Plant Growth Stage Detection | 植物生长阶段自动判定

通过图像或视频识别植物生长阶段，输出结构化分析报告，为农事管理提供数据依据。

给一张植物照片，输出 0–100% 的萎蔫指数，并结合土壤湿度判断是缺水还是水涝。

AI-powered orchid growth-status detection from HD images (including roots visible through transparent pots) via orchid cameras or smartphones. Measures new-shoot count, flower-spike length, and root color/condition (white = healthy, brown = aged, black = rotten) to deliver a holistic vitality assessment (vigorous / normal / weak) plus care guidance such as 'three new shoots, healthy roots, increase phosphorus-potassium to promote spike growth'. Helps orchid hobbyists pinpoint repotting and feeding timing. Scenarios: home orchid care, orchid greenhouses, horticulture studios. | 通过兰花栽培专用摄像头或手机拍摄的高清图像（包括透明兰盆内的根系），利用AI视觉分析技术检测兰花新芽萌发数量、花梗（花箭）生长长度以及根系颜色（白色健康、褐色老化、黑色腐烂），综合输出兰花的生长状态评估（旺盛/正常/衰弱）及养护建议（如"新芽萌发3个，根系健康，可适当增加磷钾肥促进花梗生长"）。有助于兰花爱好者精准掌握植株生长节奏，及时调整水肥管理。应用场景：兰花家庭养护、兰花大棚、兰花园艺工作室。

AI-powered non-invasive rooting-stage detection for plant cuttings in transparent containers. From smart propagation boxes or transparent cutting containers, periodically captures images of the cutting base (water roots or substrate roots visible through the transparent wall), uses AI vision to detect white root primordia (tiny white callus dots) and count/locate them, and outputs the rooting stage (No Roots / Few Primordia / Many Primordia / Well-Developed Roots). Helps propagators monitor cutting-rooting progress without disturbing the cutting and choose the optimal transplant time. Scenarios: smart propagation boxes, home cutting propagation, tissue-culture rooms, agricultural research. | 通过智能育苗箱或透明扦插容器的固定摄像头，定期拍摄枝条基部的水生根或土培根系（通过透明壁观察），利用AI视觉分析技术识别枝条基部是否出现白色凸起根点（根原基发育形成的白色小点）以及根点的数量和分布，输出生根阶段（无生根/少量根点/大量根点/根系发达）。该技能帮助育苗者在不干扰枝条的情况下，无创监测扦插生根进度，判断移栽时机。应用场景：智能育苗箱、家庭扦插繁殖、植物组培室、农业科研。

AI-powered plant nutrient deficiency diagnosis from leaf images. Detects leaf color, morphology changes (pale green/yellow-green/purple-red, marginal scorch, interveinal chlorosis) via computer vision, matches against common deficiency symptom databases, and outputs the most likely deficient nutrient element (nitrogen, phosphorus, potassium, iron, magnesium, zinc, etc.) with confidence score. Enables precision fertilization, avoids blind over-fertilization. Scenarios: smart planters, home gardening, agricultural greenhouses, plant factories. | 通过智能花盆、农业大棚或手机拍摄的植物叶片高清图像，利用AI视觉分析技术识别叶片颜色、形态变化（如叶色浅绿/黄绿/紫红、叶缘焦枯、叶脉间失绿等），与常见营养缺乏症特征库比对，输出最可能缺乏的营养元素（氮、磷、钾、铁、镁、锌等）及置信度。有助于精准施肥，避免盲目用肥造成浪费或伤害。应用场景：智能花盆、家庭园艺、农业大棚、植物工厂。

Using fixed cameras in a smart greenhouse to analyze plant morphology in real time (e.g., leaf wilting angle, stem uprightness, leaf color changes) combined with environmental sensors (light intensity, temperature, humidity, soil moisture), an AI decision model outputs climate control commands including irrigation (pump/solenoid valve), shade-net opening, fan/wet-curtain on-off, heater on-off, etc. | 通过智能温室中的固定摄像头实时分析植物的形态（如叶片萎蔫角度、茎秆挺直度、叶色变化）以及结合环境传感器（光照强度、温度、湿度、土壤水分），利用AI决策模型输出环境调控指令，包括灌溉（水泵/电磁阀）、遮阳网开度、风机/湿帘启停、加热器开关等。