9cb70267b6
基于多因子 Cobb-Douglas 产量模型的作物种植决策支持应用。
新增文件：
- app.py: Streamlit 主应用，包含产量预测模型、多作物数据库、
雷达图/敏感性分析可视化、作物推荐排行及智能建议面板
- main.py: 入口文件
- pyproject.toml: 项目配置（Python 3.14+，依赖 streamlit/plotly/pandas/numpy）
- Dockerfile: 基于 uv 镜像的容器化部署配置
- justfile: 任务自动化（运行/格式化/检查/清理）
- .gitignore: Python/IDE/缓存忽略规则
588 lines
23 KiB
Python
588 lines
23 KiB
Python
"""
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农业智能决策系统
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基于多因子 Cobb-Douglas 产量模型的作物种植决策支持应用
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"""
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import streamlit as st
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import numpy as np
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import pandas as pd
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import plotly.graph_objects as go
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import plotly.express as px
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from plotly.subplots import make_subplots
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# ─── Page Config ────────────────────────────────────────────────────────────
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st.set_page_config(
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page_title="农业智能决策系统",
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page_icon="🌾",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# ─── Custom CSS ──────────────────────────────────────────────────────────────
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st.markdown("""
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Noto+Serif+SC:wght@400;600;700&family=JetBrains+Mono:wght@400;500&display=swap');
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:root {
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--bg-dark: #0a1628;
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--bg-card: #0f2040;
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--accent-green: #4ade80;
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--accent-gold: #f59e0b;
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--accent-blue: #38bdf8;
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--text-primary: #e2e8f0;
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--text-muted: #64748b;
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--border: rgba(74, 222, 128, 0.2);
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}
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html, body, [class*="css"] {
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font-family: 'Noto Serif SC', serif;
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background-color: var(--bg-dark);
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color: var(--text-primary);
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}
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.stApp {
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background: linear-gradient(135deg, #0a1628 0%, #0d1f3c 50%, #091520 100%);
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}
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/* Sidebar */
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[data-testid="stSidebar"] {
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background: linear-gradient(180deg, #0f2040 0%, #0a1628 100%);
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border-right: 1px solid var(--border);
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}
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[data-testid="stSidebar"] .stSlider label,
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[data-testid="stSidebar"] .stNumberInput label,
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[data-testid="stSidebar"] .stSelectbox label {
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color: var(--accent-green) !important;
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font-size: 0.82rem;
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font-family: 'JetBrains Mono', monospace;
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letter-spacing: 0.05em;
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}
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/* Metric cards */
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.metric-card {
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background: linear-gradient(135deg, #0f2040, #132b55);
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border: 1px solid var(--border);
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border-radius: 12px;
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padding: 20px 24px;
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text-align: center;
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position: relative;
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overflow: hidden;
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}
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.metric-card::before {
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content: '';
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position: absolute;
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top: 0; left: 0; right: 0;
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height: 2px;
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background: linear-gradient(90deg, var(--accent-green), var(--accent-blue));
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}
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.metric-value {
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font-family: 'JetBrains Mono', monospace;
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font-size: 2.2rem;
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font-weight: 700;
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color: var(--accent-green);
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line-height: 1;
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}
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.metric-unit {
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font-size: 0.85rem;
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color: var(--text-muted);
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margin-top: 4px;
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}
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.metric-label {
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font-size: 0.9rem;
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color: var(--text-primary);
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margin-top: 8px;
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}
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/* Section headers */
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.section-header {
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font-size: 0.75rem;
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font-family: 'JetBrains Mono', monospace;
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letter-spacing: 0.15em;
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color: var(--accent-gold);
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text-transform: uppercase;
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border-bottom: 1px solid rgba(245,158,11,0.3);
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padding-bottom: 6px;
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margin-bottom: 16px;
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margin-top: 24px;
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}
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/* Crop badge */
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.crop-badge {
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display: inline-block;
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background: linear-gradient(135deg, rgba(74,222,128,0.15), rgba(56,189,248,0.15));
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border: 1px solid var(--accent-green);
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border-radius: 6px;
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padding: 4px 12px;
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font-family: 'JetBrains Mono', monospace;
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font-size: 0.78rem;
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color: var(--accent-green);
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margin: 3px;
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}
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/* Recommendation card */
