Jul 2026· 2026 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)· pp. 1-6· 0 citations· 38 references
Abstract
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.
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.
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
Poultry farming remains a major contributor to every nation’s economy, yet it is faced with many challenges, especially during disease outbreaks. In developing nations, the majority of poultry farmers lack formal knowledge in the field and access to extension agents. This article proposes the development of an AI-driven mobile application for poultry disease diagnosis and decision support that leverages a pre-trained transformer model. A dual architectural framework that integrates a Convolutional Neural Network-based image classifier and a transformer-based model was used for image-based disease detection and expert-level diagnosis and treatment recommendations, respectively. The application was developed using object-oriented analysis and design methodology. The results of the framework show that the best image-based classifier achieved 84.53% accuracy in classifying the listed poultry diseases. The transformer models also presented average precision accuracy (PA) of 95%, professional rating (PR) of 95%, a coherence rating (CR) of 97%, and a task success rate (TSR) of 96% based on the aggregation of independent assessments from 3 domain experts. These results prove that a mobile application can be engineered to democratize the possibilities associated with engaging machine learning (ML) in poultry farming since existing AI tools were not designed for illiterate farmers. Nevertheless, human veterinary professionals remain indispensable, especially in context-specific cases where clinical examination and consideration of birds’ health history are required to complement AI-driven insights.
Azubuike I. Erike, Augustine Azubogu, Charles Ikerionwu et al.· African Journal of Advances...· 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.
The implications of this study suggest that CNN-based transfer learning can be effectively applied as an early disease detection tool in poultry farming, especially for real-time and resource-limited applications.
Dwita Avrilly, Y. Putranto· Proxies: Jurnal Informatika· 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
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