ArticleFrontiers in plant science2019
Deep Learning-Based Phenotyping System With Glocal Description of Plant Anomalies and Symptoms.
Article in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- OPTIFARM: Benchmarking YOLO Architectures for Location-Robust Potato Quality Detection.Foods (Basel, Switzerland) · 2026Article
- A machine-learning-powered spectral-dominant multimodal soft wearable system for long-term and early-stage diagnosis of plant stresses.Science advances · 2025Article
- Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO.Frontiers in plant science · 2024Article
- Plant disease recognition datasets in the age of deep learning: challenges and opportunities.Frontiers in plant science · 2024Article
- Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds.Frontiers in plant science · 2023Article
- Improved YOLOX-Tiny network for detection of tobacco brown spot disease.Frontiers in plant science · 2023Article
- DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis.Frontiers in plant science · 2022Article
- The Detection Method of Potato Foliage Diseases in Complex Background Based on Instance Segmentation and Semantic Segmentation.Frontiers in plant science · 2022Article
- An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN.Sensors (Basel, Switzerland) · 2021Article
- Early real-time detection algorithm of tomato diseases and pests in the natural environment.Plant methods · 2021Article
- Machine learning in plant science and plant breeding.iScience · 2021Review
- Open Set Self and Across Domain Adaptation for Tomato Disease Recognition With Deep Learning Techniques.Frontiers in plant science · 2021Article
- Multiscale Parallel Algorithm for Early Detection of Tomato Gray Mold in a Complex Natural Environment.Frontiers in plant science · 2021Article
- Diseases Detection of Occlusion and Overlapping Tomato Leaves Based on Deep Learning.Frontiers in plant science · 2021Article
- Improving Accuracy of Tomato Plant Disease Diagnosis Based on Deep Learning With Explicit Control of Hidden Classes.Frontiers in plant science · 2021Article
- Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model.Plant methods · 2020Article
- Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network.Frontiers in plant science · 2020Article
- Attention-Based Recurrent Neural Network for Plant Disease Classification.Frontiers in plant science · 2020Article
- Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network.Frontiers in plant science · 2020Article
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3 authors.
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Abstract
Recent advances in Deep Neural Networks have allowed the development of efficient and automated diagnosis systems for plant anomalies recognition. Although existing methods have shown promising results, they present several limitations to provide an appropriate characterization of the problem, especially in real-field scenarios. To address this limitation, we propose an approach that besides being able to efficiently detect and localize plant anomalies, allows to generate more detailed information about their symptoms and interactions with the scene, by combining visual object recognition and language generation. It uses an image as input and generates a diagnosis result that shows the location of anomalies and sentences describing the symptoms as output. Our framework is divided into two main parts: First, a detector obtains a set of region features that contain the anomalies using a Region-based Deep Neural Network. Second, a language generator takes the features of the detector as input and generates descriptive sentences with details of the symptoms using Long-Short Term Memory (LSTM). Our loss metric allows the system to be trained end-to-end from the object detector to the language generator. Finally, the system outputs a set of bounding boxes along with the sentences that describe their symptoms using glocal criteria into two different ways: a set of specific descriptions of the anomalies detected in the plant and an abstract description that provides general information about the scene. We demonstrate the efficiency of our approach in the challenging tomato diseases and pests recognition task. We further show that our approach achieves a mean Average Precision (mAP) of 92.5% in our newly created Tomato Plant Anomalies Description Dataset. Our objective evaluation allows users to understand the relationships between pathologies and their evolution throughout their stage of infection, location in the plant, symptoms,
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