Introduction

India loses billions annually due to:

• Late pest detection

• Fungal infections (rust, blight, mildew)

• Water stress

• Heat stress

• Nutrient imbalance

Most of these problems are detected after visible damage appears; when yield is already compromised.

This blog explores how an intelligent edge system built around Infineon’s PSOC™ 6 AI Evaluation Kit can enable early crop stress detection for a 1-acre Indian farm. So, first lets deep dive into the problem scenarios.

Crop Stress

Crop stress occurs when plants experience adverse conditions that disrupt normal growth and metabolism.

Types of Crop Stress:

1. Biotic Stress (Living Factors)

   • Insects (aphids, borers)

   • Fungi (rust, blight)

   • Bacterial infections

   • Viral infections

2. Abiotic Stress (Environmental Factors)

   • Water deficiency

   • Excess irrigation

   • High temperature (>45°C common in India)

   • Soil salinity

   • Nutrient deficiency (NPK imbalance)

How Crops Die Due to Insects & Disease

Example: Fungal Infection Progression

1. Spores land on leaves.

2. Infection begins at microscopic level.

3. Chlorophyll activity reduces.

4. Photosynthesis declines.

5. Yellowing / spotting appears.

6. Leaf necrosis spreads.

7. Plant growth stunts.

8. Yield drops.

By the time a farmer visually notices spots, 20–40% damage may already be done.

Manual Detection Methods

Traditional Method:

• Walk across field

• Randomly inspect plants

• Observe discoloration

• Check soil moisture manually

Example of Problems in a 1 Acre Farm:

• 1 acre ≈ 43,560 sq ft

• Thousands of plants

• Impossible to inspect every plant daily

• Early-stage infection is invisible to the naked eye

• Human fatigue

• Labour cost

Disadvantages of Manual Inspection:

• Misses early biochemical stress

• Not scalable

• Time-consuming

Scientific Detection of Crop Stress

Before visible damage:

• Leaf temperature rises

• Chlorophyll fluorescence changes

• Soil moisture deviates

• Plant reflectance spectrum shifts

• Humidity patterns alter

• Micro-climate imbalance appears

These signals can be captured via:

• Soil moisture sensors

• Temperature sensors

• Humidity sensors

• Multispectral camera

• Leaf temperature IR sensors

Proposed 1 Acre Deployment Model

Layer 1 – Ground Sensor Nodes (4–6 per acre if ground is even levelled)

Each node includes:

• Soil moisture sensor

• Temperature and Humidity sensor

• Optional leaf wetness sensor (Strong indicator of fungal risk!)

• PSOC™ 6 MCU

• Solar panel + Battery

• Wi-Fi/BLE connectivity

The PSOC™ 6 MCU based Evaluation board (CY8CKIT-062S2-AI):

• Collects sensor data

• Runs lightweight Inference locally (ML)

• Predicts irrigation need

• Detects abnormal humidity trends

• Stores only summaries (not raw data)

Layer 2 – Data Collection via Drone (Once per Day)

• Drone flies a planned route

• When within range, it shall,

  1. Scan for BLE Advertisement

  2. Discover a Node/Connect to the Node

  3. Authenticate the Node

  4. Request the Inference logs for the last 24hrs

  5. Disconnect immediately

• When it returns to Dock/Resting station:

  • Drone offloads consolidated logs to Laptop via USB / Wi-fi

  • Laptop shows a “zone wise Risk / Health Map” + Alerts + Recommendations

More about PSOC™ 6

• Low-power solution (battery / solar powered rural deployments)

• High reliability in harsh weather (45°C+ summers, humidity, dust)

• Secure connected devices

• Cost-efficient scalable systems

• Edge intelligence (due to inconsistent cloud connectivity)

• The AI kit ecosystem is designed around collecting sensor data and building models with Infineon’s tooling

• DeepCraft™ helps to train/optimize/deploy our custom model to the device

The PSOC™ 6 MCU is uniquely positioned because it offers:

A Dual-Core Architecture

• 150 MHz Arm Cortex-M4 (Application + DSP/AI tasks)

• 100 MHz Cortex-M0+ (Low power management)

This enables:

• Sensor acquisition + AI inference on M4

• Low-power standby + connectivity management on M0+

Ultra-Low Power Operation

Perfect for:

• Solar-powered agricultural devices

• Wearables in rural healthcare

• Remote environmental monitoring stations

Built-in WiFi & Bluetooth

Using CYW43439:

• Farm-to-cloud telemetry

• Mobile app connectivity

• OTA firmware updates

Security

• Secure boot

• Cryptographic acceleration

• IoT-safe deployments

Implementation Challenges

Reasoning for 4–6 Nodes/Acre

1 acre is approximately equal to 4047 m².
If nodes are placed in a rough grid:

• 4 nodes = corners → spacing ~60–65 m

• 6 nodes = corners + midpoints → spacing ~40–50 m

This makes sense only if we are measuring:

• microclimate averages (temp/humidity/leaf wetness trends)

• irrigation zone-level soil moisture (drip zones / patches)

When 4–6 is not enough!

If the farm has:

• Uneven soil type (clay + rocky patches)

• Slope variations / water pooling

• Different crop varieties in same acre

• Multiple irrigation lines

• Disease hot-spots (common in humid pockets)

Then we may need 8–12 nodes (or more), because crop stress can be highly localized!

Connecting sensors to Infineon PSOC™ 6 AI Evaluation Kit

Typical wiring:

• Soil moisture: Analog voltage via Capacitive Soil Moisture probe to be channelled to PSOC ADC 1

• Leaf wetness: Analog or resistive measurement using Leaf Wetness/Humidity sensor via PSOC ADC 2

• Temp/RH: Microclimate measurement via BME280 sensor (I²C)

• Optional sensors: Soil Temperature sensor (Useful for indicating Root stress and scheduling Irrigation needs)

Inference scheduling on Infineon PSOC™ 6 AI Evaluation Kit

Sensor sampling:

• Temp/RH: every 5–10 minutes

• Soil moisture: every 15–30 minutes

• Leaf wetness: every 15–30 minutes during the day and 5-10 minutes during evening/night (fungal conditions)

Inference runs:

• Every 30–60 minutes normally

• Every 10–15 minutes during “risk windows” (high humidity + wet leaves + warm temps)

Data storage:

Instead of raw readings, we shall store:

• Hourly averages

• Min/Max

• Risk Score for Disease (0–100)

• Water Stress Score (0–100)

• A few recent raw points (last 30–60 minutes) for debugging

This keeps transfers fast and leads to a longer Battery life.

Conclusion

Edge AI, when thoughtfully deployed, can help India move from reactive crop protection to predictive crop management thereby improving yield, conserving water, reducing pesticide overuse, and building a more resilient agricultural ecosystem.

Supporting External Links:

https://documentation.infineon.com/psoc6/docs/hsg1651214227031

https://www.infineon.com/evaluation-board/CY8CKIT-062S2-AI

https://www.sciencedirect.com/science/article/pii/S016819232500019X

https://www.arable.com/wp-content/uploads/2022/05/Arable_Leaf_Wetness_2021_12.pdf