Deep learning based ensemble model for accurate tomato leaf disease classification by leveraging ResNet50 and MobileNetV2 architectures

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 22, 2025

Global food security depends on tomato growing, but several fungal, bacterial, and viral illnesses seriously reduce productivity quality, therefore causing major financial losses. Reducing these impacts early, exact diagnosis of diseases. This work provides a deep learning-based ensemble model for leaf disease classification combining MobileNetV2 ResNet50. To improve feature extraction, the models were tweaked by changing their output layers with GlobalAverage Pooling2D, Batch Normalization, Dropout, Dense layers. take use complimentary qualities, maps from both combined. study uses publicly available dataset Kaggle classification. Training 11,000 annotated pictures spanning 10 categories, including bacterial spot, early blight, late mold, septoria spider mites, target yellow curl virus, mosaic healthy leaves. Data preprocessing included image resizing splitting, along an 80-10-10 split, allocating 80% training, 10% testing, validation to ensure balanced evaluation. The proposed 99.91% test accuracy, suggested was quite remarkable. Furthermore, guaranteeing strong performance across all showed great precision (99.92%), recall (99.90%), F1-score 99.91%. With few misclassifications, confusion matrix verified almost flawless even further. These findings show how well learning can automate diagnosis, providing scalable accurate solution smart agriculture. By means intervention agriculture techniques, strategy has potential crop health monitoring, economic losses, encourage sustainable farming practices.

Язык: Английский

A Lightweight Framework for Protected Vegetable Disease Detection in Complex Scenes

Food Science & Nutrition, Год журнала: 2025, Номер 13(5)

Опубликована: Май 1, 2025

ABSTRACT The rapid development of computer vision technology has provided new technical support for smart agriculture. Vegetable diseases represent a significant threat to agricultural production, with severity that cannot be ignored. However, through scientifically effective prevention and control measures, these negative impacts can significantly mitigated. Intelligent disease detection systems, as advanced methods replacing traditional manual inspection, have become important means developing agriculture improving the efficiency vegetable production management. Nevertheless, is not only time‐consuming labor‐intensive but also faces accuracy limitations, while existing still encounter series challenges when confronting complex backgrounds, diverse manifestations, varying degrees occlusion in real cultivation environments, including insufficient anti‐interference capabilities, limited precision, suboptimal real‐time performance. This research addresses practical data acquisition sample scarcity protected by proposing an innovative strategy implements differentiated augmentation technique combinations different categories samples, enhancing model's resistance environmental interference. Based on integrated concepts machine deep learning, we developed lightweight network named VegetableDet. innovatively combines Deformable Attention Transformer (DAT) YOLOv8n backbone architecture, perception capabilities long‐range feature dependencies. Simultaneously, Channel‐Spatial Adaptive Mechanism (CSAAM) into Neck network, achieving precise localization enhancement key features. To address issue low model convergence efficiency, further designed hierarchical progressive transfer learning training strategy, effectively accelerating adaptation process accuracy. Experimental evaluation demonstrates our custom comprehensive dataset, VegetableDet exhibits excellent performance detecting 30 healthy samples across 5 types, precision (P), recall (R), average (AP) all exceeding 90%, overall mean Average Precision (mAP) reaching 94.31%. powerful adaptability under conditions, providing reliable monitoring diseases, broad application prospects.

Язык: Английский

Deep Learning Method with Domain-Task Adaptation and Client-Specific Fine-Tuning YOLO11 Model for Counting Greenhouse Tomatoes

Applied System Innovation, Год журнала: 2025, Номер 8(3), С. 71 - 71

Опубликована: Май 27, 2025

This article discusses the tasks involved in operational assessment of volume produced goods, such as tomatoes. The large-scale implementation computer vision systems greenhouses requires approaches that reduce costs, time and complexity, particularly creating training data preparing neural network models. Publicly available models like YOLO often lack accuracy needed for specific tasks. study proposes a method sequential detection models, incorporating Domain-Task Adaptation Client-Specific Fine-Tuning. model is initially trained on large, specialized dataset tomato detection, followed by fine-tuning with small custom reflecting real greenhouse conditions. results light YOLO11n achieving high validation (mAP50 > 0.83, Precision 0.75, Recall 0.73) while reducing computational resource requirements. Additionally, was developed captures unique challenges environments, dense vegetation occlusions. An algorithm counting tomatoes also created, which processes video frames to accurately count only visible front row plants. can be utilized mobile surveillance systems, enhancing monitoring efficiency greenhouses.

Язык: Английский