ArticleFrontiers in plant science2024
Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A lightweight graph-enhanced deep learning framework for explainable cucumber leaf disease diagnosis.Scientific reports · 2026Article
- Explainable CNN framework for accurate crop disease detection using plant leaf images.Scientific reports · 2026Article
- Research on Apple Surface Disease Detection Method Based on Improved YOLOv11s.Foods (Basel, Switzerland) · 2026Article
- Lightweight attention enhanced YOLOv11 for accurate multi class detection of brinjal diseases.Scientific reports · 2026Article
- Q-TriLSTM-Vision: a quantum-interference- augmented tri-stream LSTM for multi-label plant stress recognition on the OLID-I benchmark.Frontiers in plant science · 2026Article
- A multi-agent multi-modal LLM with Monte Carlo tree search for interpretable plant disease classification.Frontiers in plant science · 2026Article
- DFSNet: directional feature aggregation and shape-aware supervision for eggplant pest and disease detection.Frontiers in plant science · 2026Article
- Deep learning techniques for early detection and classification of leaf diseases in crops.Frontiers in plant science · 2026Article
- Pomegranate disease diagnosis with severity estimation and treatment remedies using deep learning and RAG-based LLM.Scientific reports · 2025Article
- Enhanced plant disease classification with attention-based convolutional neural network using squeeze and excitation mechanism.Frontiers in artificial intelligence · 2025Article
- Research on the intelligent detection model of plant diseases based on MamSwinNet.Frontiers in plant science · 2025Article
- IMNM: integrated multi-network model for identifying pepper leaf diseases.Frontiers in plant science · 2025Article
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Authors and funding
7 authors.
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Abstract
This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. In light of increasing global challenges related to food security and sustainable agriculture, this research focuses on developing a highly efficient and accurate automated system for identifying plant diseases, thereby contributing to enhanced crop protection and yield optimization. The proposed model is trained on a comprehensive dataset encompassing various plant species and disease categories, ensuring robust performance and adaptability. By evaluating the model with online random images, demonstrate its significant adaptability and effectiveness in overcoming key challenges, such as achieving high accuracy and meeting the practical demands of agricultural applications. The architectural modifications are specifically designed to enhance feature extraction and classification performance, all while maintaining computational efficiency. The evaluation results further highlight the model's effectiveness, achieving an accuracy of 98% and an F1 score of 98.2%. These findings emphasize the model's potential as a practical tool for disease identification in agricultural applications, supporting timely and informed decision-making for crop protection.
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