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.rec-card {
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background: linear-gradient(135deg, rgba(74,222,128,0.08), rgba(56,189,248,0.05));
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border: 1px solid rgba(74,222,128,0.3);
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border-radius: 12px;
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padding: 18px 22px;
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margin: 10px 0;
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}
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.rec-rank {
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font-family: 'JetBrains Mono', monospace;
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font-size: 1.5rem;
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color: var(--accent-gold);
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font-weight: 700;
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}
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.rec-crop {
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font-size: 1.1rem;
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color: var(--text-primary);
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font-weight: 600;
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}
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.rec-score {
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font-family: 'JetBrains Mono', monospace;
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font-size: 0.85rem;
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color: var(--accent-blue);
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}
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/* Hero title */
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.hero-title {
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font-size: 2rem;
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font-weight: 700;
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background: linear-gradient(135deg, var(--accent-green), var(--accent-blue));
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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background-clip: text;
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line-height: 1.2;
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}
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.hero-sub {
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font-family: 'JetBrains Mono', monospace;
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font-size: 0.8rem;
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color: var(--text-muted);
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letter-spacing: 0.1em;
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margin-top: 4px;
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}
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/* Alert boxes */
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.alert-good {
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background: rgba(74,222,128,0.1);
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border-left: 3px solid var(--accent-green);
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border-radius: 0 8px 8px 0;
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padding: 12px 16px;
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margin: 8px 0;
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font-size: 0.9rem;
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}
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.alert-warn {
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background: rgba(245,158,11,0.1);
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border-left: 3px solid var(--accent-gold);
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border-radius: 0 8px 8px 0;
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padding: 12px 16px;
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margin: 8px 0;
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font-size: 0.9rem;
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}
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/* Override streamlit slider colors */
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.stSlider [data-baseweb="slider"] [data-testid="stTickBarMin"],
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.stSlider [data-baseweb="slider"] [data-testid="stTickBarMax"] {
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color: var(--text-muted);
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}
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</style>
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""", unsafe_allow_html=True)
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# ─── Crop Database ───────────────────────────────────────────────────────────
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CROPS = {
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"水稻": {
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"emoji": "🌾",
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"optimal": {"ph": (6.0, 7.0), "N": (80, 120), "P": (30, 60), "K": (40, 80),
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"rainfall": (150, 250), "temp": (22, 30)},
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"base_yield": 7500, # kg/ha
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"color": "#4ade80"
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},
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"小麦": {
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"emoji": "🌿",
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"optimal": {"ph": (6.0, 7.5), "N": (60, 100), "P": (20, 50), "K": (30, 60),
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"rainfall": (60, 120), "temp": (15, 22)},
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"base_yield": 6000,
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"color": "#f59e0b"
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},
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"玉米": {
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"emoji": "🌽",
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"optimal": {"ph": (5.8, 7.0), "N": (100, 150), "P": (40, 70), "K": (60, 100),
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"rainfall": (100, 180), "temp": (20, 28)},
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"base_yield": 8500,
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"color": "#fbbf24"
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},
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"大豆": {
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"emoji": "🫘",
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"optimal": {"ph": (6.0, 7.0), "N": (20, 50), "P": (30, 60), "K": (40, 80),
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"rainfall": (80, 150), "temp": (18, 26)},
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"base_yield": 3500,
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"color": "#a78bfa"
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},
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"油菜": {
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"emoji": "🌻",
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"optimal": {"ph": (6.0, 7.5), "N": (80, 130), "P": (30, 60), "K": (50, 90),
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"rainfall": (80, 130), "temp": (15, 20)},
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"base_yield": 3000,
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"color": "#f97316"
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},
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"棉花": {
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"emoji": "☁️",
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"optimal": {"ph": (6.0, 8.0), "N": (60, 100), "P": (20, 45), "K": (40, 70),
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"rainfall": (70, 120), "temp": (25, 32)},
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"base_yield": 4500,
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"color": "#e2e8f0"
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},
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}
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# ─── Yield Model ─────────────────────────────────────────────────────────────
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def compute_factor(value, optimal_low, optimal_high, penalty=0.5):
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"""Score 0-1: 1 if in optimal range, decays outside."""
