Efficient Deep Learning Models for Categorizing <i>Chenopodiaceae</i> in the Wild

Heidary-Sharifabad, Ahmad; Zarchi, Mohsen Sardari; Emadi, Sima; Zarei, Gholamreza

doi:10.1142/s0218001421520157

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…Experiments have been carried out to assess fine-grained categorization among a collection of visually extremely similar plant species. The constructed classifier had a 90% accuracy rate in distinguishing between 30 distinct Chenopodiaceae species [51,52]. Purwandari et al [26] proposed an ML-based expert system for bamboo identification using the K-nearest neighbors (KNN) algorithm.…”

Section: Background and Related Workmentioning

confidence: 99%

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Sawarkar,

Shrimankar,

Ali

et al. 2024

Applied Sciences

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Bamboos, also known as non-timber forest products (NTFPs) and belonging to the family Poaceae and subfamily Bambusoideae, have a wide range of flowering cycles from 3 to 120 years; hence, it is difficult to identify species. Here, the focus is on supervised machine learning (ML) and deep learning (DL) as a potential automated approach for the identification and classification of commercial bamboo species, with the help of the majority multiclass voting (MajMulVot) algorithm. We created an image dataset of 2000 bamboo instances, followed by a texture dataset prepared using local binary patterns (LBP) and gray-level cooccurrence matrix (GLCM)-based methods. First, we deployed five ML models for the texture datasets, where support vector machine (SVM) shows an accuracy rate of 82.27%. We next deployed five DL-based convolutional neural network (CNN) models for bamboo classification, namely AlexNet, VGG16, ResNet18, VGG19, and GoogleNet, using the transfer learning (TL) approach, where VGG16 prevails, with an accuracy rate of 88.75%. Further, a MajMulVot-based ensemble approach was introduced to improve the classification accuracy of all ML- and DL-based models. The ML-MajMulVot enhanced the accuracy for the texture dataset to 86.96%. In the same way, DL-MajMulVot increased the accuracy to 92.8%. We performed a comparative analysis of all classification models with and without K-fold cross-validation and MajMulVot methods. The proposed findings indicate that even difficult-to-identify species may be identified accurately with adequate image datasets. The suggested technology can also be incorporated into a mobile app to offer farmers effective agricultural methods.

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Section: Background and Related Workmentioning

confidence: 99%

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Sawarkar,

Shrimankar,

Ali

et al. 2024

Applied Sciences

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“…2 and 3 . The details and hyper-parameters of both proposed models are fully described in the related research paper [1] . The experimental results show that both proposed models can perform Chenopodiaceae species recognition with promising accuracy on ACHENY dataset.…”

Section: Data Descriptionmentioning

confidence: 99%

ACHENY: A standard Chenopodiaceae image dataset for deep learning models

et al. 2021

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This paper contains datasets related to the “Efficient Deep Learning Models for Categorizing Chenopodiaceae in the wild” (Heidary-Sharifabad et al., 2021). There are about 1500 species of Chenopodiaceae that are spread worldwide and often are ecologically important. Biodiversity conservation of these species is critical due to the destructive effects of human activities on them. For this purpose, identification and surveillance of Chenopodiaceae species in their natural habitat are necessary and can be facilitated by deep learning. The feasibility of applying deep learning algorithms to identify Chenopodiaceae species depends on access to the appropriate relevant dataset. Therefore, ACHENY dataset was collected from natural habitats of different bushes of Chenopodiaceae species, in real-world conditions from desert and semi-desert areas of the Yazd province of IRAN. This imbalanced dataset is compiled of 27,030 RGB color images from 30 Chenopodiaceae species, each species 300-1461 images. Imaging is performed from multiple bushes for each species, with different camera-to-target distances, viewpoints, angles, and natural sunlight in November and December. The collected images are not pre-processed, only are resized to 224 × 224 dimensions which can be used on some of the successful deep learning models and then were grouped into their respective class. The images in each class are separated by 10% for testing, 18% for validation, and 72% for training. Test images are often manually selected from plant bushes different from the training set. Then training and validation images are randomly separated from the remaining images in each category. The small-sized images with 64 × 64 dimensions also are included in ACHENY which can be used on some other deep models.

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“…Poaceae species are ubiquitous, often dominate entire landscapes (Veen et al, 2009) and their occurrence and distribution provide invaluable information on the condition and development of the habitat (e.g., Diekmann et al, 2019). Experiments to evaluate fine-grained classification within a group of visually very similar plant species have been performed e.g., for Chenopodiaceae, which represent another plant family with mainly wind-or self pollinated and inconspicuous flowers (Heidary-Sharifabad et al, 2021). The developed classifier is able to differentiate between 30 species of Chenopodiaceae with an accuracy of about 90%.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Automated Recognition of 31 Poaceae Species: The Most Relevant Perspectives

et al. 2022

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Poaceae represent one of the largest plant families in the world. Many species are of great economic importance as food and forage plants while others represent important weeds in agriculture. Although a large number of studies currently address the question of how plants can be best recognized on images, there is a lack of studies evaluating specific approaches for uniform species groups considered difficult to identify because they lack obvious visual characteristics. Poaceae represent an example of such a species group, especially when they are non-flowering. Here we present the results from an experiment to automatically identify Poaceae species based on images depicting six well-defined perspectives. One perspective shows the inflorescence while the others show vegetative parts of the plant such as the collar region with the ligule, adaxial and abaxial side of the leaf and culm nodes. For each species we collected 80 observations, each representing a series of six images taken with a smartphone camera. We extract feature representations from the images using five different convolutional neural networks (CNN) trained on objects from different domains and classify them using four state-of-the art classification algorithms. We combine these perspectives via score level fusion. In order to evaluate the potential of identifying non-flowering Poaceae we separately compared perspective combinations either comprising inflorescences or not. We find that for a fusion of all six perspectives, using the best combination of feature extraction CNN and classifier, an accuracy of 96.1% can be achieved. Without the inflorescence, the overall accuracy is still as high as 90.3%. In all but one case the perspective conveying the most information about the species (excluding inflorescence) is the ligule in frontal view. Our results show that even species considered very difficult to identify can achieve high accuracies in automatic identification as long as images depicting suitable perspectives are available. We suggest that our approach could be transferred to other difficult-to-distinguish species groups in order to identify the most relevant perspectives.

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Efficient Deep Learning Models for Categorizing Chenopodiaceae in the Wild

Cited by 7 publications

References 52 publications

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

ACHENY: A standard Chenopodiaceae image dataset for deep learning models

Image-Based Automated Recognition of 31 Poaceae Species: The Most Relevant Perspectives

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