ArticleFrontiers in plant science2022
Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.Scientific reports · 2026Article
- Unveiling the landscape of plant virology in Saudi Arabia: seven decades of progress and future directions toward Vision 2030.Frontiers in plant science · 2026Review
- Deep learning framework using UAV imagery for multi-disease detection in cereal crops.Scientific reports · 2025Article
- AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas.Scientific reports · 2025Article
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- AI and IoT-powered edge device optimized for crop pest and disease detection.Scientific reports · 2025Article
- Residual-SwishNet: a deep learning-based approach for reliable lung cancer classification.Frontiers in oncology · 2025Article
- DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments.Plant methods · 2024Article
- Maize disease classification using transfer learning and convolutional neural network with weighted loss.Heliyon · 2024Article
- Double-stranded RNA prevents and cures infection by rust fungi.Communications biology · 2023Article
- MS-Net: a novel lightweight and precise model for plant disease identification.Frontiers in plant science · 2023Article
- A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection.Plant phenomics (Washington, D.C.) · 2023Article
- Feature Mapping for Rice Leaf Defect Detection Based on a Custom Convolutional Architecture.Foods (Basel, Switzerland) · 2022Article
- Artificial Intelligence in Biological Sciences.Life (Basel, Switzerland) · 2022Review
- DCNet: DenseNet-77-based CornerNet model for the tomato plant leaf disease detection and classification.Frontiers in plant science · 2022Article
- Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification.Frontiers in plant science · 2022Article
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5 authors.
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Abstract
The role of agricultural development is very important in the economy of a country. However, the occurrence of several plant diseases is a major hindrance to the growth rate and quality of crops. The exact determination and categorization of crop leaf diseases is a complex and time-required activity due to the occurrence of low contrast information in the input samples. Moreover, the alterations in the size, location, structure of crop diseased portion, and existence of noise and blurriness effect in the input images further complicate the classification task. To solve the problems of existing techniques, a robust drone-based deep learning approach is proposed. More specifically, we have introduced an improved EfficientNetV2-B4 with additional added dense layers at the end of the architecture. The customized EfficientNetV2-B4 calculates the deep key points and classifies them in their related classes by utilizing an end-to-end training architecture. For performance evaluation, a standard dataset, namely, the PlantVillage Kaggle along with the samples captured using a drone is used which is complicated in the aspect of varying image samples with diverse image capturing conditions. We attained the average precision, recall, and accuracy values of 99.63, 99.93, and 99.99%, respectively. The obtained results confirm the robustness of our approach in comparison to other recent techniques and also show less time complexity.
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