ArticleSensors (Basel, Switzerland)2017
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 191 papers, 1 of them a synthesis that pooled it.
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Who cites it
191 citing papers in PubMed, 1 synthesis or guideline pooled it, 1,477 citations in OpenAlex.
- Plant Disease Detection and Classification: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Lesion-Aware Multiple-Instance Learning for Grapevine Disease Recognition from Field RGB Images.Sensors (Basel, Switzerland) · 2026Article
- A YOLO11-MSC-Based Method for Small Pest Detection in Insect Pest Monitoring Lamp Images.Sensors (Basel, Switzerland) · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Deep learning based apple leaf disease detection using spatially modulated continuouslayer.Scientific reports · 2026Article
- Fungal infections: Classification performance and detectability with machine learning models.Scientific reports · 2026Article
- Deep learning-based citrus plant disease classification using a computationally efficient CNN model.Scientific reports · 2026Article
- Frost damage segmentation in grapevine organs using YOLOv11s with ASPP and dynamic confidence thresholding.Scientific reports · 2026Article
- Artificial Intelligence-Driven Plant Disease Detection and Diagnosis: A Comprehensive Review of Deep Learning Approaches, Multimodal Sensing Technologies, and Future Perspectives in Precision Agriculture.The plant pathology journal · 2026Review
- A wavelet-based frequency-domain approach for accurate multi-crop disease detection.Scientific reports · 2026Article
- Detection of Soil-Borne Pathogens Using Fine-Tuned Deep Learning Models: A Case Study on the Soybean Cyst Nematode (Heterodera glycines Ichinohe).The plant pathology journal · 2026Article
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- YOLO-based deep learning framework for real-time multi-class plant health monitoring in precision agriculture.Scientific reports · 2026Article
- Frequency-domain attention enhanced YOLOv11-EfficientFormerV2 for Tiny lesion detection in complex field plant images.Frontiers in plant science · 2026Article
- Interpretable crop pest and disease identification based on comparative concept tree.PloS one · 2026Article
- Mobile-assisted deep learning framework for identification of insect pests and diseases of maize from field images.Frontiers in plant science · 2026Article
- RubberFormer: a transformer-based detection benchmark for rubber tree powdery mildew.Frontiers in plant science · 2026Article
- Toward smart agriculture: a hybrid mamba-transformer vision framework for plant disease detection.Frontiers in plant science · 2026Article
- FD-DEIM: efficient and robust detection of small, irregular foliar lesions in the field.Frontiers in plant science · 2026Article
- A lightweight RPB-YOLO11-based detector improves mobile phenotyping of rice panicle blast.Frontiers in plant science · 2026Article
131 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called "deep learning meta-architectures". We combine each of these meta-architectures with "deep feature extractors" such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant's surrounding area.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.