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mid = (optimal_low + optimal_high) / 2
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width = (optimal_high - optimal_low) / 2 + 1e-9
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if optimal_low <= value <= optimal_high:
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return 1.0
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dist = min(abs(value - optimal_low), abs(value - optimal_high))
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return max(0.0, 1.0 - penalty * (dist / width))
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def predict_yield(crop_name, ph, N, P, K, rainfall, temp, pesticide, area):
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crop = CROPS[crop_name]
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opt = crop["optimal"]
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f_ph = compute_factor(ph, *opt["ph"], penalty=0.6)
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f_N = compute_factor(N, *opt["N"], penalty=0.4)
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f_P = compute_factor(P, *opt["P"], penalty=0.4)
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f_K = compute_factor(K, *opt["K"], penalty=0.4)
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f_rain = compute_factor(rainfall, *opt["rainfall"], penalty=0.5)
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f_temp = compute_factor(temp, *opt["temp"], penalty=0.7)
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f_pest = 0.5 + 0.5 * min(pesticide / 100, 1.0)
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# Cobb-Douglas style yield function
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nutrient_idx = (f_N * f_P * f_K) ** (1/3)
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soil_idx = f_ph
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climate_idx = (f_rain * f_temp) ** 0.5
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total_factor = soil_idx ** 0.2 * nutrient_idx ** 0.4 * climate_idx ** 0.3 * f_pest ** 0.1
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yield_per_ha = crop["base_yield"] * total_factor
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total_yield = yield_per_ha * area
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factors = {
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"土壤pH": f_ph, "氮(N)": f_N, "磷(P)": f_P,
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"钾(K)": f_K, "降雨量": f_rain, "温度": f_temp, "农药": f_pest
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}
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return yield_per_ha, total_yield, factors
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def rank_crops(ph, N, P, K, rainfall, temp, pesticide, area):
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results = []
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for name in CROPS:
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yph, ytotal, factors = predict_yield(name, ph, N, P, K, rainfall, temp, pesticide, area)
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score = np.mean(list(factors.values()))
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results.append({
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"crop": name,
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"emoji": CROPS[name]["emoji"],
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"yield_ha": yph,
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"total_yield": ytotal,
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"score": score,
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"color": CROPS[name]["color"],
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"factors": factors
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})
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results.sort(key=lambda x: x["score"], reverse=True)
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return results
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# ─── Sidebar Inputs ──────────────────────────────────────────────────────────
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with st.sidebar:
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st.markdown('<div class="hero-title">🌾 农业决策</div>', unsafe_allow_html=True)
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st.markdown('<div class="hero-sub">SMART FARMING SYSTEM v2.0</div>', unsafe_allow_html=True)
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st.markdown("---")
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st.markdown('<div class="section-header">🧪 土壤参数</div>', unsafe_allow_html=True)
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col1, col2 = st.columns(2)
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with col1:
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ph = st.slider("pH 值", 4.0, 9.0, 6.5, 0.1)
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N = st.slider("氮 N (mg/kg)", 0, 200, 90, 5)
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with col2:
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P = st.slider("磷 P (mg/kg)", 0, 100, 45, 5)
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K = st.slider("钾 K (mg/kg)", 0, 150, 60, 5)
