养殖业牲畜个体识别与监控数据集

养殖业牲畜个体识别与监控数据集运用牛只信息识别、个体识别及猪只体长三类算法，其中牛只信息识别算法输出牛只追溯码、养殖数量、种类、花纹类别、预估体重及入侵识别结果，分别实现 “一牛一码” 全程追溯以规避传统打耳标风险，为养殖场提供精准存栏数据支撑，助力智慧养殖管理与保险承保理赔核验，为活体贷款融资提供价值评估依据，同时保障养殖场安全；个体识别算法输出猪、牛、羊等动物类别及个体数量，解决传统动物识别类别混淆问题，支撑多品类养殖管理与保险理赔定损；猪只体长算法输出猪只体长、体重等精准数据，规避人工测量误差与动物应激反应，为猪只养殖精细化管理及保险承保理赔提供科学依据，整体服务于农业信贷贷后监控与风险控制。The Livestock Individual Identification and Monitoring Dataset for Animal Husbandry incorporates three core algorithms: cattle information recognition, multi-species individual identification, and pig body length measurement. The cattle information recognition algorithm outputs cattle traceability codes, breeding population size, breed types, coat pattern categories, estimated body weight, and intrusion detection results. It enables full-process traceability with the "one cow, one code" mechanism to eliminate risks associated with traditional ear tagging practices. This dataset provides accurate on-hand livestock inventory data for farms, supports intelligent breeding management, insurance underwriting and claim verification, offers value assessment basis for living livestock-backed loan financing, and enhances farm safety. The multi-species individual identification algorithm outputs animal categories including pigs, cattle, sheep and individual counts, addressing the category confusion issue prevalent in traditional animal identification methods, and facilitates multi-species breeding management and insurance claim loss assessment. The pig body length measurement algorithm outputs precise data such as pig body length and body weight, avoiding manual measurement errors and animal stress reactions, and provides scientific support for refined pig breeding management as well as insurance underwriting and claim handling. Overall, this dataset serves agricultural credit post-loan monitoring and risk control.

搜集汇总

背景与挑战

背景概述

该数据集专注于养殖业牲畜的个体识别与监控，通过牛只信息识别、个体识别及猪只体长三类算法，实现牲畜的精准追溯、存栏管理和数据采集。它支持“一牛一码”全程追溯，解决传统动物识别混淆问题，并提供猪只体长体重等科学数据，整体服务于智慧养殖管理、保险承保理赔以及农业信贷的风险控制。