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AI for Agriculture

224 models ranked for agriculture and farming. Scored with bonuses for vision (crop/pest analysis), reasoning (yield prediction), JSON mode (structured data), function calling (IoT integration), and web search (weather data).

How we rank: composite score (benchmark scores 90%, capabilities 5%, context window 5%) adjusted with use-case-specific capability bonuses.
224
Total Ranked
224
Vision
201
Reasoning
214
JSON Mode

Agriculture AI - Ranked by Agriculture Score

#ModelScore
1Claude Fable 5.1Anthropic96
2Claude Fable 5.1 (batch)Anthropic96
3Claude Fable 5Anthropic96
4Claude Fable 5 (batch)Anthropic96
5Claude Opus 5.5Anthropic95
6Claude Opus 5Anthropic95
7Claude Opus 4.8Anthropic95
8Claude Opus 4.7Anthropic95
9GPT-5.5 ProOpenAI93
10GPT-5.5 Pro (batch)OpenAI93
11GPT-5.5OpenAI93
12Gemini 3.1 Pro Preview Custom ToolsGoogle92
13Gemini 3.1 Pro PreviewGoogle92
14Gemini 3.1 Pro Preview (batch)Google92
15GPT-5.4 ProOpenAI92
16GPT-5.4 Pro (batch)OpenAI92
17GPT-5.4OpenAI92
18GPT-5.3-CodexOpenAI91
19GPT-5.2-CodexOpenAI91
20GPT-5.2 ProOpenAI91
21GPT-5.2 Pro (batch)OpenAI91
22GPT-5.2OpenAI91
23Claude Opus 4.6Anthropic90
24GPT-5.6 Luna ProOpenAI89
25GPT-5.6 Luna Pro (batch)OpenAI89
26GPT-5.6 LunaOpenAI89
27GPT-5.6 Luna (batch)OpenAI89
28GPT-5.6 Terra ProOpenAI89
29GPT-5.6 Terra Pro (batch)OpenAI89
30GPT-5.6 TerraOpenAI89

AI for Agriculture & Farming

Crop Analysis

Vision models identify crop diseases, nutrient deficiencies, and growth stages from field images. Upload drone or satellite imagery for automated field analysis.

Pest Detection

Identify insects, weeds, and diseases from photos. Models recommend treatment options with dosage, timing, and organic alternatives.

Yield Prediction

Analyze weather, soil data, and historical yields to forecast harvests. JSON mode produces structured predictions for farm management systems.

Precision Farming

Optimize irrigation schedules, fertilizer application, and planting density. Function calling integrates with IoT sensors and weather APIs for real-time decisions.

Frequently Asked Questions

Vision-capable models analyze satellite imagery, drone photos, and soil maps to identify crop stress, pest damage, and irrigation issues. Models with web search can pull real-time weather data and market prices for yield optimization.

Self-hostable open-source models can run on local hardware without internet. Smaller models (7B-13B parameters) work on edge devices, though they trade capability for offline access. Consider models ranked high for open-source flexibility.

Models with reasoning capabilities can analyze historical yield data, weather patterns, and market trends to provide forecasts. However, these are estimates - combine AI insights with agronomist expertise and local knowledge for best results.

For analyzing multi-page soil reports, seasonal data, or regulatory documents, 128K+ context windows are essential. Smaller context models may miss connections between early-season conditions and harvest projections.

AI for Agriculture - Best AI Models (2026) | LM Market Cap