ArticleSensors (Basel, Switzerland)2021
An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Tomato leaf disease and severity prediction using multi-task learning.BMC plant biology · 2026Article
- PLDC-Net: A Domain-Specific Base Model for Plant Leaf Disease Classification Domain Adaptation Tasks.Plant direct · 2026Article
- Deep learning-based high-throughput phenotyping for tiller quantification in interspecific bentgrass hybrids using YOLOv8.Frontiers in plant science · 2026Article
- Assessing the performance of domain-specific models for plant leaf disease classification: a comprehensive benchmark of transfer-learning on open datasets.Scientific reports · 2025Article
- Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV.Plants (Basel, Switzerland) · 2025Article
- VM-YOLO: YOLO with VMamba for Strawberry Flowers Detection.Plants (Basel, Switzerland) · 2025Article
- Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds.Plant methods · 2025Article
- Article
- Comparative analysis of adaptive and general labeling methods for soybean leaf detection.Frontiers in plant science · 2025Article
- Enhancing anomaly detection in plant disease recognition with knowledge ensemble.Frontiers in plant science · 2025Article
- YOLOv8-G: An Improved YOLOv8 Model for Major Disease Detection in Dragon Fruit Stems.Sensors (Basel, Switzerland) · 2024Article
- The impact of fine-tuning paradigms on unknown plant diseases recognition.Scientific reports · 2024Article
- Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks.Foods (Basel, Switzerland) · 2024Article
- Semantic segmentation of microbial alterations based on SegFormer.Frontiers in plant science · 2024Article
- A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms.Plants (Basel, Switzerland) · 2023Article
- Smart Strawberry Farming Using Edge Computing and IoT.Sensors (Basel, Switzerland) · 2022Article
- Data-centric annotation analysis for plant disease detection: Strategy, consistency, and performance.Frontiers in plant science · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases. Deep neural networks have achieved state-of-the-art performance in numerous aspects of human life including the agriculture sector. The current state of the literature indicates that there are a limited number of datasets available for autonomous strawberry disease and pest detection that allow fine-grained instance segmentation. To this end, we introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions. As a baseline for future works, we propose a model based on the Mask R-CNN architecture that effectively performs instance segmentation for these seven diseases. We use a ResNet backbone along with following a systematic approach to data augmentation that allows for segmentation of the target diseases under complex environmental conditions, achieving a final mean average precision of 82.43%.
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