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st.markdown('<div class="section-header">🌦 气象数据</div>', unsafe_allow_html=True)
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col3, col4 = st.columns(2)
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with col3:
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rainfall = st.slider("降雨量 (mm/月)", 0, 400, 120, 10)
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with col4:
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temp = st.slider("温度 (°C)", 0, 45, 22, 1)
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st.markdown('<div class="section-header">🌱 种植参数</div>', unsafe_allow_html=True)
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area = st.number_input("种植面积 (公顷)", 0.1, 10000.0, 100.0, 10.0)
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pesticide = st.slider("农药用量 (kg/ha)", 0, 200, 50, 5)
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st.markdown('<div class="section-header">🎯 目标作物</div>', unsafe_allow_html=True)
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selected_crop = st.selectbox(
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"选择分析作物",
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list(CROPS.keys()),
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format_func=lambda x: f"{CROPS[x]['emoji']} {x}"
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)
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# ─── Compute ──────────────────────────────────────────────────────────────────
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yph, ytotal, factors = predict_yield(selected_crop, ph, N, P, K, rainfall, temp, pesticide, area)
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rankings = rank_crops(ph, N, P, K, rainfall, temp, pesticide, area)
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best_crop = rankings[0]
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# ─── Main Layout ─────────────────────────────────────────────────────────────
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st.markdown(f"""
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<div style="display:flex; align-items:baseline; gap:16px; margin-bottom:4px;">
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<div class="hero-title">农业智能决策系统</div>
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</div>
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<div class="hero-sub">YIELD = f(SOIL · WEATHER · PESTICIDE) | 基于多因子 Cobb-Douglas 产量模型</div>
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""", unsafe_allow_html=True)
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st.markdown("<br>", unsafe_allow_html=True)
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# KPI row
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k1, k2, k3, k4 = st.columns(4)
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with k1:
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-value">{yph:,.0f}</div>
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<div class="metric-unit">kg / 公顷</div>
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<div class="metric-label">{CROPS[selected_crop]['emoji']} {selected_crop} 单产</div>
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</div>""", unsafe_allow_html=True)
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with k2:
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-value">{ytotal/1000:,.1f}</div>
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<div class="metric-unit">吨 / 总产量</div>
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<div class="metric-label">📦 {area:.0f} 公顷总产</div>
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</div>""", unsafe_allow_html=True)
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with k3:
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overall = np.mean(list(factors.values()))
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-value">{overall*100:.1f}%</div>
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<div class="metric-unit">综合适宜度</div>
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<div class="metric-label">🎯 环境匹配指数</div>
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</div>""", unsafe_allow_html=True)
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with k4:
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-value">{best_crop['emoji']}</div>
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<div class="metric-unit">{best_crop['crop']} ({best_crop['score']*100:.0f}%)</div>
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<div class="metric-label">🏆 最优推荐作物</div>
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</div>""", unsafe_allow_html=True)
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st.markdown("<br>", unsafe_allow_html=True)
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# ─── Charts Row ──────────────────────────────────────────────────────────────
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col_left, col_right = st.columns([3, 2])
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with col_left:
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st.markdown('<div class="section-header">📊 影响因子雷达图</div>', unsafe_allow_html=True)
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factor_names = list(factors.keys())
