AI Agent在农业领域的创新应用：从精准种植到智慧养殖

class SoilHealthAgent:
    def __init__(self):
        self.sensors = IoTNetwork()  # 土壤温湿度、pH、NPK传感器
        self.vision = SoilImageAnalyzer()  # 土壤颜色/质地图像分析
        self.history = FieldHistory()  # 历史种植数据
    
    async def diagnose(self, field_id):
        # 1. 采集实时传感器数据
        sensor_data = await self.sensors.read(field_id)
        
        # 2. 图像分析
        soil_image = await self.capture_image(field_id)
        visual_analysis = self.vision.analyze(soil_image)
        
        # 3. 综合诊断
        diagnosis = await self.llm.analyze(
            sensor_data=sensor_data,
            visual=visual_analysis,
            history=self.history.get(field_id),
            crop_type=self.history.current_crop(field_id)
        )
        
        # 4. 生成建议
        return {
            "health_score": diagnosis.score,
            "issues": diagnosis.problems,
            "recommendations": diagnosis.actions,
            "priority": diagnosis.urgency
        }

今日灌溉建议：
  灌溉区域: A3号地块（玉米，拔节期）
  灌溉量: 15mm
  最佳时间: 18:00-19:00（蒸发量最低）
  理由: 土壤20cm湿度32%（低于拔节期适宜范围40-50%），
        明日气温35℃，需提前补充水分
  预计节省: 相比定时灌溉节水35%

class PestDetectionAgent:
    async def monitor(self, field_id):
        # 1. 无人机巡检图像
        images = await self.drone.capture(field_id, altitude=5)
        
        # 2. 多模型识别
        detections = []
        for img in images:
            # 叶片病斑检测
            leaf_diseases = self.disease_model.detect(img)
            # 害虫识别
            pests = self.pest_model.detect(img)
            # 营养缺乏症状
            deficiency = self.nutrient_model.detect(img)
            detections.extend(leaf_diseases + pests + deficiency)
        
        # 3. 严重程度评估
        severity = self.assess_severity(detections, field_area=field_id)
        
        # 4. 防治方案
        if severity.score > 0.3:
            treatment = await self.plan_treatment(
                detections, severity, 
                weather=await self.weather.forecast(7),
                crop_stage=self.crop_stage(field_id)
            )
            return Alert(level="warning", treatment=treatment)
        
        return Alert(level="normal")

风险预警：
  小麦赤霉病: 高风险（未来3天有持续降雨，气温20-25℃利于发病）
  推荐操作: 4月15-16日喷施预防性杀菌剂
  预防效果: 可降低发病率60-70%

class LivestockHealthAgent:
    async def daily_check(self, barn_id):
        # 1. 行为分析（摄像头）
        behaviors = await self.camera_analyzer.analyze(barn_id)
        # 检测异常行为: 聚集、离群、减少采食
        
        # 2. 体温监测（红外摄像头）
        temps = await self.thermal.scan(barn_id)
        fever_animals = [t for t in temps if t > 39.5]
        
        # 3. 声音分析
        sounds = await self.audio_analyzer.analyze(barn_id)
        # 咳嗽检测、异常叫声检测
        
        # 4. 采食/饮水监测
        feed_pattern = await self.feed_monitor.pattern(barn_id)
        
        # 5. 综合健康评估
        return HealthReport(
            herd_status=self.assess_herd(behaviors, temps, sounds),
            individuals=self.flag_individuals(fever_animals, abnormal_behaviors),
            recommendations=self.suggest_actions()
        )

牛只 #042（荷斯坦，泌乳中期，日产奶28kg）
当前体重: 620kg
体况评分: 3.0
今日饲喂方案:
  混合精料: 8.5kg（比昨日-0.3kg，因活动量降低）
  青贮: 22kg
  干草: 4kg
  补充: 维生素E + 硒（乳腺健康维护）
  饮水: 保证80L以上

class HarvestTimingAgent:
    async def recommend(self, field_id):
        # 作物成熟度评估
        maturity = await self.assess_maturity(field_id)
        
        # 气象窗口
        weather = await self.weather.forecast(14)
        harvest_windows = self.find_dry_windows(weather)
        
        # 市场价格预测
        price_forecast = await self.market.predict(
            crop=self.crop_type(field_id),
            horizon=30  # 30天价格预测
        )
        
        # 最优收获时间
        return {
            "maturity": maturity.score,
            "best_window": harvest_windows[0],
            "expected_yield": maturity.estimated_yield,
            "price_forecast": price_forecast,
            "recommendation": self.optimize(maturity, harvest_windows, price_forecast)
        }