ArticleScientific reports2025
Hybrid vision GNNs based early detection and protection against pest diseases in coffee plants.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- DiffPlantCT: a training-free, annotation-free approach to cross-species plant CT image segmentation.Plant methods · 2026Article
- An advanced framework for predicting pollen outbreaks using multi-input multi-output temporal convolutional networks and firefly algorithm.Scientific reports · 2026Article
- Hybrid CNN-GNN architectures with distributed training for heathland plant classification.Environmental monitoring and assessment · 2026Article
- A hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification.BMC plant biology · 2026Article
- Metaheuristic optimization based improved neural network for the timely prediction of paddy leaf diseases.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants.BMC plant biology · 2026Article
- Efficient deep learning framework for arecanut disease detection using graph neural network and Bat algorithm.Scientific reports · 2026Article
- Enhanced corn leaf disease detection using sharpness-aware minimization optimized CNNs.Plant methods · 2026Article
- DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection.Plant methods · 2026Article
- Feature extraction in sensor plant disease datasets using reformed membership functions independent of class variables.Scientific reports · 2026Article
- AgriFusionNet: a context-aware multimodal leaf disease diagnosis and classification system for sustainable plant health monitoring.Frontiers in fungal biology · 2026Article
- A novel framework GRCornShot for corn disease detection using few shot learning with prototypical network.Scientific reports · 2025Article
- Unmanned aerial vehicle payload technology applications in agriculture and other low-altitude scenarios: a review.Frontiers in plant science · 2025Review
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4 authors.
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Abstract
Agriculture is an essential foundation that supports numerous economies, and the longevity of the coffee business is of paramount significance. Controlling and safeguarding coffee farms from harmful pests, including the Coffee Berry Borer, Mealybugs, Scales, and Leaf Miners, which may drastically affect crop productivity and quality. Standard methods for detecting pest diseases sometimes need specialized knowledge or thorough analysis, leading to a substantial commitment of time and effort. To address this challenge, researchers have explored the use of computer vision and deep learning techniques for the automated detection of plant pest diseases. This paper presents a novel strategy for the early detection of coffee crop killers using Hybrid Vision Graph Neural Networks (HV-GNN) in coffee plantations. The model was trained and validated using a curated dataset of 2850 labelled coffee plant images, which included diverse insect infestations. The HV-GNN design allows the model to recognize individual pests within images and capture the complex relationships between them, potentially leading to improved detection accuracy. HV-GNN proficiently detect pests by analyzing their visual characteristics and elucidating the interconnections among pests in images. Experimental findings indicate that HV-GNN attain a detection accuracy of 93.6625%, exceeding that of leading models. The increased accuracy underscores the feasibility of practical implementation, enabling proactive pest control to protect coffee farms and improve agricultural output.
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