boosting algorithm, a novel hybrid Chimp-based Boltzmann Prediction Network (CbBPN) was developed in this article. The presented work was designed and verified in MATLAB software with the herbal leaf dataset. In the model development, the pre-processing and feature extraction module is responsible for extracting valuable features that are pertinent to the classification process. Furthermore, the chimp fitness function increases the classification rate by removing unwanted elements during the classification stage. Additionally, the developed model uses the matching operation to specify the types of the leaf. Furthermore, a case study was created to explain the ways the suggested approach operates. Moreover, a comparison of the projected findings with the existing categorization approaches validates the effectiveness of the constructed model. The comparative analysis shows that the new methods outperformed previously available ones in terms of output.

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CNN algorithm for plant classification in deep learning

Cited by 14 publications

References 4 publications

Applications of Artificial Intelligence in Mechanical Engineering

Applications of Artificial Intelligence in Mechanical Engineering

Transfer Learning for Caladium bicolor Classification: Proof of Concept to Application Development

An Optimized Intelligent Deep Network for Herbal Leaf Classification

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