Evaluating CNN Models for Chicken Disease Identification Using Feces Image

Poultry-CViTNet. A Hybrid ViT--CNN Network for Non-Invasive Poultry Health Monitoring Based on Fecal Images

Poultry diseases remain a major challenge for sustainable poultry production because delayed diagnosis can increase mortality, treatment costs, and the risk of disease transmission across farms. Conventional diagnostic methods based on manual observation, veterinary inspection, and laboratory testing are often time-consuming, labor-intensive, and difficult to scale in low-resource farming environments. To address these limitations, this study proposes a hybrid Convolutional Neural Network (CNN) and Vision Transformer (ViT) framework for automated poultry health monitoring using fecal images. The proposed model combines the local texture extraction capability of CNNs with the global contextual modeling ability of ViTs, enabling effective discrimination between healthy and unhealthy fecal samples. The proposed CNN–ViT model was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis, with additional ablation experiments. The experimental results show that the hybrid model achieved an accuracy of 97.47 ± 0.36%, precision of 97.50 ± 0.38%, recall of 97.47 ± 0.41%, and F1-score of 97.47 ± 0.39%, outperforming the CNN-only and ViT-only baseline models. The ablation study confirms that integrating local convolutional features with transformer-based global attention improves classification reliability. These results demonstrate the potential of the proposed framework as a non-invasive, low-cost, and scalable decision-support tool for early poultry disease screening in smart farming environments. Additionally, the model contained only 3.62 million parameters, required 2.60 GFLOPs, and processed 194.95 images per second. Furthermore, Grad-CAM-based interpretability was applied to visualize the model’s decision-making process.

Smart Mobile App for Detecting Coccidiosis in Droppings Using Deep Learning ⋆

Selective Detection of Black Rot in Grapevine Leaves Using Convolutional Neural Networks

Grapevine diseases represent a major threat to vineyard productivity, with Black Rot being among the most destructive due to its rapid spread and visual similarity to other diseases. These diseases are associated with a diversity of pathogenic agents, namely fungi, oomycetes, bacteria and pests. While prior work frequently reports high accuracy in controlled multi-class classification, practical deployments commonly require selective detection of a target disease against a contaminated negative class. In this work, Black Rot detection is formulated as a binary classification task, where the negative class includes healthy leaves and other visually similar diseases. This study employed ImageNet pretrained Convolutional Neural Network (CNN) backbones, MobileNetV2, DenseNet121, ResNet50 and VGG16, using a two-stage transfer learning protocol. The ability of the CNN models to accurately identify Black Rot cases was evaluated using standard classification metrics, namely accuracy, precision, recall and F1-score. The results show clear differences in detection behaviour across architectures. ResNet50 achieves the highest overall performance, obtaining 100.0% precision, with no false positives while maintaining a high recall 96.3% and a F1-score of 98.1%, with an accuracy of 98.9%. Overall, the achieved performance is competitive and exceeds values reported in the literature, while addressing a more realistic contaminated-negative scenario.

Poultryguard: AI-Driven Poultry Disease Detection and Clinical Decision Support Using Efficientnet-B3

Poultry farming is a critical component of global food security, yet it is highly vulnerable to fast spreading infectious diseases such as coccidiosis, salmonellosis and Newcastle disease. Conventional diagnostic pathways depend on clinical examination and laboratory tests, which are time consuming, expertise dependent and often inaccessible for small and medium scale farmers. This paper presents a combined survey and implementation of PoultryGuard, a smartphone-centric framework for early poultry disease detection from chicken fecal images using convolutional neural networks. First, recent progress in image-based poultry disease detection is reviewed, with emphasis on fecal-image classification and transfer learning strategies. Then, the design and implementation of an EfficientNet-B3 based classifier trained on 8,067 annotated fecal images across four classes (healthy, coccidiosis, salmonella and Newcastle disease) is described. The model is deployed inside a Streamlit application that integrates user authentication, real time image acquisition, a structured veterinary knowledge base, automated diagnostic report generation in PDF format, Telegram based emergency alerts, a veterinary hospital locator and an admin analytics dashboard. Experimental evaluation on a heldout test set shows that the implemented EfficientNet-B3 model achieves 93.73% accuracy, with macro-averaged precision, recall and F1-scores above 0.93, while maintaining low inference latency suitable for smartphone assisted deployment. The study shows that poultry disease management in resource constrained farm environments can be revolutionized by combining deep learning with clinical decision support and communication modules.

Deep Learning-Based Intelligent System for Mango Leaf Disease Diagnosis

The infection of mango leaves is a major yield and fruit quality loss problem, and the necessity of having a precise and early diagnostic system for sustainable agriculture. Five convolutional neural network (CNN) architectures–namely, VGG16, VGG19, ResNet50, DenseNet121, and a custom-built AlexNet variant–were evaluated for their performance in classifying eight categories of mango leaf diseases. All models were pre-trained using a well-defined dataset of healthy and infected leaves and fine-tuned using transfer learning. VGG16 had the highest test accuracy of 99%, which was better than ResNet50 (91.5%), DenseNet121 (90.5%), VGG19 (87%), and AlexNet (86.5%). Further evaluation of these models using confusion matrices and F1-scores was conducted to ensure the strength of these models for various diseases including powdery mildew, anthracnose, and bacterial canker. Overall, the results underscore that advanced CNN models—particularly VGG16— can deliver near-expert precision in automated mango leaf disease identification. The leaf disease of mango is a major issue confronting farmers in Southeast Asia, particularly in India. Mango leaf disease is one of the biggest challenges faced by the farmers of south-east Asian countries, especially India.

Deep Learning-Driven Poultry Fecal Image Recognition and Disease Early Warning System Design

An intelligent poultry-disease early-warning system based on deep-learning-driven fecal image recognition that integrates edge computing, industrial imaging, and wireless sensing for real-time deployment in poultry houses is proposed.