A Hybrid Model for the Classification of Sunflower Diseases Using Deep Learning

Sirohi, Akash; Malik, Arun

doi:10.1109/iciem51511.2021.9445342

Cited by 26 publications

(13 citation statements)

References 17 publications

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“…The method has no advantage in precision, recall and F1-score compared to the proposed method. Sirohi and Malik [41] proposed a stacked integrated learning technique to recognize sunflower diseases but only achieved 89.20% accuracy. Sathi et al [42] first segmented the disease region based on k-mean clustering algorithm to extract features and then achieved a sub-optimal classification accuracy of 97.88% using multiple deep learning classifiers.…”

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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“…Moreover, we obtain hay from the leaves and a yellow dye from the flowers. However, diseases such as downy mildew, Phom blight, Verticillium wilt, leaf scars, leaf rust, and Septoria leaf spot can negatively affect sunflower production [1,2]. Moreover, most of our farmers, especially in developing countries, are illiterate and technologically unsound, which leads to more damage from such diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Within DL, the convolutional neural network [10], defined as ConvNet or CNN, is a category of DL algorithm that utilizes data with a matrix, such as an image by extracting different features. Feature extraction transforms plain data into numeric values that can be processed for further analysis [2,15]. Moreover, the CNN model is widely used for plant disease detection with reasonable accuracy [12].…”

Section: Introductionmentioning

confidence: 99%

“…In this research, we introduce a hybrid model of TL and CNN. A hybrid algorithm is a technique that converges two or more other techniques together that solve the exact issues [2]. Here, we want to use the TL model to extract features and then apply CNN techniques for classification from sunflower disease on a small dataset [16].…”

Section: Introductionmentioning

confidence: 99%

“…A few studies in the literature [1,2,20] deal with the recognition of sunflower diseases using DL/ML algorithms, and current studies have not explained the outcomes of the models with intelligent tools. Moreover, there is still scope to improve the disease recognition rate to promote sunflower production.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

et al. 2023

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Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional approaches. Thus, a computerized model composed of vision, artificial intelligence, and machine learning is the demand of the age to detect diseases in plants efficiently. In this paper, we develop a hybrid model with transfer learning (TL) and a simple CNN using a small dataset for detecting sunflower diseases. Out of the eight models tested on the dataset of four different classes (downy mildew, gray mold, leaf scars, and fresh leaf), the VGG19 + CNN hybrid model achieves the best results in terms of precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, and Cohen’s kappa metrics. The experimental outcomes show that the proposed model provides better precision, recall, and accuracy than other approaches on the benchmark dataset.

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Hybrid Activation Functions in Deep Convolutional Neural Networks for Maize and Paddy Leaf Disease Recognition: A Transfer Learning Approach

Sunilkumar,

Poornima

2024

Lecture Notes in Electrical Engineering

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A Hybrid Model for the Classification of Sunflower Diseases Using Deep Learning

Cited by 26 publications

References 17 publications

TeenyNet: a novel lightweight attention model for sunflower disease detection

TeenyNet: a novel lightweight attention model for sunflower disease detection

Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Hybrid Activation Functions in Deep Convolutional Neural Networks for Maize and Paddy Leaf Disease Recognition: A Transfer Learning Approach

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