Israeli Agritech Advisor

Israeli Agritech Advisor

Instructions

Step 1: Identify the Agritech Use Case

| Use Case | Key Platforms | Data Types | Goal | |----------|--------------|------------|------| | Irrigation optimization | CropX, Netafim, Manna | Soil moisture, weather, ET0 | Reduce water use 20-40% | | Pest/disease detection | Taranis, AgroScout | Aerial imagery, NDVI | Early detection, targeted treatment | | Greenhouse monitoring | Prospera/Valmont | Climate, imagery | Optimal growing conditions | | Pollination management | BeeHero | Hive sensors, GPS | Maximize pollination efficiency | | Farm data platform | Multiple | All sensor data | Unified decision dashboard | | Water compliance | Mekorot data, sensors | Water flow, quotas | Meet Water Authority regulations |

Step 2: Connect to Agritech APIs

CropX -- Soil Monitoring Integration:

import requests

class CropXClient:
    """Client for CropX soil monitoring API."""

    BASE_URL = "https://api.cropx.com/v2"

    def __init__(self, client_id, client_secret):
        self.token = self._authenticate(client_id, client_secret)
        self.headers = {"Authorization": f"Bearer {self.token}"}

    def _authenticate(self, client_id, client_secret):
        response = requests.post(f"{self.BASE_URL}/auth/token", json={
            "client_id": client_id,
            "client_secret": client_secret,
            "grant_type": "client_credentials"
        })
        return response.json()["access_token"]

    def get_sites(self):
        """List all monitored field sites."""
        return requests.get(f"{self.BASE_URL}/sites", headers=self.headers).json()

    def get_soil_readings(self, device_id, start_date, end_date):
        """Get soil sensor readings for a device."""
        return requests.get(
            f"{self.BASE_URL}/devices/{device_id}/measurements",
            headers=self.headers,
            params={"from": start_date.isoformat(), "to": end_date.isoformat(),
                    "metrics": "moisture,temperature,ec"}
        ).json()

    def get_irrigation_recommendation(self, site_id):
        """Get AI-driven irrigation recommendation for a site."""
        return requests.get(
            f"{self.BASE_URL}/sites/{site_id}/recommendations",
            headers=self.headers
        ).json()

Netafim GrowSphere -- Irrigation Control Integration:

> Note: The GrowSphere API URL below is illustrative. GrowSphere is a consumer app and Netafim does not publish a documented public API. Contact Netafim directly for partnership/API access.

class GrowSphereClient:
    """Client for Netafim GrowSphere irrigation platform.
    NOTE: No documented public API exists. Contact Netafim for access."""

    BASE_URL = "https://growsphere.netafim.com/api/v1"  # Unverified, illustrative only

    def __init__(self, api_key):
        self.headers = {"X-API-Key": api_key, "Content-Type": "application/json"}

    def get_controllers(self):
        """List all irrigation controllers."""
        return requests.get(f"{self.BASE_URL}/controllers", headers=self.headers).json()

    def create_irrigation_schedule(self, controller_id, zone_id, schedule):
        """Set irrigation schedule for a zone."""
        return requests.post(
            f"{self.BASE_URL}/controllers/{controller_id}/zones/{zone_id}/schedules",
            headers=self.headers, json=schedule
        ).json()

    def get_flow_data(self, controller_id, start_date, end_date):
        """Get water flow data for compliance tracking."""
        return requests.get(
            f"{self.BASE_URL}/controllers/{controller_id}/flow",
            headers=self.headers,
            params={"from": start_date.isoformat(), "to": end_date.isoformat()}
        ).json()

Taranis -- Crop Intelligence Integration:

class TaranisClient:
    """Client for Taranis crop intelligence platform."""

