[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126647-en":3,"doc-seo-126647-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126647,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification","This study proposes a computer-vision and supervised machine-learning pipeline to classify gender and breed in Indian native chicken production with minimal human intervention. Eleven chicken breeds are modeled using 17,600 training and 4,400 testing samples in an 80:20 split. Gray-level co-occurrence matrix (GLCM) features are extracted, while principal component analysis (PCA) supports feature selection. Among 27 classifiers, FG-SVM, F-KNN, and W-KNN exceed 90% accuracy, and the BT classifier delivers 99.3% accuracy with high precision, sensitivity, F-scores, and a mean absolute error of 0.7.","An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification  \nThavamani Subramani*, Vijayakumar Jeganathan, Sruthi Kunkuma Balasubramanian  \nDepartment of Electronics and Instrumentation, Bharathiar University, Coimbatore, India  \nReceived 04 April 2023; received in revised form 07 April 2023; accepted 11 April 2023  \nDOI: [https://doi.org/10.46604/peti.2023.11361](https://doi.org/10.46604/peti.2023.11361)  \nAbstract  \nThis study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio) . The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1% . The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7.  \nKeywords: native chicken breed classification, gender classification, machine learning algorithms, GLCM, PCA  \n1. Introduction  \nGender and breed predictions for native chickens are important tasks in large-scale poultry production, which require human intervention. Chicken gender identification plays a major role in chicken ratio management, and breed identification is essential in large-scale production to avoid farm-level cross-breeding. With the advancement of machine learning (ML) approaches, ML can be implemented in poultry farms to perform identification activities. This section discusses the types and importance of native chickens, the classification of gender and breed, and the feasible approaches for poultry management.  \n1.1. Importance of native chicken production  \nNative chickens have good survival capabilities, and they obtain good yields without any special diet. The native breed can survive with kitchen wastes, insects, and greens [1] . Because of good survivability, harsh environmental adoption, and simple housing facility, rural poultry farmers prefer native breeds’ meat and egg. The main motive for native poultry farming is the high monetary value of their meat and eggs. The Tamil Nadu (TN) government also encourages open-source poultry farming with native chicken breeds to maximize income and provide nutritious food to underdeveloped villages. The medium-scale native chicken production is also a key to empowering women and unemployed youths in rural regions ofTN [2] .  \nLess production creates demand for native chicken meat and egg in the markets. Therefore, native chicken meat and egg are more expensive than other poultry meat and egg to date. Owing to the benefits for health, consumers are willing to spend more on native breeds of India [3]. Large-scale native-breed poultry farming was introduced to meet the growing demand. In large-scale production houses, the breed selection is based on individual characteristics, including growth period, laying, and  \n* Corresponding author. E-mail address: [thavamani.ei@buc.edu.in](thavamani.ei@buc.edu.in)  \nhatching performances. Native Chicken breeds have the advantages of well environmental adaptability, low financial input, suboptimal rearing conditions, abundant protein nutrients in meat and eggs, and genetic diversity. Moreover, native breed hens are good sitters and have good hatching capability and broodiness. [4-5] .  \n1.2. Types of native chicken breeds  \nThe chicken breed is defined according to individual characteristics and features. In India, nineteen varieties of native chicken breeds are registered","cbCaimsfVtEh6cyq","https://ap.wps.com/l/cbCaimsfVtEh6cyq","pdf",1425501,1,14,"English","en",105,"# Introduction\n## Importance of native chicken production\n## Types of native chicken breeds\n## Difficulties of identify the chicken gender and breed\n## Different ML approaches for poultry industry management","[{\"question\":\"What does the proposed approach aim to classify in native chickens?\",\"answer\":\"It aims to classify both the gender and the breed of Indian native chickens for production use.\"},{\"question\":\"How are features extracted and selected in the study?\",\"answer\":\"GLCM is used for feature extraction, and PCA is applied for feature selection.\"},{\"question\":\"Which classifiers achieved the best performance according to the results?\",\"answer\":\"FG-SVM, F-KNN, and W-KNN surpass 90% accuracy, while the BT classifier achieves very high metrics for gender and breed classification including 99.3% accuracy and a mean absolute error of 0.7.\"}]","An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification | 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does the proposed approach aim to classify in native chickens?","Question",{"text":75,"@type":76},"It aims to classify both the gender and the breed of Indian native chickens for production use.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are features extracted and selected in the study?",{"text":80,"@type":76},"GLCM is used for feature extraction, and PCA is applied for feature selection.",{"name":82,"@type":73,"acceptedAnswer":83},"Which classifiers achieved the best performance according to the results?",{"text":84,"@type":76},"FG-SVM, F-KNN, and W-KNN surpass 90% accuracy, while the BT classifier achieves very high metrics for gender and breed classification including 99.3% accuracy and a mean absolute error of 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