On identifying leaves: A comparison of CNN with classical ML methods

Hedjazi, Mohamed Abbas; Kourbane, Ikram; Genç, Yakup

doi:10.1109/siu.2017.7960257

Cited by 33 publications

(12 citation statements)

References 7 publications

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“…The learning rate is an important parameter; it is the rate at which the gradients of each neuron are updated. A higher learning rate can reach the goal quickly but risks reaching a local minima [ 73 , 74 , 75 , 76 , 77 ]. The goal of the loss function is to reach a global minimum acceptable value for the loss function.…”

Section: Methodsmentioning

confidence: 99%

Classification of the Acoustics of Loose Gravel

Saeed

Nyberg

Alam

et al. 2021

Sensors

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Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

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Section: Methodsmentioning

confidence: 99%

Classification of the Acoustics of Loose Gravel

Saeed

Nyberg

Alam

et al. 2021

Sensors

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“…Animal data are also used to classify or diagnose diseases (Banzato et al, 2018a , b ; Choi et al, 2018 ; Kim et al, 2019 ) and to study animal cognition (Hao et al, 2019 ; Yudin et al, 2019 ; Mohammed and Hussain, 2021 ). In plants, AI-based image analyses can be used to recognize specific tissues (i.e., flowers and fruits), detect diseases (Wozniak and Połap, 2018 ; Maeda-Gutierrez et al, 2020 ), and classify species, cultivars, and lineages (Lee et al, 2015 ; Grinblat et al, 2016 ; Hedjazi et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Jung

Song²,

Hong

et al. 2021

Front. Plant Sci.

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Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end, various algorithms have been developed and applied in fields such as disease diagnosis, species classification, and object prediction. In the field of phenomics, classification of accessions and variants is important for basic science and industrial applications. To construct AI-based classification models, three types of phenotypic image data were generated from 156 Brassica rapa core collections, and classification analyses were carried out using four different convolutional neural network architectures. The results of lateral view data showed higher accuracy compared with top view data. Furthermore, the relatively low accuracy of ResNet50 architecture suggested that definition and estimation of similarity index of phenotypic data were required before the selection of deep learning architectures.

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“…There have been several comparative studies of CNNs and LBP for image classification [55][56][57], with datasets captured by various devices in different conditions. While the CNNs and LBP performances have been extensively investigated for proof-of-concept classification demonstration, the computation time for both deep learning and machine learning methods was little mentioned.…”

Section: Introductionmentioning

confidence: 99%

Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies

Ahderom

Alameh

2020

Sensors

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Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, ResNet-50 and InceptionV3. The experimental results on the “bccr-segset” dataset in the laboratory testbed setting show that the accuracy of CNN models with fine-tuned hyper-parameters is slightly higher than the k-FLBPCM method, while the accuracy of the k-FLBPCM algorithm is higher than the CNN models (except for VGG-16) for the more realistic “fieldtrip_can_weeds” dataset collected from real-world agricultural fields. However, the CNN models require a large amount of labelled samples for the training process. We conducted another experiment based on training with crop images at mature stages and testing at early stages. The k-FLBPCM method outperformed the state-of-the-art CNN models in recognizing small leaf shapes at early growth stages, with error rates an order of magnitude lower than CNN models for canola–radish (crop–weed) discrimination using a subset extracted from the “bccr-segset” dataset, and for the “mixed-plants” dataset. Moreover, the real-time weed–plant discrimination time attained with the k-FLBPCM algorithm is approximately 0.223 ms per image for the laboratory dataset and 0.346 ms per image for the field dataset, and this is an order of magnitude faster than that of CNN models.

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On identifying leaves: A comparison of CNN with classical ML methods

Cited by 33 publications

References 7 publications

Classification of the Acoustics of Loose Gravel

Classification of the Acoustics of Loose Gravel

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies

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