A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification

Nuanmeesri, Sumitra

doi:10.48084/etasr.4455

Cited by 27 publications

(12 citation statements)

References 11 publications

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“…This is considered a major limitation, especially for real-time disease detection, where multiple diseases would be present. Nuanmeesri [ 46 ] advanced the image processing technique from traditional image segmentation to deep learning-based detection in order to identify up to 15 different diseases. A hybrid deep learning model built by fusing convolutional neural networks (CNNs) and a support vector machine (SVM) were used.…”

Section: Sensing and Automation Technologies For Ornamental Cropsmentioning

confidence: 99%

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

Mahmud

Zahid

Das

2023

Sensors

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The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production.

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Section: Sensing and Automation Technologies For Ornamental Cropsmentioning

confidence: 99%

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

Mahmud

Zahid

Das

2023

Sensors

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“…To build the feature map, output of the convolutional layer is converted by an function similar to ANNs'. After each convolutional layer, additional subsampling operations such as max-pooling and softmax are performed to enhance the performance [24][25]. As with ANNs, hyper-parameters such as learning rate, batch size, and the number of epochs should be investigated for the CNN to improve its classification performance.…”

Section: ) Input Preparationmentioning

confidence: 99%

Improvement of Classification Accuracy of Four-Class Voluntary-Imagery fNIRS Signals using Convolutional Neural Networks

Milu¹,

Rahman²,

Rashid

et al. 2023

Eng. Technol. Appl. Sci. Res.

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Multiclass functional Near-Infrared Spectroscopy (fNIRS) signal classification has become a convenient way for optical brain-computer interface. fNIRS signal classification with high accuracy is a challenging assignment while the signals are produced by means of voluntary and imagery movements of the same limb. Since the activation in time and space of voluntary and imagery movement show a similar pattern, the classification accuracy by the conventional shallow classifiers cannot reach an acceptable range. This paper proposes an accuracy improvement approach with the use of Convolutional Neural Networks (CNNs). In this work, voluntary and imagery hand movements (left hand and right hand) were performed by several participants. These four-class signals were acquired utilizing fNIRS devices. The signals were separated based on the tasks and filtered. With manual feature extraction, the signals were classified by support vector machine and linear discriminant analysis. The automatic feature extraction and classification mechanism of the CNN were applied to the fNIRS signals. From the results, it was found that CNN improves the classification accuracy to an acceptable range, which has not been achieved by any convolutional network.

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“…Rather a short dataset containing 215 images is used for classification where high accuracy is achieved when flowers of dissimilar shapes are classified. The authors use pre-trained VGG16 architecture to rose flower disease classification in [ 21 ]. Early and late fusion techniques are applied combining VGG16 and SVM where the early fusion models show better results with 88.33% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Performance Comparison of Classification Algorithms for Rose Plants

Malik

Aslam

Nasr

et al. 2022

Computational Intelligence and Neuroscience

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One of the key roles of Botanists is to be able to recognize flowers. This role has become highly challenging given that the number of discovered flower types are nearing half a million. To support Botanists, Information Technology offers promising solutions. Specifically, machine learning techniques are intrinsically appealing due to being precise enough as required. To this aim, two observations on flower leaves are relevant and leverage flower identification: one, flower plants exhibit unique features in their leaves thus allow distinction of their co-located flowers; two, leaves have a much longer life than flowers thus preserve identity properties longer. This paper proposes the use of machine learning-based identification of rose types by leveraging the features from their leaves. For this purpose, the performance of Naive Bayes, Generalized Linear Model, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine has been analyzed. This study optimizes the RF model by investigating and tuning its various parameters such as the number of trees, the depth of trees, and splitting criteria. The best results are achieved with gain ratio because it takes more distinct values to avoid the problems associated with Information Gain. Optimizing the number of trees and the depth of trees of RF yield better accuracy than other models. Extensive experiments are performed to analyze the results of ensemble algorithms by using the voting method for each instance. Results suggest that the performance of ensemble classifiers is superior to that of individual models.

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A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification

Cited by 27 publications

References 11 publications

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

Improvement of Classification Accuracy of Four-Class Voluntary-Imagery fNIRS Signals using Convolutional Neural Networks

A Performance Comparison of Classification Algorithms for Rose Plants

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