Plant species recognition methods using leaf image: Overview

Zhang, Shanwen; Huang, Wenzhun; Huang, Yuan; Zhang, Chuanlei

doi:10.1016/j.neucom.2019.09.113

Cited by 38 publications

(21 citation statements)

References 128 publications

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“…Plant species can be recognized and classified via analysis of various organs, including leaves, stems, fruits, flowers, roots, and seeds [ 61 , 62 ]. Using leaf-based plant recognition seems to be the most common approach by examining specific leaf’s characteristics like color, shape, and texture [ 63 ]. With the broader use of satellites and aerial vehicles as means of sensing crop properties, crop classification through remote sensing has become particularly popular.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

428

146

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

428

146

View full text Add to dashboard Cite

show abstract

“…For the first one, features are chosen manually and extracted through instructed algorithms, then only a subset of the most discriminant features are considered the obtained handcrafted features are used to train classifiers (SVM, NN, NB, etc. [15]). For the second one (i.e., deep learning), it can learn discriminative characteristics from the raw images and recognize them automatically.…”

Section: Related Workmentioning

confidence: 99%

Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

et al. 2021

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In this paper, we propose a novel method for plant leaves recognition by incorporating an unsupervised convolutional auto-encoder (CAE) and Siamese neural network in a unified framework by considering Siamese as an alternative to the conventional loss of CAE. Rather than the conventional exploitation of CAE and Siamese, in our case we have proposed to extend CAE for a novel supervised scenario by considering it as one-class learning classifier. For each class, CAE is trained to reconstruct its positive and negative examples and Siamese is trained to distinguish the similarity and the dissimilarity of the obtained examples. On the contrary and asymmetric to the related hierarchical classification schemes which require pre-knowledge on the dataset being recognized, we propose a hierarchical classification scheme that doesn’t require such a pre-knowledge and can be employed by non-experts automatically. We cluster the dataset to assemble similar classes together. A test image is first assigned to the nearest cluster, then matched to one class from the classes that fall under the determined cluster using our novel one-class learning classifier. The proposed method has been evaluated on the ImageCLEF2012 dataset. Experimental results have proved the superiority of our method compared to several state-of-the art methods.

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“…e feature extraction of plant leaf image is a crucial step of a plant recognition method. ere are many plant species recognition algorithms [1][2][3][4][5], which can be divided into two main types of feature representation methods for describing leaf images, i.e., hand-crafted features [6,7] and deep learning features [8,9]. In fact, the hand-crafted features are mainly dependent on the ability of computer vision experts to encode the morphological characters of the leaves [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Convolutional Neural Networks with Attention for Plant Species Recognition

Wang

Zhang

2021

Computational Intelligence and Neuroscience

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Plant species recognition is a critical step in protecting plant diversity. Leaf-based plant species recognition research is important and challenging due to the large within-class difference and between-class similarity of leaves and the rich inconsistent leaves with different sizes, colors, shapes, textures, and venations. Most existing plant leaf recognition methods typically normalize all leaf images to the same size and then recognize them at one scale, which results in unsatisfactory performances. A novel multiscale convolutional neural network with attention (AMSCNN) model is constructed for plant species recognition. In AMSCNN, multiscale convolution is used to learn the low-frequency and high-frequency features of the input images, and an attention mechanism is utilized to capture rich contextual relationships for better feature extraction and improving network training. Extensive experiments on the plant leaf dataset demonstrate the remarkable performance of AMSCNN compared with the hand-crafted feature-based methods and deep-neural network-based methods. The maximum accuracy attained along with AMSCNN is 95.28%.

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Plant species recognition methods using leaf image: Overview

Cited by 38 publications

References 128 publications

Machine Learning in Agriculture: A Comprehensive Updated Review

Machine Learning in Agriculture: A Comprehensive Updated Review

Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

Multiscale Convolutional Neural Networks with Attention for Plant Species Recognition

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