Model Development for Identifying Aromatic Herbs Using Object Detection Algorithm

The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—r...

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Bibliographic Details
Published in:AgriEngineering 2024-09, Vol.6 (3), p.1924-1936
Main Authors: Antunes, Samira Nascimento, Okano, Marcelo Tsuguio, Nääs, Irenilza de Alencar, Lopes, William Aparecido Celestino, Aguiar, Fernanda Pereira Leite, Vendrametto, Oduvaldo, Fernandes, João Carlos Lopes, Fernandes, Marcelo Eloy
Format: Article
Language:English
Subjects:
Accuracy
Agriculture
Algorithms
aromatic herb
Artificial intelligence
Automation
Computer vision
convolutional neural network
Data mining
Deep learning
Digital technology
Efficiency
Evolutionary algorithms
Herbs
Identification
Machine learning
Neural networks
Object recognition
Performance evaluation
Plant layout
Recall
Training
YOLO v8
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Summary:The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—rosemary, mint, and bay leaf—using advanced computer-aided detection within the You Only Look Once (YOLO) framework. Employing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, the study meticulously covers data understanding, preparation, modeling, evaluation, and deployment phases. The dataset, consisting of images from diverse devices and annotated with bounding boxes, was instrumental in the training process. The model’s performance was evaluated using the mean average precision at a 50% intersection over union (mAP50), a metric that combines precision and recall. The results demonstrated that the model achieved a precision of 0.7 or higher for each herb, though recall values indicated potential over-detection, suggesting the need for database expansion and methodological enhancements. This research underscores the innovative potential of deep learning in aromatic plant identification and addresses both the challenges and advantages of this technique. The findings significantly advance the integration of artificial intelligence in agriculture, promoting greater efficiency and accuracy in plant identification.
ISSN:2624-7402
2624-7402
DOI:10.3390/agriengineering6030112