Houseplant leaf classification system based on deep learning algorithms

Botanical experts are typically relied upon to classify houseplants since even subtle differences in characteristics such as leaves can distinguish one species from another. Therefore, an automated system for recognizing houseplant leaves with accuracy and reliability becomes a valuable asset for th...

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Bibliographic Details
Published in:	Journal of Electrical Systems and Information Technology Vol. 11; no. 1; pp. 18 - 15
Main Authors:	Hama, Hersh M., Abdulsamad, Taib Sh, Omer, Saman M.
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2024 Springer Nature B.V SpringerOpen
Subjects:	Accuracy Agriculture Algorithms Artificial Intelligence Artificial neural networks Automation Classification CNN Data augmentation Datasets Deep learning Electrical Engineering Electrical Machines and Networks Engineering Environmental monitoring Flowers & plants Identification systems Image classification Improved ResNet-50 Information Systems and Communication Service Leaves Machine learning Mechanical Engineering Methods Model accuracy Neural networks Plants (botany) Power Electronics ResNet-50 architecture modification Support vector machines Wavelet transforms Deep learning CNN ResNet-50 architecture modification Improved ResNet-50
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Summary:	Botanical experts are typically relied upon to classify houseplants since even subtle differences in characteristics such as leaves can distinguish one species from another. Therefore, an automated system for recognizing houseplant leaves with accuracy and reliability becomes a valuable asset for the identification of indoor plant species. In this paper, a houseplant leaf classification system utilizing deep learning algorithms is proposed, which has been improved to effectively classify and identify a variety of houseplant leaf types. The system uses the ResNet-50 architecture based on convolutional neural network to analyze features of the leaf images and extract relevant information for classification. In addition, this work presents a newly constructed local dataset consisting of 2500 images to classify species of houseplant leaves. The dataset includes ten types of houseplant leaves that are suitable for cultivation in various climates at home. The dataset was augmented using data augmentation algorithms to increase its size and reduce overfitting. The developed system was training and testing using a local dataset. To evaluate the improved model, comparative experiments were conducted utilizing pre-trained models (original ResNet-50 and MobileNet_v2). The improved model revealed recognition accuracy of 99% with the augmented dataset and 98.60% without the augmentation, affirming its effectiveness. The improved model could potentially be used in various fields, including horticulture, plant pathology, and environmental monitoring to identify plant species.
ISSN:	2314-7172 2314-7172
DOI:	10.1186/s43067-024-00141-5