Deep Learning for Plant Species Classification Survey

Sobha, P. G. Mary; Thomas, Princy Ann

doi:10.1109/icac347590.2019.9036796

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…The former has been used to define the convex or concave quality of a shape, while the latter has been used to express the degree of bending and shape relative to the spatial aspects of the contour of the shape. Translation, rotation, and scaling transformations were used to perfectly capture the overall properties of leaf shape without loss of data [42].…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

Barhate,

Pathak,

Kumar Dubey

2023

AEJ

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Plants play a crucial role in supporting all forms of life on Earth, not just humans but every living organism. Understanding the diverse range of plant species that surround us is essential due to their significance in various aspects of human life, including agriculture, the environment, medicine, cosmetics, and more. Advancements in machine learning and computer vision algorithms have opened possibilities for identifying different types of plant species, both within and across classes. Plant species detection typically involves several steps, such as image acquisition, feature extraction, categorization, and pre-processing. In this study, three datasets—namely Flavia, Swedish, and the intelligent computing laboratory (ICL) dataset—were chosen for experimentation purposes. For feature extraction, three different models were employed: k-nearest neighbour (KNN), naive Bayes (NB), and the visual geometry group (VGG)-16 model. These models were used to extract distinctive features such as shape, texture, venation, and margin from the plant images. A multiclass classification task was conducted to categorize the plant species. Among the models tested, the VGG-16 model consistently demonstrated superior performance in terms of accuracy. Specifically, when using the VGG-16 model, the obtained accuracies were 96.68% for the Flavia dataset, 97.65% for the Swedish dataset, and 96.11% for the ICL dataset.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

Barhate,

Pathak,

Kumar Dubey

2023

AEJ

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“…Similarly, research presented by Gawli and Gaikwad [20] employed a deep learning approach incorporating CNN for the automatic classification of 17 distinct plant species, predicated on the texture and color characteristics of their leaves, which culminated in an impressive accuracy rate exceeding 94.26%. The suitability and superior performance of deep learning-particularly CNNs-in the automatic plant species recognition and classification highlights its distinct advantage over conventional, hand-crafted methods [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Reshaping Leaf-Level Reflectance Data for Plant Species Discrimination: Exploring Image Shape’s Impact on Deep Learning Results

Yuan,

Song,

Gong

et al. 2023

Remote Sensing

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The application of hyperspectral imagery coupled with deep learning shows vast promise in plant species discrimination. Reshaping one-dimensional (1D) leaf-level reflectance data (LLRD) into two-dimensional (2D) grayscale images as convolutional neural network (CNN) model input demonstrated marked effectiveness in plant species distinction. However, the impact of the image shape on CNN model performance remained unexplored. This study addressed this by reshaping data into fifteen distinct rectangular formats and creating nine CNN models to examine the effect of image structure. Results demonstrated that irrespective of CNN model structure, elongated narrow images yielded superior species identification results. The ‘l’-shaped images at 225 × 9 pixels outperformed other configurations based on 93.95% accuracy, 94.55% precision, and 0.94 F1 score. Furthermore, ‘l’-shaped hyperspectral images consistently produced high classification precision across species. The results suggest this image shape boosts robust predictive performance, paving the way for enhancing leaf trait estimation and proposing a practical solution for pixel-level categorization within hyperspectral imagery (HSIs).

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Classification of Plant Species with Compound and Simple Leaves Using CNN Fusion Networks

Sobha¹,

Thomas²

2021

Communications in Computer and Information Science

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Deep Learning for Plant Species Classification Survey

Cited by 11 publications

References 13 publications

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

A Deep Learning Methodology for Plant Species Recognition Using Morphology of Leaves

Reshaping Leaf-Level Reflectance Data for Plant Species Discrimination: Exploring Image Shape’s Impact on Deep Learning Results

Classification of Plant Species with Compound and Simple Leaves Using CNN Fusion Networks

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