AI in Agriculture 2026: From Pest Detection to Yield Prediction – The AI Revolution in Modern Farming

A Practical AI Beginner's Guide for Farmers, Agribusinesses, and Agritech Practitioners

Chinese agriculture is entering the AI era, but many practitioners still don't realize these tools are already usable.

1. Mobile AI: The Lowest Barrier to Agricultural AI

1.1 Pest and Disease Identification Apps

Shihuajun / Xingsè (Plant Identification):

Identify plant species by taking a photo

Display common diseases and control methods

Ministry of Agriculture Official App:

Upload photos via phone, AI identifies pests and diseases

Provides medication recommendations and dosage guidance

Commercial Tools:

DJI Agriculture App: Crop protection drones + AI disease identification

Nongxin Internet: Feed/livestock AI management

1.2 Consulting ChatGPT on Agricultural Issues


I am growing [crop] in [province/region], currently in [month],
and I observe the following symptoms: [describe symptoms or upload image]
Please help me analyze:
What disease/pest might this be?
How to assess severity
Control plan (pesticide name + dosage + precautions)
Preventive measures
Local climate: [brief description], Planting area: [X mu]

2. Precision Agriculture: AI + Sensors

2.1 Soil Monitoring + AI Fertilizer Recommendations


My soil test report:
pH: [X], Organic matter: [X%],
Nitrogen: [X mg/kg], Phosphorus: [X mg/kg], Potassium: [X mg/kg]
Crop: [crop name], Planned planting density: [X plants/mu]
Target yield: [X kg/mu]
Please provide:
Fertilization plan (base fertilizer + top dressing)
Dosage calculation (per mu)
Timing recommendations for fertilization
Whether soil improvement is needed and how

2.2 Weather AI for Decision Support


Weather forecast for the past week: [temperature/precipitation/wind speed data]
I am considering:
Whether this week is suitable for spraying (needs no wind, >6 hours after rain)
Whether early harvest is needed (risk of rain/strong wind)
Irrigation schedule adjustment (water saving)
Planting timing decision
Crop: [crop], Growth stage: [stage]

3. Recommended Agricultural AI Platforms

3.1 Domestic Platforms

PlatformFeaturesSuitable Scale

Nongxin InternetFull-chain AI for livestockLivestock enterprises DJI AgricultureDrones + precision crop protection100+ mu Carbon Road TechCarbon sink management + AI decision-makingLarge farms Huiteng Energy TechAI control for facility agricultureGreenhouses

3.2 Open-Source Tools (For Those with Technical Background)

python
Train a disease identification model using the PlantDoc dataset
from ultralytics import YOLO
Load a pretrained model
model = YOLO('yolov8n.pt')
Fine-tune on an agricultural disease dataset
results = model.train(
    data='plantdoc.yaml',
    epochs=100,
    imgsz=640,
    device=0  # GPU
)
Real-time prediction
results = model.predict('field_photo.jpg')
for r in results:
    for box in r.boxes:
        print(f'Detected: {r.names[int(box.cls)]}, Confidence: {box.conf:.2f}')

4. Yield Prediction and Price Analysis

4.1 AI-Assisted Planting Decisions


I plan to plant [crop] this year on an area of [X mu]
Please help me analyze:
Market outlook for this crop this year (based on your knowledge)
Impact of weather conditions in major production areas on yield
Recommended selling timing (when prices might peak)
Any better alternative crops to consider
Local conditions: [region], Land conditions: [brief description]