Maize Small Leaf Spot Classification Based on Improved Deep Convolutional Neural Networks with a Multi-Scale Attention Mechanism

Yin, Chenghai; Zeng, Tiwei; Zhang, Huiming; Wei, Fu; Wang, Lei; Yao, Siyu

doi:10.3390/agronomy12040906

Cited by 30 publications

(20 citation statements)

References 29 publications

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“…The studies ( Liu et al., 2020 ; Zhang et al., 2021 ; Amin et al., 2022 ) used various deep CNN architectures and the PlantVillage maize disease dataset as transfer learning. Few of them employed the attention method in CNNs to enhance classification accuracy ( Chen et al., 2021 ; Zeng et al., 2022a ; Qian et al., 2022 ; Yin et al., 2022 ). However, the accurate identification of maize disease is difficult under realistic field settings.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…However, the accurate identification of maize disease is difficult under realistic field settings. The studies may ( Ahmad et al., 2021 ; Chouhan et al., 2021 ; Xiang et al., 2021 ; Corn or maize leaf disease dataset, 2022 ; Yin et al., 2022 ) suffer from the model over-fitting issue as a result of their complex network structures. Moreover, the comparative approaches show robust performance on samples having a simple background or limited disease categories.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This method is evaluated using corn leaf disease samples from the PlanVillage database and thus has limited generalization for real environment conditions. Zeng et al (2022) presented a CNN model namely SKPSNet-50 for the recognition of different maize leaf diseases at an early stage. A selected kernel unit with a swish activation function was integrated into the ResNet50 model to improve the feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…The overall identification accuracy for maize leaf diseases and healthy leaves was 95.4%. Yin et al (2022) introduced an improved GoogleNet architecture for the grading of maize SLB spots. A dilated inception block was added to improve the extraction of multi-scale features.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Albahli

Masood

2022

Front. Plant Sci.

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Maize leaf disease significantly reduces the quality and overall crop yield. Therefore, it is crucial to monitor and diagnose illnesses during the growth season to take necessary actions. However, accurate identification is challenging to achieve as the existing automated methods are computationally complex or perform well on images with a simple background. Whereas, the realistic field conditions include a lot of background noise that makes this task difficult. In this study, we presented an end-to-end learning CNN architecture, Efficient Attention Network (EANet) based on the EfficientNetv2 model to identify multi-class maize crop diseases. To further enhance the capacity of the feature representation, we introduced a spatial-channel attention mechanism to focus on affected locations and help the detection network accurately recognize multiple diseases. We trained the EANet model using focal loss to overcome class-imbalanced data issues and transfer learning to enhance network generalization. We evaluated the presented approach on the publically available datasets having samples captured under various challenging environmental conditions such as varying background, non-uniform light, and chrominance variances. Our approach showed an overall accuracy of 99.89% for the categorization of various maize crop diseases. The experimental and visual findings reveal that our model shows improved performance compared to conventional CNNs, and the attention mechanism properly accentuates the disease-relevant information by ignoring the background noise.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Albahli

Masood

2022

Front. Plant Sci.

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“…Several scholars have applied it to fruit sorting tasks and achieved successful results ( Yin et al., 2022 ; Lin et al., 2022 ). Some researchers have also carried out relevant studies in the field of surface defect identification of jujube, which will be elaborated in the section “Related Work”.…”

Section: Introductionmentioning

confidence: 99%

JujubeNet: A high-precision lightweight jujube surface defect classification network with an attention mechanism

Jiang

Yuan

et al. 2023

Front. Plant Sci.

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Surface Defect Detection (SDD) is a significant research content in Industry 4.0 field. In the real complex industrial environment, SDD is often faced with many challenges, such as small difference between defect imaging and background, low contrast, large variation of defect scale and diverse types, and large amount of noise in defect images. Jujubes are naturally growing plants, and the appearance of the same type of surface defect can vary greatly, so it is more difficult than industrial products produced according to the prescribed process. In this paper, a ConvNeXt-based high-precision lightweight classification network JujubeNet is presented to address the practical needs of Jujube Surface Defect (JSD) classification. In the proposed method, a Multi-branching module using Depthwise separable Convolution (MDC) is designed to extract more feature information through multi-branching and substantially reduces the number of parameters in the model by using depthwise separable convolutions. What’s more, in our proposed method, the Convolutional Block Attention Module (CBAM) is introduced to make the model concentrate on different classes of JSD features. The proposed JujubeNet is compared with other mainstream networks in the actual production environment. The experimental results show that the proposed JujubeNet can achieve 99.1% classification accuracy, which is significantly better than the current mainstream classification models. The FLOPS and parameters are only 30.7% and 30.6% of ConvNeXt-Tiny respectively, indicating that the model can quickly and effectively classify JSD and is of great practical value.

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A review on detection and differentiation of maize diseases and pests by imaging sensors

Pfordt,

Paulus

2024

J Plant Dis Prot

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Maize (Zea mays) is one of the most important cereal crops globally, providing food, feed, and fuel for humans and animals. However, the production of maize is frequently challenged by various stresses that can severely impact crop yield and quality. Early detection and identification are critical for implementing timely and effective control measures to reduce their impact. Therefore, it is essential to establish effective disease and pest monitoring and management strategies to ensure sustainable maize production and maintain food security. Conventional detection methods relying on visual assessments by human experts are time-consuming, labour-intensive, and subjective. The emergence of imaging sensors, computer vision technologies, and high throughput platforms has revolutionised the detection and differentiation of crop diseases, offering a non-invasive and rapid approach. In this publication, we present a review on imaging sensors for the detection and differentiation of symptoms on maize caused by diseases and pests. The main advantages and limitations of each imaging sensor, along with its applications and case studies for maize disease detection, are introduced and discussed. Recent advances in the visible, near-infrared, and hyperspectral imaging for maize disease detection are highlighted, and the importance of different sensors is discussed. The goal is to provide a comprehensive overview of the current state-of-the-art in this field, highlighting the potential of imaging sensors for improving maize production and identifying future research directions in this area.

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Maize Small Leaf Spot Classification Based on Improved Deep Convolutional Neural Networks with a Multi-Scale Attention Mechanism

Cited by 30 publications

References 29 publications

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

JujubeNet: A high-precision lightweight jujube surface defect classification network with an attention mechanism

A review on detection and differentiation of maize diseases and pests by imaging sensors

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