Maize leaf disease classification using CBAM and lightweight Autoencoder network

Cui, Shaodong; Su, Yila; Duan, Kaibo; Liu, Yingxi

doi:10.1007/s12652-022-04438-z

Cited by 18 publications

(5 citation statements)

References 27 publications

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“…The generalization capability was poor for this method and increased the error while training with larger datasets Cui et al [28] CBAM Reduce the dimensionality features in an efficient way CBAM can consume significant memory, especially when dealing with high-resolution images or large batch sizes, which may limit its applicability in resource-constrained environments Yu et al [29] KMC Enhanced the classification performance…”

Section: Vgg-16 Feasible Model and Enhanced Robustnessmentioning

confidence: 99%

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CSIU-Net+: Pepper and corn leaves classification and severity identification using hybrid optimization

Begum,

Syed

2024

Environ. Res. Commun.

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An agricultural product plays a major role in the economical growth of developing countries. Agricultural products like pepper and corn are the essential crops with respect to human health food security. But, these two crops are prone to different diseases such as gray leaf spot, common rust and fruit rot which affects the productivity of crops. Further, the severity identification is also a challenging one. To address these limitations, this work presents different approaches for identifying the crop lesions and predicting the severity and thereby increasing the productivity of crops. The development of the proposed model includes steps such as dataset collection, noise removal, segmentation, feature extraction, classification and severity prediction. Initially, the crop images are pre-processed by the median filter and the pre-processed images are processed are segmented, extracted and classified by the optimized U-Net model. Moreover, hybrid optimizer which is the integration of GJA (Golden jackal algorithm) and RDA (Red deer algorithm) are utilized for precise segmentation and classification. Finally, the severity prediction is computed for the diseased leaves by the measuring the size of image pixels. The experimentation is carried out on the PlantVillage dataset; the accuracy and precision values achieved are 99.2% and 99.1%. Thus, the experimental outcomes show the effectiveness and stability of the model.

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Section: Vgg-16 Feasible Model and Enhanced Robustnessmentioning

confidence: 99%

“…Cui et al [28] developed CBAM (convolutional block attention model) with autoencoder was utilized for corn leaf disease classification. For reducing the dimsnionality, the DWT (discrete wavelet transform) was presented.…”

Section: Vgg-16 Feasible Model and Enhanced Robustnessmentioning

confidence: 99%

CSIU-Net+: Pepper and corn leaves classification and severity identification using hybrid optimization

Begum,

Syed

2024

Environ. Res. Commun.

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“…Haque et al [13] proposed a DL-based strategy for diagnosing maize crop illnesses, whereas Singh et al [14] endorsed the notion of deep transfer for maize plant leaf disease classification. Cui et al [15] took it a step further, using CBAM and a lightweight auto encoder network to classify maize leaf diseases (Table 1).…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh

Mohanty,

Ghosh,

Rahat

et al. 2023

RAiSE-2023

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“…The importance of capturing both spatial and channel-wise attention leads to the demand for CBAM. The CBAM module improves the model's selective power by recalibrating feature maps adaptively [25]. As presented in Figure 5, it accomplishes this by using a two-step procedure.…”

Section: B Proposed Architecturementioning

confidence: 99%

Abundance of Mosquito Larvae at Jagannath University and Dhaka University Campus of Dhaka, Bangladesh

Islam

Prithul

2021

Bangladesh J. Zool.

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A year-round survey on the abundance of mosquito larvae was conducted at Jagannath University - and Dhaka University - Campus from October 2018 to September 2019. A number of total 4415 mosquito larvae were collected of which 3086 were identified as Culex and 1329 were Aedes. Only 3 species belonging to 2 genera were identified in this study. At Jagannath University campus, highest number of Culex larvae was recorded in the month of August which was 167 (10.9%) and the lowest number was recorded in February which was 66 (4.3%), on the other hand, highest number of Aedes larvae was recorded in the month of July which was 137 (23.8%) and the lowest number was found in December which was 9 (1.6%). At Dhaka University campus, the highest number of Culex larvae was found in the month of April, which was 179 (11.5%) and the lowest number was found in June, which was 66 (4.3%), on the other hand, the highest number of Aedes larvae was found in the month of June which was 200 (26.5%) and lowest number was found in February which was 8 (1.06%). This study could be helpful in launching larvicidal programs in a more cost-effective way at these two urban public universities of Dhaka city. Bangladesh J. Zool. 49 (2): 321-329, 2021

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Maize leaf disease classification using CBAM and lightweight Autoencoder network

Cited by 18 publications

References 27 publications

CSIU-Net+: Pepper and corn leaves classification and severity identification using hybrid optimization

CSIU-Net+: Pepper and corn leaves classification and severity identification using hybrid optimization

Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh

Abundance of Mosquito Larvae at Jagannath University and Dhaka University Campus of Dhaka, Bangladesh

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