Application of Image Segmentation Technology in Crop Disease Detection and Recognition

Deng, Leilei; Wang, Zhenghao; Zhou, Hui

doi:10.1007/978-3-030-06137-1_33

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…This situation not only leads to environmental pollution but also causes unnecessary economic losses. The application of computer vision technology in pest detection has become increasingly popular [3]. The ability of computer vision technology to accurately identify pest types has provided the basis for automated and accurate pest detection [4].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s

2023

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The real-time target detection of crop pests can help detect and control pests in time. In this study, we built a lightweight agricultural pest identification method based on modified Yolov5s and reconstructed the original backbone network in tandem with MobileNetV3 to considerably reduce the number of parameters in the network model. At the same time, the ECA attention mechanism was introduced into the MobileNetV3 shallow network to meet the aim of effectively enhancing the network’s performance by introducing a limited number of parameters. A weighted bidirectional feature pyramid network (BiFPN) was utilized to replace the path aggregation network (PAnet) in the neck network to boost the feature extraction of tiny targets. The SIoU loss function was utilized to replace the CIoU loss function to increase the convergence speed and accuracy of the model prediction frame. The updated model was designated ECMB-Yolov5. In this study, we conducted experiments on eight types of common pest dataset photos, and comparative experiments were conducted using common target identification methods. The final model was implemented on an embedded device, the Jetson Nano, for real-time detection, which gave a reference for further application to UAV or unmanned cart real-time detection systems. The experimental results indicated that ECMB-Yolov5 decreased the number of parameters by 80.3% and mAP by 0.8% compared to the Yolov5s model. The real-time detection speed deployed on embedded devices reached 15.2 FPS, which was 5.7 FPS higher than the original model. mAP was improved by 7.1%, 7.3%, 9.9%, and 8.4% for ECMB-Yolov5 compared to Faster R-CNN, Yolov3, Yolov4, and Yolov4-tiny models, respectively. It was verified through experiments that the improved lightweight method in this study had a high detection accuracy while significantly reducing the number of parameters and accomplishing real-time detection.

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Section: Introductionmentioning

confidence: 99%

A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s

2023

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“…The image processing methods include extracting the color and texture of disease spots through grayscale values or performing pixel-level segmentation of disease spots. Deng et al (2019) Support vector machine (SVM), Mokhtar et al (2015) k-means clustering, Naive Bayes, etc. Ma et al (2018) are most widely used classifier.…”

Section: Introductionmentioning

confidence: 99%

Identification of apple leaf disease via novel attention mechanism based convolutional neural network

Cheng,

2023

Front. Plant Sci.

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IntroductionThe identification of apple leaf diseases is crucial for apple production.MethodsTo assist farmers in promptly recognizing leaf diseases in apple trees, we propose a novel attention mechanism. Building upon this mechanism and MobileNet v3, we introduce a new deep learning network.Results and discussionApplying this network to our carefully curated dataset, we achieved an impressive accuracy of 98.7% in identifying apple leaf diseases, surpassing similar models such as EfficientNet-B0, ResNet-34, and DenseNet-121. Furthermore, the precision, recall, and f1-score of our model also outperform these models, while maintaining the advantages of fewer parameters and less computational consumption of the MobileNet network. Therefore, our model has the potential in other similar application scenarios and has broad prospects.

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AppleLeafNet: a lightweight and efficient deep learning framework for diagnosing apple leaf diseases

Ali,

Khalid,

Farrash

et al. 2024

Front. Plant Sci.

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Accurately identifying apple diseases is essential to control their spread and support the industry. Timely and precise detection is crucial for managing the spread of diseases, thereby improving the production and quality of apples. However, the development of algorithms for analyzing complex leaf images remains a significant challenge. Therefore, in this study, a lightweight deep learning model is designed from scratch to identify the apple leaf condition. The developed framework comprises two stages. First, the designed 37-layer model was employed to assess the condition of apple leaves (healthy or diseased). Second, transfer learning was used for further subclassification of the disease class (e.g., rust, complex, scab, and frogeye leaf spots). The trained lightweight model was reused because the model trained with correlated images facilitated transfer learning for further classification of the disease class. A dataset available online was used to validate the proposed two-stage framework, resulting in a classification rate of 98.25% for apple leaf condition identification and an accuracy of 98.60% for apple leaf disease diagnosis. Furthermore, the results confirm that the proposed model is lightweight and involves relatively fewer learnable parameters in comparison with other pre-trained deep learning models.

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Application of Image Segmentation Technology in Crop Disease Detection and Recognition

Cited by 3 publications

References 7 publications

A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s

A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s

Identification of apple leaf disease via novel attention mechanism based convolutional neural network

AppleLeafNet: a lightweight and efficient deep learning framework for diagnosing apple leaf diseases

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