    BASE_URL = "https://api.taranis.com/v1"

    def __init__(self, api_key):
        self.headers = {"Authorization": f"Bearer {api_key}"}

    def get_fields(self):
        """List monitored fields."""
        return requests.get(f"{self.BASE_URL}/fields", headers=self.headers).json()

    def get_detections(self, field_id, scan_id=None):
        """Get pest/disease detections for a field."""
        params = {}
        if scan_id:
            params["scan_id"] = scan_id
        return requests.get(
            f"{self.BASE_URL}/fields/{field_id}/detections",
            headers=self.headers, params=params
        ).json()

    def request_scan(self, field_id, scan_type="full"):
        """Request a new aerial scan of a field."""
        return requests.post(
            f"{self.BASE_URL}/fields/{field_id}/scans",
            headers=self.headers, json={"type": scan_type}
        ).json()

Step 3: Implement Irrigation Optimization

def calculate_irrigation_need(soil_data, crop_type, weather_data):
    """Calculate irrigation need based on soil, crop, and weather data.
    Uses water balance approach common in Israeli precision agriculture.
    """
    # Crop coefficients (Kc) -- Israeli Volcani Institute values
    CROP_KC = {
        "citrus": {"initial": 0.65, "mid": 0.70, "late": 0.65},
        "avocado": {"initial": 0.60, "mid": 0.85, "late": 0.75},
        "tomato": {"initial": 0.60, "mid": 1.15, "late": 0.80},
        "pepper": {"initial": 0.60, "mid": 1.05, "late": 0.90},
        "date_palm": {"initial": 0.90, "mid": 0.95, "late": 0.95},
        "table_grape": {"initial": 0.30, "mid": 0.85, "late": 0.45},
    }
    kc = CROP_KC.get(crop_type, {"initial": 0.6, "mid": 1.0, "late": 0.8})
    et_crop = weather_data["et0"] * kc["mid"]
    effective_rain = max(0, weather_data.get("precipitation", 0) * 0.8)
    net_need = max(0, et_crop - effective_rain)

    current_moisture = soil_data["moisture_percent"]
    field_capacity = soil_data.get("field_capacity", 35)
    wilting_point = soil_data.get("wilting_point", 15)
    mad = 0.50
    threshold = field_capacity - (field_capacity - wilting_point) * mad

    if current_moisture > threshold:
        return {"irrigate": False, "reason": "Soil moisture adequate",
                "current": current_moisture, "threshold": threshold}

    efficiency = 0.92  # Drip irrigation: 90-95% in Israel
    gross_need = net_need / efficiency
    return {
        "irrigate": True,
        "net_need_mm": round(net_need, 1),
        "gross_need_mm": round(gross_need, 1),
        "current_moisture": current_moisture,
        "threshold": threshold,
        "et_crop": round(et_crop, 1)
    }

Step 4: Israeli Agricultural Climate Zones

| Zone | Region | Avg Rainfall (mm/yr) | Key Crops | Irrigation Need | |------|--------|---------------------|-----------|----------------| | Mediterranean | Coastal plain, Galilee | 500-700 | Citrus, avocado, vegetables | Moderate (summer) | | Semi-arid | Northern Negev, Shephelah | 250-400 | Wheat, olives, grapes | High | | Arid | Central Negev | 50-200 | Limited rainfed | Very high (full irrigation) | | Hyper-arid | Arava Valley | less than 50 | Dates, peppers, tomatoes | Full irrigation year-round | | Subtropical | Jordan Valley, Beit Shean | 300-400 | Dates, bananas, fish ponds | High (extreme heat) |

Step 5: Israeli Agritech Ecosystem Overview

Key companies beyond the main platforms:

• Phytech: Plant-based sensors for water stress detection
• Manna Irrigation: Satellite-based irrigation, no ground sensors
• BeeHero: IoT beehive monitoring for pollination
• BeeWise: Robotic, AI-managed beehives ("Beehome") for autonomous hive management
• AgroScout: Drone-based crop scouting and disease detection
• Tevel Aerobotics: Autonomous fruit-picking drones tethered to ground units
• SupPlant: AI-driven irrigation for smallholder farmers
• Phytech: Plant-based water stress sensors (acquired Saturas in 2023)
• Groundwork BioAg: Mycorrhizal inoculants for nutrient uptake
• Agropalette: Data and analytics for produce supply chains

Israel-specific agricultural context:

• Israel recycles 85%+ of wastewater for agriculture (highest rate globally)
• Water sources: Mekorot (national), recycled wastewater, desalinated, local wells
• Desalination feeds the system at scale: the Sorek, Hadera, and Ashkelon plants (among others) supply Mekorot's potable and agricultural mix, making Israel a global leader in seawater reverse osmosis.
• Kibbutz innovation underpins much of the sector: Netafim's drip irrigation originated at Kibbutz Hatzerim in 1965 and remains a defining export.
• Data formats: GeoJSON for field boundaries, GeoTIFF for satellite imagery, CSV/JSON for sensors

Examples

Example 1: Smart Irrigation Setup

User says: "I need to set up smart irrigation for an avocado orchard in the Galilee" Result: Guide CropX sensor placement (2 per management zone), connect to Netafim controller, configure Kc values for avocado, set MAD at 50%, implement weather-adjusted scheduling.

