Sunflower Diseases Recognition Using Computer Vision-Based Approach

Rajbongshi, Aditya; Biswas, Al Amin; Biswas, Jahanur; Shakil, Rashiduzzaman; Akter, Bonna; Barman, Mala Rani

doi:10.1109/r10-htc53172.2021.9641588

Cited by 19 publications

(11 citation statements)

References 16 publications

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“…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. Rajbongshi et al [43] conducted a study on sunflower disease recognition based on image segmentation algorithm with random forest algorithm and achieved 90.68% recognition accuracy. However, the necessity of manually designing and selecting the disease features in advance is a non-negligible disadvantage of the method, which happens to be the challenge already addressed by 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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“…Table 3 shows the segmentation procedure of guava images. After segmentation, two features, Gray Level Co-occurrence Matrix (GLCM) and Statistical features are extracted [5] . All of the GLCM and Statistical features are not effective to classify guava diseases.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

A comprehensive guava leaves and fruits dataset for guava disease recognition

et al. 2022

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“…The work presented in [2] is the primary research of [1], where they used MobileNet, AlexNet, InceptionV3, and DenseNet121. In [20], the authors proposed a method for classifying four types of sunflower diseases. In [1,2], the authors applied stacking ensemble learning with the VGG-16 and MobileNet architectures on a dataset of size 329.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, ref. [20] used a larger dataset of size 650, which they preprocessed by resizing, converting color, and enhancing contrast. They found that random forest provided the best results, although they did not provide an explanation of the significant features used by the model.…”

Section: Related Workmentioning

confidence: 99%

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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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Sunflower Diseases Recognition Using Computer Vision-Based Approach

Cited by 19 publications

References 16 publications

TeenyNet: a novel lightweight attention model for sunflower disease detection

TeenyNet: a novel lightweight attention model for sunflower disease detection

A comprehensive guava leaves and fruits dataset for guava disease recognition

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

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