An enhanced lightweight model for apple leaf disease detection in complex orchard environments

Frontiers in Plant Science, Год журнала: 2025, Номер 16

Опубликована: Март 13, 2025

Automated detection of apple leaf diseases is crucial for predicting and preventing losses enhancing yields. However, in complex natural environments, factors such as light variations, shading from branches leaves, overlapping disease spots often result reduced accuracy detecting diseases. To address the challenges small-target on leaves backgrounds difficulty mobile deployment, we propose an enhanced lightweight model, ELM-YOLOv8n.To mitigate high consumption computational resources real-time deployment existing models, integrate Fasternet Block into C2f backbone network neck network, effectively reducing parameter count load model. In order to enhance network’s anti-interference ability its capacity differentiate between similar diseases, incorporate Efficient Multi-Scale Attention (EMA) within deep structure in-depth feature extraction. Additionally, design a detail-enhanced shared convolutional scaling head (DESCS-DH) enable model capture edge information issues poor performance object across different scales. Finally, employ NWD loss function replace CIoU function, allowing locate identify small targets more accurately further robustness, thereby facilitating rapid precise identification Experimental results demonstrate ELM-YOLOv8n’s effectiveness, achieving 94.0% F1 value 96.7% mAP50 value—a significant improvement over YOLOv8n. Furthermore, are by 44.8% 39.5%, respectively. The ELM-YOLOv8n better suited devices while maintaining accuracy.

Язык: Английский

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Semi-supervised deep learning framework based on modified pyramid scene parsing network for multi-label fine-grained classification and diagnosis of apple leaf diseases

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 151, С. 110743 - 110743

Опубликована: Апрель 8, 2025

Язык: Английский

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Early detection of red palm weevil infestations using deep learning classification of acoustic signals

Computers and Electronics in Agriculture, Год журнала: 2023, Номер 212, С. 108154 - 108154

Опубликована: Авг. 28, 2023

Язык: Английский

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LeafSpotNet: A deep learning framework for detecting leaf spot disease in jasmine plants

Artificial Intelligence in Agriculture, Год журнала: 2024, Номер 12, С. 1 - 18

Опубликована: Март 11, 2024

Leaf blight spot disease, caused by bacteria and fungi, poses a threat to plant health, leading leaf discoloration diminished agricultural yield. In response, we present MobileNetV3-based classifier designed for the Jasmine plant, leveraging lightweight Convolutional Neural Networks (CNNs) accurately identify disease stages. The model integrates depth-wise convolution layers max pool enhanced feature extraction, focusing on crucial low-level features indicative of disease. Through preprocessing techniques, including data augmentation with Conditional GAN Particle Swarm Optimization selection, achieves robust performance. Evaluation curated datasets demonstrates an outstanding 97% training accuracy, highlighting its efficacy. Real-world testing diverse conditions, such as extreme camera angles varied lighting, attests model's resilience, yielding test accuracies between 94% 96%. dataset's tailored design CNN-based classification ensures result reliability. Importantly, classification, marked fast computation time reduced size, positions it efficient solution real-time applications. This comprehensive approach underscores proposed classifier's significance in addressing challenges commercial crops.

Язык: Английский

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EResNet‐SVM: an overfitting‐relieved deep learning model for recognition of plant diseases and pests

Journal of the Science of Food and Agriculture, Год журнала: 2024, Номер 104(10), С. 6018 - 6034

Опубликована: Март 14, 2024

Abstract BACKGROUND The accurate recognition and early warning for plant diseases pests are a prerequisite of intelligent prevention control pests. As result the phenotype similarity hazarded after occur, as well interference external environment, traditional deep learning models often face overfitting problem in pests, which leads to not only slow convergence speed network, but also low accuracy. RESULTS Motivated by above problems, present study proposes model EResNet‐support vector machine (SVM) alleviate classification First, feature extraction capability is improved increasing layers convolutional neural network. Second, order‐reduced modules embedded sparsely activated function introduced reduce complexity overfitting. Finally, classifier fused SVM fully connected transforms original non‐linear into linear high‐dimensional space further improve accuracy ablation experiments demonstrate that structure can effectively experimental results typical show proposed EResNet‐SVM has 99.30% test eight conditions (seven one normal), 5.90% higher than ResNet18. Compared with classic AlexNet, GoogLeNet, Xception, SqueezeNet DenseNet201 models, 5.10%, 7%, 8.10%, 6.20% 1.90%, respectively. testing 6 insect 100%, 3.90% ResNet18 model. CONCLUSION This research provides useful references alleviating learning, theoretical technical support detection © 2024 Society Chemical Industry.

Язык: Английский

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