Example 2: Pest Detection Pipeline

User says: "How do I integrate Taranis for pest detection in our pepper fields?" Result: Set up Taranis field boundaries, configure scan schedule (weekly during growing season), implement detection webhook handler, create alert pipeline for high-severity threats.

Example 3: Water Compliance Dashboard

User says: "Build a dashboard tracking water usage against our Water Authority quota" Result: Connect flow meters via GrowSphere API, aggregate daily/weekly/monthly usage, compare against quota allocation, generate compliance reports, alert at 80% and 95% thresholds.

Bundled Resources

References

• references/agritech-ecosystem.md , Directory of Israeli agritech platforms and APIs (CropX, Netafim GrowSphere, Taranis) with endpoint details, plus a company directory covering irrigation, crop monitoring, pollination, and biological sectors. Includes standard data formats (GeoJSON, GeoTIFF, CSV/JSON), agricultural zone rainfall and water source data, and Volcani Institute crop coefficients (Kc) for Israeli conditions. Consult when selecting platforms, configuring API integrations, or looking up crop-specific irrigation parameters.

Recommended MCP Servers

No agritech-specific MCP server is currently in the directory. For weather data feeding irrigation models, the Israel Meteorological Service MCP (ims-mcp) provides rain, ET0, and station data via official IMS endpoints.

Reference Links

| Source | URL | What to Check | |--------|-----|---------------| | Volcani Institute / Agricultural Research Organization | https://www.agri.gov.il | Crop coefficient (Kc) tables, Israeli-context agronomy research | | Israel Ministry of Agriculture and Rural Development | https://www.gov.il/en/departments/ministryofagricultureandruraldevelopment | Subsidy programs, regulations, and certifications | | Israel Water Authority | https://www.gov.il/en/departments/waterauthority | Water allocation quotas, agricultural-tariff updates, and reclaimed-water rules | | Israel Innovation Authority | https://innovationisrael.org.il/en | Agritech grants and pilot funding programs | | Start-Up Nation Central -- AgriFoodTech | https://www.startupnationcentral.org | Industry directory and company-stage data for the Israeli agritech ecosystem |

Gotchas

• Israeli agricultural seasons differ from Northern European/US patterns due to the Mediterranean climate. Agents may recommend planting schedules based on temperate-zone assumptions.
• Water allocation in Israeli agriculture is regulated by Mekorot (the national water company). Agents may not account for water quota restrictions when recommending irrigation plans.
• Israeli organic certification ("Mekori") follows different standards than USDA Organic or EU Organic. Agents may cite incorrect certification requirements.
• Agricultural technology subsidies from the Israeli Ministry of Agriculture change annually. Agents may reference outdated subsidy programs or amounts.
• Shmita (sabbatical year) laws affect religious agricultural operations in Israel every 7 years. Agents may not be aware of this religious agricultural cycle and its implications.

Troubleshooting

Error: "Sensor readings seem inaccurate"

Cause: Soil sensor calibration issue or installation depth mismatch Solution: CropX sensors need soil-specific calibration. Verify installation depth matches crop root zone. Israeli soils vary dramatically, coastal sand vs. Negev loess vs. basalt in Golan.

Error: "Irrigation recommendation overwatering"

Cause: ET0 calculation using wrong climate zone or outdated Kc values Solution: Verify weather station is local (Israel's microclimates vary over short distances). Use Volcani Institute Kc values for Israeli conditions. Check soil type matches sensor calibration.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: squadcodercom
• Source: squadcodercom/squadcoder
• License: MIT
• Homepage: https://squadcoder.com

Install and usage instructions live in the source repository linked above.