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.
Abstract
The sustainable development of the poultry industry is constrained by frequent disease outbreaks and delayed clinical diagnosis, while conventional disease-control approaches based on manual experience are inadequate for precise management in large-scale farming. This study proposes an intelligent poultry-disease early-warning system based on deep-learning-driven fecal image recognition. Fecal morphological features are used as key indicators for early disease detection, and a dataset containing 10,548 valid samples across five categories—healthy, coccidiosis, Newcastle disease, infectious bursal disease, and salmonellosis—is constructed in collaboration with large-scale poultry farms in Guangdong Province. EfficientNet-B3 is adopted as the backbone network, and a Convolutional Block Attention Module (CBAM) and lightweight Lite-FPN multi-scale feature fusion structure are embedded to enhance fine-grained lesion recognition. The system integrates edge computing, industrial imaging, and wireless sensing for real-time deployment in poultry houses. Results show that warning accuracy reaches 91.6%, system availability reaches 99.94%, and the mortality-and-culling rate decreases by 22.6%, demonstrating the engineering feasibility and practical utility of the proposed system.
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.
S. K, D. S, Deeksha V Panchal et al.· 2026 International Conferenc...· 0 citations
Poultry is a major source of food, and growing demand for animal-based products has driven agricultural industries to increase production. However, this expansion has also led to a significant rise in the spread of infectious diseases. Several limitations faced by conventional methods include limited visual indicators, inability to capture complex interactions and limited integration of modern technology. The advancement of modern technology in the poultry industry helps to monitor and track the health of poultry chickens. The early detection of poultry diseases is essential for sustainable poultry farming, reducing poultry losses, and preventing the spread of zoonotic diseases to humans. In this work, a SpinalNet Fusion Recurrent Neural Network (SPFRNN) model is proposed for poultry disease classification based on Deep Learning (DL). The main objective of the proposed research is to design an enhanced deep learning-based poultry disease detection system that enables early and accurate classification of diseases, thereby reducing mortality rates, minimizing economic losses, and preventing the spread of infections. At first, the Internet of Things (IoT) is simulated, and images are collected from IoT nodes. Then, routing is performed at Base Station (BS) utilizing Proposed Jellyfish Search Honey Badger Optimization (JSHBO), whereas the optimal path is predicted by routing based on fitness parameters, such as energy, distance and delay. At BS, poultry disease is detected and classified. Initially, the input image is sent for pre-processing, which is performed by Anisotropic Filtering. Then, the disease area is segmented by Psi-Net, which is followed by Image augmentation. Moreover, suitable features, like Speeded Up Robust Features (SURF), Local binary pattern (CLBP), and Feature Local binary pattern (FLBP), along with statistical features are extracted in the feature extraction stage. Finally, poultry disease is detected by implementing a devised SPFRNN that integrates SpinalNet and Recurrent Neural Network (RNN). The proposed framework performs multi-level classification, where the first level identifies whether the poultry sample is healthy or diseased, and the second level classifies the detected diseased samples into specific disease categories, namely Coccidiosis, Salmonella, and Newcastle disease. Comparative evaluation demonstrates that the proposed SPFRNN model achieves superior performance, exhibiting improvements of 11.476%, 9.468%, 6.585%, 6.384%, 5.205%, 3.683%, 3.066%, and 2.513% over existing state-of-the-art techniques. The obtained specificity, sensitivity, and accuracy are 0.929, 0.924, and 0.920, respectively, confirming the effectiveness of the proposed approach for accurate poultry disease classification.
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.
Syed Irtiza Ali Shah, Shams ur Rahman, Khalid Khan et al.· International Journal of Inn...· 0 citations
Cotton is one of the most economically significant cash crops in the world, contributing approximately 70,000 crore annually to India’s agricultural economy and supporting roughly 15 million farming households. Yet, undetected leaf diseases and nutrient deficiencies cause 20-40% annual yield losses, and smallholder farmers in rural areas rarely have timely access to agronomic expertise. This paper presents CottonCare AI, an EfficientNet-B4-based cotton leaf disease detection and treatment support system designed for smart and precision farming. The system introduces a dual-module deep learning framework: a primary Disease Detection Module that classifies six conditions-Aphids, Army Worm, Bacterial Blight, Powdery Mildew, Target Spot, and Healthy Leaf-with 96.3% test accuracy and a macro-averaged F1-score of 96.0%, trained on 4,200 images per class acquired across RGB, grayscale, and alternative color-space representations; and a complementary Nutrient Deficiency Module that separately identifies four macro-nutrient deficiencies-Nitrogen, Potassium, Magnesium, and Iron-through priority-ordered HSV color-space analysis. A dedicated severity estimation module quantifies the fraction of leaf area affected and categorizes disease progression as Mild, Moderate, or Severe, achieving 89% agreement (Cohen’s $\kappa=0.83$) with expert-annotated ground truth. Explainable AI (XAI) techniques-Grad-CAM and SHAP-are integrated to generate spatial heatmaps and pixel-level attributions, making every prediction transparent and auditable by farmers and extension officers alike. A hybrid treatment recommendation engine provides disease-specific chemical and organic management options, including neem oil, bio-fungicides, and soil amendments, promoting environmentally responsible farming. The full-stack system is deployed as a React.js web dashboard, a Flutter mobile application, and a FastAPI backend orchestrated with Docker and Kubernetes, achieving sub-3-second inference latency. Comparative experiments demonstrate consistent outperformance of VGG16 (87.2%), ResNet50 (90.4%), DenseNet121 (91.1%), InceptionV3 (89.7%), and EfficientNet-B3 (94.1%) under identical experimental conditions.
N. Uday, Vijaykumar, Vishwanath Angadi et al.· International Conference on...· 0 citations
Experimental data show that the proposed EfficientNetB0V2 + CNN model achieves superior performance, with higher accuracy and better precision, recall and F1 Score across all classes, highlighting the effectiveness of the suggested approach in detecting complex disease patterns.
Nisha Rani· International journal of com...· 0 citations
Agriculture is a key sector in developing economies, but crop diseases significantly impact productivity, food security, and farmers’ livelihoods. Early detection is crucial to minimize losses, yet traditional methods are slow, error-prone, and depend heavily on human expertise. Recent advancements in Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled more efficient automated crop disease detection. This study reviews pre-2018 AI-based approaches, focusing on techniques such as image processing, feature extraction, and classification methods. It highlights models like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid systems combining traditional and modern techniques. The proposed approach includes preprocessing, segmentation, color transformation, and extraction of texture, color, and shape features, followed by supervised learning for classification. AI systems can detect subtle disease symptoms early, achieving over 90% accuracy under controlled conditions. Integration with mobile and IoT technologies enables real-time monitoring and decision support for farmers. However, challenges such as limited datasets, environmental variability, and computational constraints remain. Future work should focus on developing scalable, robust, and field-deployable solutions for diverse agricultural conditions.
J. Rogers, Brandon Truaxe· International Journal of Mod...· 0 citations
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