Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study

Gulzar, Yonis; Ünal, Zeynep; Aktaş, Hakan; Mir, Mohammad Shuaib

doi:10.3390/agriculture13081479

Cited by 35 publications

(28 citation statements)

References 41 publications

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“…Ghosh et al [39] developed a hybrid model with TL and simple CNN to recognize sunflower diseases and obtained 93.00% accuracy. Gulzar et al [40] used several classical deep learning models trained in multiple iterations on a sunflower disease dataset and achieved a high accuracy of 97.60%. The method has no advantage in precision, recall and F1-score compared to the proposed method.…”

Section: Comparison With Existing Deep Learning Methodsmentioning

confidence: 99%

TeenyNet: a novel lightweight attention model for sunflower disease detection

Zhong,

Tong

2023

Meas. Sci. Technol.

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Sunflower is one of the oilseed crops which is popularly and widely cultivated globally and contributes significantly to human health. Leaf diseases of sunflower seriously affect the growth and yield of sunflower, which directly leads to the loss of agricultural economy. However, existing machine learning algorithms and deep learning techniques are mainly based on large models with attention mechanisms, which lack considerations in computational performance, especially model size. Therefore, this study proposes a lightweight model called TeenyNet to break through the dilemma. First, the designed global multi-frequency feature extraction module decomposes the image to extract multi-frequency multi-scale features. Then, a parameter-free maximum pooling layer further extracts edge and texture features and simplifies the network complexity through downsampling, after which the proposed lightweight dual fusion attention and multi-branching structure fuses all the feature vectors to enhance multidimensional feature learning and accelerate the model convergence. Finally, the fully connected linear layer solves the multi-classification problem of sunflower disease under natural illumination background conditions. The experimental results show that TeenyNet obtains the highest accuracy of 98.94% for sunflower disease recognition with a minimum size of 143 KB and has better recognition performance in comparison experiments. TeenyNet can be effectively used for the detection of sunflower leaf diseases to achieve disease prevention and control.

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Section: Comparison With Existing Deep Learning Methodsmentioning

confidence: 99%

TeenyNet: a novel lightweight attention model for sunflower disease detection

Zhong,

Tong

2023

Meas. Sci. Technol.

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“…Remarkably, their approach achieved remarkable results, attaining an accuracy of 98.74% along with an impressive F1score of 98.18%. Gulzar et al [16] employed five deep-learning models, including AlexNet, VGG16, InceptionV3, MobileNetV3, and EfficientNet, for the classification of sunflower disease detection. Their findings revealed that the EfficientNetB3 model achieved the highest accuracy, reaching 97.9%.…”

Section: Related Studymentioning

confidence: 99%

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Aggarwal,

Khullar,

Goyal

et al. 2023

Agronomy

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Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework’s performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools.

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“…DNNs are a particular type of ML algorithm that are intended to replicate the brain’s framework and the function of the human brain. To learn and extract more sophisticated characteristics from the input data, they are built of progressively more complicated layers of interconnected nodes [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor

Khan,

Ayoub,

Gulzar

et al. 2023

J. Imaging

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The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods.

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Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study

Cited by 35 publications

References 41 publications

TeenyNet: a novel lightweight attention model for sunflower disease detection

TeenyNet: a novel lightweight attention model for sunflower disease detection

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor

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