WITHDRAWN: A comparative analysis on plant pathology classification using deep learning architecture – Resnet and VGG19

Subetha, T.; Khilar, Rashmita; Christo, Mary Subaja

doi:10.1016/j.matpr.2020.11.993

Cited by 20 publications

(10 citation statements)

References 12 publications

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“…The plant sciences have also steadily incorporated DL methods, with experimentation using DL algorithms providing superior performance relative to conventional ML algorithms in classifying plants and in detecting various plant diseases [26] , [27] , [28] . Convolutional Neural Networks (CNNs) have successfully classified plants [29] , [30] , [31] , [32] , [33] and identified diseased plants [34] , [35] , [36] . For example, a CNN-based approach DenseNet-77 gave better accuracy than SVM and K-Nearest Neighbors (KNN) in detecting diseased plants [35] .…”

Section: Introductionmentioning

confidence: 99%

“…For example, a CNN-based approach DenseNet-77 gave better accuracy than SVM and K-Nearest Neighbors (KNN) in detecting diseased plants [35] . CNN techniques have also been proven capable of differentiating plants according to species [27] , [31] , [32] , [33] , [37] . Recurrent Neural Networks (RNN) have also been successful in analyzing spatiotemporal data when paired with CNNs [38] , [39] , [40] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data

Sakeef,

Scandola,

Kennedy

et al. 2023

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data

Sakeef,

Scandola,

Kennedy

et al. 2023

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

“…11 In the field of image recognition, convolution neural networks have been proposed. A large number of scholars have worked in this field and built excellent neural networks, such as GoogLeNet, 12 ResNet, 13 and EfficientNet. 13 In particular, the ResNet-18 network structure, because of its simplicity and good performance, has been widely used and can achieve excellent results in weed classification and recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of scholars have worked in this field and built excellent neural networks, such as GoogLeNet, 12 ResNet, 13 and EfficientNet. 13 In particular, the ResNet-18 network structure, because of its simplicity and good performance, has been widely used and can achieve excellent results in weed classification and recognition tasks. In the field of target detection, we need to use a large number of weed data to train the model.…”

Section: Introductionmentioning

confidence: 99%

High-information data-centric weed images data identification system based on triple filter for smart agriculture

Zhang

2023

J. Electron. Imag.

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Weeds have seriously impacted crop planting and caused serious human losses.The intelligent weed identification algorithm based on the convolutional neural network has made some achievements in intelligent agriculture. However, the current results rely on a large number of labeled weed data, which is costly to obtain. How to effectively identify without big data is a key task in the current field. We designed a high-information data-centric weed images data identification system based on a triple filter. The system consists of three modules: nearest-neighbor core metric, unobserved components model, and outlier detection. Extensive scientific experiments have shown that our system requires only a small amount of data to achieve excellent performance in weed identification tasks. Our system saves 5% to 20% of the data. When using only 80% of the data, our system achieves the model performance obtained with 100% of data training. Compared with other methods, this method improves the accuracy by 4.9% and reaches a state-of-the-art performance. Our work solves the problem of relying on image data in smart agriculture, provides a scheme for weed identification tasks, and provides valuable ideas for future intelligent agricultural research.

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“…As discussed by Guo et al (2016), there are many variations of the CNN methods, where the difference is basically in terms of the total number of convolutional layers. The CNN classifiers have demonstrated exceptional accuracy and precision in many agriculture applications (Khanramaki et al, 2021;Alhnaity et al, 2020;Ayan et al, 2020;Chouhan et al, 2019;Ferentinos, 2018;Habiba et al, 2019), including in the field of plant phenotyping (Fuentes et al, 2019;Wang et al, 2019;Subetha et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Efficacy of Deep Learning Algorithm in Classifying Chilli Plant Growth Stages

Rozilan

Hanafi²,

Ali³

et al. 2021

Adv Agric Food Res J

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Automatic plant growth monitoring has received considerable attention in recent years. The demand in this field has created various opportunities, especially for automatic classification using deep learning methods. In this paper, the efficiency of deep learning algorithms in classifying the growth stage of chili plants is studied. Chili is one of the high cash value crops, and automatic identification of chili plant growth stages is essential for crop productivity. Nevertheless, the study on automatic chili plant growth stage classification using deep learning approaches is not widely explored, and this is due to the unavailability of public datasets on the chili plant growth stages. Various deep learning methods, namely Inception V3, ResNet50, and VGG16, were used in the study, and the results have shown that these methods performed well in terms of accuracy and stability when tested on a dataset that consists of 2,320 images of Capsicum annum 'Bird's Eye' plants of various growth stages and imaging conditions. Nevertheless, the results have also shown that the deep learning methods have difficulty classifying images with a complex background where more than one chili plant was captured in an image.

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WITHDRAWN: A comparative analysis on plant pathology classification using deep learning architecture – Resnet and VGG19

Cited by 20 publications

References 12 publications

Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data

Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data

High-information data-centric weed images data identification system based on triple filter for smart agriculture

Efficacy of Deep Learning Algorithm in Classifying Chilli Plant Growth Stages

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