从基因组到田间，无尽用机器学习预测性状、优选亲本，再通过多点试种网络验证品种，让好品种更快走出实验室、走进新的产区。 From genome to field, Wujin uses machine learning to predict traits and optimize crosses, then validates varieties through a multi-site trial network, moving better seeds out of the lab and into new regions, faster.
把育种家的经验和海量数据结合起来：在播种之前筛选，在田间自动测量，在杂交之前模拟，在推广之前验证。We pair breeders' expertise with data at scale: screen before sowing, measure automatically in the field, simulate before crossing, validate before release.
基于全基因组标记，用深度学习预测育种值，在播种之前完成初筛。Predict breeding values from genome-wide markers with deep learning, screening candidates before a single seed is sown.
无人机与图像识别自动测量株高、穗型、粒形等性状，替代人工记载。Drone imaging and computer vision measure plant height, panicle architecture and grain shape, replacing manual scoring.
模拟海量杂交组合，推荐兼顾遗传增益与多样性的亲本配对。Simulate vast numbers of crosses and recommend parent pairs that balance genetic gain with diversity.
联合多地试验田验证候选品种，田间数据回流模型，持续校准预测。Validate candidates across partner trial fields; field data flows back to keep the models calibrated.
调控粒形、产量、株型与品质的关键基因分布在整个基因组上。AI 模型同时学习成千上万个标记位点的微小效应，在播种之前估计一个品系的潜力。Genes shaping grain size, yield, plant architecture and quality are spread across the genome. Our models learn the small effects of thousands of markers at once to estimate a line's potential before it is planted.
每一季田间数据都回到模型里，下一轮预测建立在更多真实表现之上。Every season's field data returns to the models, so each new round of predictions rests on more real-world performance.
获取候选材料的全基因组标记Genome-wide markers for every candidate
关联基因型与历史表型Link genotypes to historical phenotypes
在播种前给每个品系打分Score every line before sowing
推荐最优杂交组合Recommend the best crosses
多地试种，真实表现说话Let real performance decide
新数据校准下一轮模型New data recalibrates the next round
基因数据在实验室，表型数据散在各地的试验田里，两头长期割裂。我们用试种网络把大田表型、环境与农事数据按标准、可核验地收回来，和基因数据打通。Genomic data sits in the lab; phenotype data is scattered across trial fields. Our trial network brings field phenotype, environment and management data back to one standard, verified, and links it to the genome.
定好要记的性状、拍照时点和测产方法Traits, photo timing and yield method
在小程序里逐项打卡、拍照、上传Step-by-step check-ins and photos in the mini program
核验通过才计酬，数据可追溯Paid only after verification; every record traceable
多点表现校准下一轮预测Multi-site results recalibrate the next round
多个试验点同步推进，育种家把时间用在判断和组配上，而不是长期蹲守基地。Many sites advance in parallel, so breeders spend their time on decisions and crosses rather than camping at trial bases.
配合度高、有销售能力的试种户和新农人，可以成为当地的品种推广伙伴，按销量分成。Reliable growers and young farmers with sales ability can become local promotion partners, paid by a share of sales.
先从数据积累更充分的大田作物做起，再把模型扩展到蔬菜，补齐蔬菜的基因与表型数据。We start with field crops, where data is richer, then extend the models to vegetables while building up their genomic and phenotype data.
北京无尽科技有限公司是一家 AI 驱动的种业成果转化平台。我们与育种科研机构和种业企业合作，用 AI 加速品种筛选，通过多点试种网络验证表现，再由各地伙伴连接种植者与市场，让好品种真正产生价值。Wujin is an AI-driven platform for turning breeding research into varieties that reach farmers. We work with breeding institutes and seed companies, use AI to accelerate selection, validate performance through a multi-site trial network, and connect proven varieties with growers and markets through local partners.
为育种团队提供 AI 育种系统，共享模型能力与数据成果。AI breeding systems for research teams, with shared models and results.
在新区域做多点标准化试种，用数据证明品种表现，再决定在哪里推广。Standardized multi-site trials in new regions that prove performance with data before you decide where to launch.
按标准试种、上传数据获得报酬，优秀者成为当地的品种推广伙伴。Get paid for standardized trials and data; the best become local promotion partners.
科研合作、育种系统部署、多点试种、品种转化，欢迎来信。Research collaboration, breeding system deployment, multi-site trials, variety licensing: we'd love to hear from you.
wujinseed@163.com