Recognition of the part of growth of flue-cured tobacco leaves based on support vector machine

Li-qun, Han

doi:10.1109/wcica.2008.4593502

Cited by 3 publications

(2 citation statements)

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“…To overcome the limitations of manual grading, in recent years several methods have been employed to achieve automatic tobacco leaf grading. Previous works [6]- [9] have focused on extracting handcrafted features and designing specific classifiers to perform tobacco leaf grading. With the rapid development of deep learning, some works have employed neural networks to improve agricultural production efficiency, such as fruit counting [10], leaf disease detection [11], plant recognition [12], etc.…”

Section: Introductionmentioning

confidence: 99%

CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading

He,

Zhang,

et al. 2023

IEEE Access

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Tobacco leaf grading plays a crucial role in ensuring the quality of tobacco production. For a very long period, the grading process is manually determined by experienced experts. In recent years, some methods have been introduced to automate the grading process by utilizing the reflection images of tobacco leaves. However, the high visual similarity among reflection images at different grades renders a single reflection image insufficient for achieving accurate grading. Besides, the tobacco leaves with an identical grade may have inconsistent visual appearances due to their different planting locations. It is known that an expert integrates multimodal information such as visual, tactile, and planting location cues when performing grading. Inspired by this, we propose an end-to-end Cross-modal Enhancement Network, named CMENet, for automatic tobacco leaf grading. In addition to the common reflection image, the network also adopts a transmission image to incorporate the thickness information in manual grading. CMENet comprises a difference-aware fusion module and a meta self-attention module, enabling the extraction of multimodal information from the transmission image and the planting location, respectively. Experimental results demonstrate that our CMENet achieves a high grading accuracy (80.15%) when incorporating multimodal information, surpassing the performance of existing methods that rely solely on reflection images.INDEX TERMS Tobacco leaf grading, image classification, convolutional neural network, cross-modal information fusion.

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Section: Introductionmentioning

confidence: 99%

CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading

He,

Zhang,

et al. 2023

IEEE Access

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“…Fuzzy methods have been used for botanical purposes [2,3], such as diagnosing diseases of cotton [4] and classifing tobacco leaves [5]. In addition, other methods are presented to recognize plant leaves based on SVM [6], neural network [7], moving center hypersphere [8] or shape-based image retrieval [9,10].…”

Section: Introductionmentioning

confidence: 99%

WAPSI: Web Application for Plant Species Identification Using Fuzzy Image Retrieval

Caballero

Aranda

2012

Communications in Computer and Information Science

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Abstract. In this paper, a Web Application for Plant Species Identification (WAPSI) that incorporates content-based image retrieval (CBIR) is described. At the heart of this application is a shape-based leaf image retrieval system which uses a contour descriptor based on the curvature of the leaf contour which reduces the number of points for the shape representation. Futhermore, a two-step algorithm for retrieval is used which in the first step reduces the database using some geometrical features. Secondly, leaf images are ranked using a similarity measure between the contour representations. The similarity function between images uses a variant triangular membership function to calculate the distance between the characteristic points' vectors. This membership function is the key of the good results obtained. The effectiveness for plant species identification of the method is shown through several experiments using our Web application.

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