2023年6月1日 · The pipeline of our approach by integrating shape and convolutional features for plant leaf identification: (a) a leaf image; (b) image preprocessing and normalization; (c) feature extraction; (d) feature representation (the dotted orange box is the IMTD feature and the purple dashed box is the deep convolutional features); (e) feature fusion ...

2023年4月15日 · In this study, we propose a multi-feature fusion approach based on the Local Binary Pattern (LBP) feature for plant leaf recognition by using the partition block strategy. By comparing the original LBP and Multiscale Block Local Binary Pattern (MB-LBP), we present an improved LBP feature descriptor.

2021年4月1日 · We present a plant leaf recognition scheme that effectively combines the MTD shape and LBP-HF texture features. The experimental results show that the proposed method outperforms the state-of-the-art plant species recognition approaches. Plant leaf identification is a significant challenge in the fields of computer vision and pattern recognition.

Deciduous Tree Characteristics • Broad flat leaves • Lose all leaves each year in the fall • Angiosperm (flowering plants), broadleaf, hardwood • Examples: oak, maple, beech, aspen, ash

In this paper, we learn useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gain intuition of the chosen features based on a Network (DN) approach.

2021年6月20日 · Here, we focused on several leaf morphological traits, including leaf size, leaf shape, leaf margin (with or without teeth) and leaf type (i.e. single vs. compound leaf), and reviewed...

2025年4月2日 · Plant leaf diseases significantly threaten agricultural productivity and global food security, emphasizing the importance of early and accurate detection and effective crop health management. Current deep learning models, often used for plant disease classification, have limitations in capturing intricate features such as texture, shape, and color of plant leaves. Furthermore, many of these ...

In this paper, we present a novel set of features based on Pearson correlation coefficients, and we show the applicability of the proposed features for the classification of plant leaves. The foremost contribution in this paper is the use of the Pearson correlation coefficient computed from the leaf boundary pixels for analyzing shape similarity.

2019年6月19日 · Botanists and foresters empirically determine plant categories mainly via visual features of leaves, e.g. leaf shape, leaf margin, leaf arrangement and leaf venation. The leaf shape and leaf margin can be captured easily with cheap devices. As a result, automatic plant recognition is generally based on leaf shape or margin features.

2019年3月29日 · Since the leaves contain useful features for recognising various types of plants, so these features can be extracted and applied by automatic image recognition algorithms to classify plant species. In this study, the authors investigate a novel approach for recognition of plant species using GIST texture features.