# Clemson HGIC Home & Garden Factsheet Scraper (`jungle_synthesizer/clemson-hgic-home-garden-factsheet-scraper`) Actor Scrapes the Clemson HGIC factsheet library — 2,500+ science-based factsheets on plant care, diseases, pest management, lawn care, and food preservation. Outputs structured records: HGIC ID, body sections, symptoms, causal agent, management, products, authors. - **URL**: https://apify.com/jungle\_synthesizer/clemson-hgic-home-garden-factsheet-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Developer tools, Education - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Clemson HGIC Home & Garden Factsheet Scraper Scrapes the [Clemson Home & Garden Information Center (HGIC)](https://hgic.clemson.edu/factsheet/) factsheet library — 2,500+ science-based factsheets covering plant care, diseases, pest management, lawn care, and food preservation. Outputs structured records with HGIC ID, body sections, symptoms, causal agent, management recommendations, recommended products, authors, and related factsheets. ### What It Does Clemson HGIC is one of the largest university extension factsheet libraries in the US (SE US plant palette, 2,500+ documents). Each factsheet follows a consistent template with discrete sections: symptoms, causal agent (pathogen/pest binomial), management/control recommendations, and prevention. This actor parses that structure into machine-readable fields — exactly what plant-diagnosis apps, AI garden assistants, and agronomy SaaS platforms need as grounding data. The actor reads the Yoast sitemap index to enumerate all factsheet URLs, then crawls each page with impit Chrome TLS fingerprinting — no proxy or CAPTCHA solver required. ### Use Cases - Training data for plant disease diagnosis AI and AI garden assistant models - Structured extension knowledge base for horticulture SaaS - Agronomy/landscaping content and reference data pipelines - Garden app content enrichment (symptom/treatment lookup) ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | maxItems | integer | 10 | Maximum number of factsheets to scrape. Set to a large number to scrape all ~2,500+ factsheets. | ### Output Each item represents one HGIC factsheet. | Field | Type | Description | |-------|------|-------------| | factsheet_id | string | HGIC factsheet number, e.g. `HGIC 1223` | | slug | string | URL slug, e.g. `turfgrasses-for-the-carolinas` | | title | string | Factsheet title | | category | string | Subject category: Diseases, Insects, Lawns, Soils, Vegetables, Trees & Shrubs, Flowers, Fruits & Nuts, Food Safety & Preservation, Human Health & Safety, General | | plant_subjects | string | Comma-separated plant names from the title | | problem_type | string | Problem type: `disease`, `insect`, `cultural`, or `none` | | summary | string | First meaningful paragraph / introductory text | | body_sections | string | JSON array of `{heading, text}` objects for the full structured body | | symptoms | string | Symptom description text (for disease/pest/damage factsheets) | | causal_agent | string | Pathogen or pest scientific/common name | | management | string | Management and control recommendation text | | prevention | string | Prevention and cultural practices text | | recommended_products | string | Comma-separated trade names and chemistries found in management sections | | related_factsheets | string | Comma-separated related factsheet links (`title | URL`) | | last_updated | string | Revision date as shown in factsheet metadata, e.g. `Feb 28, 2016` | | authors | string | Comma-separated list of factsheet authors | | images | string | Comma-separated image URLs embedded in the factsheet | | factsheet_url | string | Canonical URL of the factsheet | | scrapedAt | string | ISO-8601 timestamp when the record was scraped | ### Sample Output ```json { "factsheet_id": "HGIC 1223", "slug": "turfgrasses-for-the-carolinas", "title": "Turfgrasses for the Carolinas", "category": "Lawns", "problem_type": "none", "summary": "For over 50 years the lawn has been an integral part of the landscape...", "body_sections": "[{\"heading\":\"Mowing\",\"text\":\"...\"}]", "last_updated": "Feb 28, 2016", "authors": "Millie Davenport, Gary Forrester", "factsheet_url": "https://hgic.clemson.edu/factsheet/turfgrasses-for-the-carolinas/" } ```` ### Discovery Method Reads the Yoast sitemap index at `https://hgic.clemson.edu/sitemap.xml`, filters for `factsheet-sitemap.xml` and `factsheet-sitemap2.xml`, and collects all `/factsheet//` URLs. The `maxItems` cap is applied before crawling begins. ### Performance - **Memory**: 128–256 MB - **Throughput**: ~200 pages/minute at default concurrency (5) - **Full corpus (~2,500 factsheets)**: ~15–20 minutes - **Timeout**: 2-hour default (sufficient for full corpus) # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of factsheets to scrape. Leave blank to scrape all available factsheets (~2,500+). Default is 10 for a quick test run. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/clemson-hgic-home-garden-factsheet-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/clemson-hgic-home-garden-factsheet-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }' | apify call jungle_synthesizer/clemson-hgic-home-garden-factsheet-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/clemson-hgic-home-garden-factsheet-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Clemson HGIC Home & Garden Factsheet Scraper", "description": "Scrapes the Clemson HGIC factsheet library — 2,500+ science-based factsheets on plant care, diseases, pest management, lawn care, and food preservation. Outputs structured records: HGIC ID, body sections, symptoms, causal agent, management, products, authors.", "version": "0.1", "x-build-id": "dyydwQkJCEcgktkzN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~clemson-hgic-home-garden-factsheet-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-clemson-hgic-home-garden-factsheet-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~clemson-hgic-home-garden-factsheet-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-clemson-hgic-home-garden-factsheet-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~clemson-hgic-home-garden-factsheet-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-clemson-hgic-home-garden-factsheet-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of factsheets to scrape. Leave blank to scrape all available factsheets (~2,500+). Default is 10 for a quick test run.", "default": 10 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```