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factor_vals = [round(v * 100, 1) for v in factors.values()]
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factor_names_closed = factor_names + [factor_names[0]]
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factor_vals_closed = factor_vals + [factor_vals[0]]
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fig_radar = go.Figure()
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fig_radar.add_trace(go.Scatterpolar(
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r=factor_vals_closed,
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theta=factor_names_closed,
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fill='toself',
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fillcolor='rgba(74,222,128,0.15)',
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line=dict(color='#4ade80', width=2),
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name=selected_crop,
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))
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fig_radar.add_trace(go.Scatterpolar(
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r=[100]*len(factor_names_closed),
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theta=factor_names_closed,
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line=dict(color='rgba(255,255,255,0.1)', width=1, dash='dot'),
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mode='lines',
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name='理想值',
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))
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fig_radar.update_layout(
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polar=dict(
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bgcolor='rgba(0,0,0,0)',
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radialaxis=dict(range=[0, 100], showticklabels=True,
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tickfont=dict(color='#64748b', size=9),
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gridcolor='rgba(255,255,255,0.06)'),
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angularaxis=dict(tickfont=dict(color='#e2e8f0', size=11),
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gridcolor='rgba(255,255,255,0.08)'),
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),
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paper_bgcolor='rgba(0,0,0,0)',
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plot_bgcolor='rgba(0,0,0,0)',
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font=dict(color='#e2e8f0'),
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legend=dict(orientation='h', y=-0.12, font=dict(size=10)),
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margin=dict(t=20, b=40, l=40, r=40),
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height=320,
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)
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st.plotly_chart(fig_radar, use_container_width=True)
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with col_right:
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st.markdown('<div class="section-header">🏅 作物推荐排行</div>', unsafe_allow_html=True)
|
||
for i, r in enumerate(rankings[:4]):
|
||
rank_icons = ["🥇", "🥈", "🥉", "4️⃣"]
|
||
bar_width = int(r['score'] * 100)
|
||
bar_color = r['color']
|
||
st.markdown(f"""
|
||
<div class="rec-card" style="margin-bottom:8px;">
|
||
<div style="display:flex; justify-content:space-between; align-items:center;">
|
||
<div>
|
||
<span class="rec-rank">{rank_icons[i]}</span>
|
||
<span class="rec-crop" style="margin-left:8px;">{r['emoji']} {r['crop']}</span>
|
||
</div>
|
||
<span class="rec-score">{r['score']*100:.1f}%</span>
|
||
</div>
|
||
<div style="background:rgba(255,255,255,0.06); border-radius:4px; height:4px; margin-top:8px; overflow:hidden;">
|
||
<div style="width:{bar_width}%; height:100%; background:{bar_color}; border-radius:4px;"></div>
|
||
</div>
|
||
<div style="font-size:0.78rem; color:#64748b; margin-top:4px; font-family:'JetBrains Mono',monospace;">
|
||
{r['yield_ha']:,.0f} kg/ha · 总产 {r['total_yield']/1000:,.1f} 吨
|
||
</div>
|
||
</div>""", unsafe_allow_html=True)
|
||
|
||
# ─── Sensitivity Analysis ─────────────────────────────────────────────────────
|
||
st.markdown('<div class="section-header">📈 产量敏感性分析</div>', unsafe_allow_html=True)
|
||
|
||
sa_col1, sa_col2 = st.columns(2)
|
||
|
||
with sa_col1:
|
||
N_range = np.linspace(0, 200, 60)
|
||
y_N = [predict_yield(selected_crop, ph, n, P, K, rainfall, temp, pesticide, 1)[0] for n in N_range]
|
||
|
||
fig_N = go.Figure()
|
||
fig_N.add_trace(go.Scatter(
|
||
x=N_range, y=y_N,
|
||
mode='lines', line=dict(color='#4ade80', width=2.5),
|
||
fill='tozeroy', fillcolor='rgba(74,222,128,0.08)',
|
||
name='产量'
|
||
))
|
||
fig_N.add_vline(x=N, line=dict(color='#f59e0b', width=1.5, dash='dot'),
|
||
annotation_text=f"当前 {N}", annotation_font_color='#f59e0b')
|
||
fig_N.update_layout(
|
||
title=dict(text="氮肥用量 vs 产量", font=dict(color='#94a3b8', size=12)),
|
||
xaxis=dict(title="氮 N (mg/kg)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
|
||
yaxis=dict(title="产量 (kg/ha)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
|
||
paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
|
||
font=dict(color='#e2e8f0', size=10),
|
||
margin=dict(t=36, b=36, l=50, r=20), height=220,
|
||
showlegend=False,
|
||
)
|
||
st.plotly_chart(fig_N, use_container_width=True)
|
||
|
||
with sa_col2:
|
||
rain_range = np.linspace(0, 400, 60)
|
||
y_rain = [predict_yield(selected_crop, ph, N, P, K, r, temp, pesticide, 1)[0] for r in rain_range]
|
||
|
||
fig_R = go.Figure()
|
||
fig_R.add_trace(go.Scatter(
|
||
x=rain_range, y=y_rain,
|
||
mode='lines', line=dict(color='#38bdf8', width=2.5),
|
||
fill='tozeroy', fillcolor='rgba(56,189,248,0.08)',
|
||
name='产量'
|
||
))
|
||
fig_R.add_vline(x=rainfall, line=dict(color='#f59e0b', width=1.5, dash='dot'),
|
||
annotation_text=f"当前 {rainfall}mm", annotation_font_color='#f59e0b')
|
||
fig_R.update_layout(
|
||
title=dict(text="月降雨量 vs 产量", font=dict(color='#94a3b8', size=12)),
|
||
xaxis=dict(title="降雨量 (mm/月)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
|
||
yaxis=dict(title="产量 (kg/ha)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
|
||
paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
|
||
font=dict(color='#e2e8f0', size=10),
|
||
margin=dict(t=36, b=36, l=50, r=20), height=220,
|
||
showlegend=False,
|
||
)
|
||
st.plotly_chart(fig_R, use_container_width=True)
|
||
|
||
# ─── All Crops Comparison Bar Chart ───────────────────────────────────────────
|
||
st.markdown('<div class="section-header">🌐 全作物产量对比</div>', unsafe_allow_html=True)
|
||
|
||
crop_names = [f"{r['emoji']} {r['crop']}" for r in rankings]
|
||
crop_yields = [r['yield_ha'] for r in rankings]
|
||
crop_colors = [r['color'] for r in rankings]
|
||
|
||
fig_bar = go.Figure()
|
||
fig_bar.add_trace(go.Bar(
|
||
x=crop_names, y=crop_yields,
|
||
marker=dict(color=crop_colors, opacity=0.85,
|
||
line=dict(color='rgba(255,255,255,0.2)', width=1)),
|
||
text=[f"{y:,.0f}" for y in crop_yields],
|
||
textposition='outside',
|
||
textfont=dict(color='#94a3b8', size=10, family='JetBrains Mono'),
|
||
))
|
||
fig_bar.update_layout(
|
||
xaxis=dict(color='#64748b', gridcolor='rgba(255,255,255,0.04)'),
|
||
yaxis=dict(title="预期产量 (kg/ha)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
|
||
paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
|
||
font=dict(color='#e2e8f0', size=11),
|
||
margin=dict(t=20, b=30, l=60, r=20), height=240,
|
||
showlegend=False,
|
||
)
|
||
st.plotly_chart(fig_bar, use_container_width=True)
|
||
|
||
# ─── Advisory Panel ───────────────────────────────────────────────────────────
|
||
st.markdown('<div class="section-header">💡 智能建议</div>', unsafe_allow_html=True)
|
||
adv1, adv2 = st.columns(2)
|
||
|
||
with adv1:
|
||
crop_opt = CROPS[selected_crop]["optimal"]
|
||
advisories = []
|
||
|
||
if not (crop_opt["ph"][0] <= ph <= crop_opt["ph"][1]):
|
||
advisories.append(("warn", f"pH {ph} 偏离 {selected_crop} 适宜范围 {crop_opt['ph']}，建议{'施石灰' if ph < crop_opt['ph'][0] else '施硫磺'}调节"))
|
||
else:
|
||
advisories.append(("good", f"土壤 pH {ph} 处于 {selected_crop} 适宜范围内 ✓"))
|
||
|
||
if N < crop_opt["N"][0]:
|
||
advisories.append(("warn", f"氮肥不足（{N} vs 建议 {crop_opt['N'][0]}-{crop_opt['N'][1]} mg/kg），建议追施尿素"))
|
||
elif N > crop_opt["N"][1]:
|
||
advisories.append(("warn", f"氮肥过量（{N} mg/kg），可能造成徒长，建议减施"))
|
||
else:
|
||
advisories.append(("good", f"氮肥水平 {N} mg/kg 适宜 ✓"))
|
||
|
||
if rainfall < crop_opt["rainfall"][0]:
|
||
advisories.append(("warn", f"降雨量不足，建议增加灌溉（缺水 {crop_opt['rainfall'][0]-rainfall} mm）"))
|
||
elif rainfall > crop_opt["rainfall"][1]:
|
||
advisories.append(("warn", f"降雨量偏多，注意防涝排水"))
|
||
else:
|
||
advisories.append(("good", f"降雨量 {rainfall}mm 适合 {selected_crop} 生长 ✓"))
|
||
|
||
for typ, msg in advisories:
|
||
css_class = "alert-good" if typ == "good" else "alert-warn"
|
||
st.markdown(f'<div class="{css_class}">{msg}</div>', unsafe_allow_html=True)
|
||
|
||
with adv2:
|
||
st.markdown(f"""
|
||
<div class="rec-card">
|
||
<div style="font-size:0.82rem; color:#64748b; font-family:'JetBrains Mono',monospace; margin-bottom:12px;">
|
||
当前环境参数下适宜种植：
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
badges = "".join([
|
||
f'<span class="crop-badge">{r["emoji"]} {r["crop"]} {r["score"]*100:.0f}%</span>'
|
||
for r in rankings if r['score'] > 0.6
|
||
])
|
||
st.markdown(f'{badges}</div>', unsafe_allow_html=True)
|
||
|
||
st.markdown(f"""
|
||
<div style="margin-top:16px; font-size:0.85rem; color:#94a3b8; line-height:1.7;">
|
||
<b style="color:#4ade80;">最优方案：</b>{best_crop['emoji']} {best_crop['crop']}<br>
|
||
预期单产：<span style="font-family:'JetBrains Mono',monospace; color:#38bdf8;">{best_crop['yield_ha']:,.0f} kg/ha</span><br>
|
||
{area:.0f}公顷总产：<span style="font-family:'JetBrains Mono',monospace; color:#38bdf8;">{best_crop['total_yield']/1000:,.1f} 吨</span>
|
||
</div>
|
||
""", unsafe_allow_html=True)
|
||
|
||
# ─── Footer ───────────────────────────────────────────────────────────────────
|
||
st.markdown("<br>", unsafe_allow_html=True)
|
||
st.markdown("""
|
||
<div style="text-align:center; font-family:'JetBrains Mono',monospace; font-size:0.72rem;
|
||
color:#334155; padding:16px; border-top:1px solid rgba(74,222,128,0.1);">
|
||
YIELD = f(Soil, Weather, Pesticide) | Cobb-Douglas Multi-Factor Model | 农业智能决策系统
|
||
</div>
|
||
""", unsafe_allow_html=True)
|