Capstone & Resources · Study deck

Capstone Projects: Agriculture and Environment

A field node and an indoor monitor may share sensors but face different power, weather, and network limits.

Test Tessa is your guide for this deck.

projects

Test Tessa, the module guide, in a scene from this chapter.

After studying this chapter

Learning objectives

You will be able to:

Explain: Calibrated sensors establish the evidence, watering control acts on it, power and connectivity keep the loop available outdoors, the algorithm explains when action is permitted, and documentation preserves calibration, safety, and recovery steps.
Explain: With 80% charging efficiency, we get 20Wh/day, enough to run the system and charge a 18650 battery with about 11Wh capacity in roughly half a day, leaving margin for cloudy days.
Explain: The shared capstone approach (reviewable claims, the thin-slice method, and interface evidence) is introduced on the Capstone Projects hub; this part works through the Smart Agriculture System brief.

Major section

Start With the Decision

A field node and an indoor monitor may share sensors but face different power, weather, and network limits.

Each project brief must turn those limits into testable evidence.

Major section

Capstone Project 2: Smart Agriculture System

The shared capstone approach (reviewable claims, the thin-slice method, and interface evidence) is introduced on the Capstone Projects hub; this part works through the Smart Agriculture System brief.

The second brief adds actuation.
The system should work both indoors (houseplants) and outdoors (garden), with solar power capability for remote deployment.

Major section

Capstone Project 2: Smart Agriculture System (continued)

That changes the review question from "did the system observe correctly?" to "did the system act safely when soil, weather, water level, and schedule evidence disagreed?".

Your system should demonstrate closed-loop sensing and actuation while still protecting plants from unsafe automated decisions.
Solar power sizing requires matching daily energy consumption with panel output.
ESP32 + LoRa transmits once/hour (30s active at 200mA, rest at 5mA sleep).

Major section

Capstone Project 2: Smart Agriculture System (continued)

The safety logic includes dry and wet thresholds, a rain threshold, and a 6-hour minimum interval so one bad reading cannot drive repeated watering.

Step 2 — Node publishes readings over LoRa or Wi-Fi.
Step 3 — Gateway or cloud service stores readings and evaluates watering rules.
Together those records make the field claim reproducible.

Major section

Capstone Project 2: Smart Agriculture System (continued)

The evidence package should cover calibration, relay behavior, LoRa or Wi-Fi choice, solar margin for 3+ days without sun, and outdoor credential protection.

No category can compensate for an unsafe or unreviewable control path.
Those readings and actions must share a traceable zone and time context.
The result is a functional system within budget.

Major section

Capstone Project 2: Smart Agriculture System (continued)

The team identified must-have features for monitoring, control, and cloud visibility, then deferred solar, LoRa, and weather integration.

With 80% charging efficiency, we get 20Wh/day, enough to run the system and charge a 18650 battery with about 11Wh capacity in roughly half a day, leaving margin for cloudy days.
Calibrated sensors establish the evidence, watering control acts on it, power and connectivity keep the loop available outdoors, the algorithm explains when action is permitted, and documentation preserves calibration, safety, and recovery steps.
Smart irrigation projects show how IoT can reduce manual monitoring, improve watering consistency, and make resource use visible.

Major section

Capstone Project 1: Smart Environment Monitor

The first brief keeps the system relatively contained: indoor sensing, Wi-Fi/MQTT delivery, cloud storage, and a dashboard that makes room conditions visible.

Message Queuing Telemetry Transport (MQTT) is a lightweight method for sending named data streams through a message service.
A student build is not a certified safety system.

Major section

Capstone Project 1: Smart Environment Monitor (continued)

A bright chart is easy to build, but it can hide an unplugged sensor or a value with the wrong units.

The deeper sections guide design, parts, data flow, tests, and review so the final project states what it proved and what remains outside scope.
The system should provide real-time dashboards, historical analytics, and automated alerts when conditions exceed healthy thresholds.
Meeting the 7-day battery life target requires careful power budgeting.

Major section

Capstone Project 1: Smart Environment Monitor (continued)

Its separate 2 kohm by 100 pF sensing time constant is 200 ns.

Indoor environment monitoring helps facility teams spot poor ventilation, comfort problems, and air-quality trends before they become persistent operational issues.
Your system should turn raw sensor readings into clear dashboard signals and actionable alerts.
Step 1 — Sensors provide temperature, humidity, CO2, particulate, light, and noise readings.
Step 2 — ESP32 validates and publishes readings over Wi-Fi using MQTT.

Deck summary

Key takeaways

A field node and an indoor monitor may share sensors but face different power, weather, and network limits.

The shared capstone approach (reviewable claims, the thin-slice method, and interface evidence) is introduced on the Capstone Projects hub; this part works through the Smart Agriculture System brief.
That changes the review question from "did the system observe correctly?" to "did the system act safely when soil, weather, water level, and schedule evidence disagreed?".
The safety logic includes dry and wet thresholds, a rain threshold, and a 6-hour minimum interval so one bad reading cannot drive repeated watering.

Retrieval practice

Recall check 1 of 6

Test Tessa says: answer from memory, then check your reasoning.

Q1The hardware list specifies 'Capacitive (not resistive!)' soil moisture sensors. Why is this distinction critical for a long-term agricultural deployment?

ACapacitive probes are always factory-calibrated more accurately than resistive probes

BResistive probes can corrode as DC current passes through wet soil over time

CCapacitive probes usually cost less once waterproof cabling is included

DCapacitive probes automatically draw less power during every measurement

Show answer

Answer: B Correct!

Retrieval practice

Recall check 2 of 6

Test Tessa says: answer from memory, then check your reasoning.

Q2The smart watering algorithm above checks MIN_INTERVAL_HOURS before allowing watering. Why is this safeguard important even if soil moisture readings indicate dry conditions?

ATo cap daily water use even when every soil zone reports consistently dry

BTo prevent repeated watering from one bad dry reading

CTo allow time for the water to soak into the soil before measuring again

DTo synchronize watering times across multiple zones

Show answer

Answer: B Correct!

Retrieval practice

Recall check 3 of 6

Test Tessa says: answer from memory, then check your reasoning.

Q3The agriculture system offers 'LoRa or Wi-Fi connectivity' as options. For a field deployment 200 meters from the farmhouse with no Wi-Fi coverage, which factor makes LoRa the clear choice?

ALoRa is selected because it sends dashboard data faster than Wi-Fi

BLoRa provides longer range and lower power beyond Wi-Fi coverage

CLoRa simplifies deployment by connecting the field node directly to the cloud service

DLoRa is preferable because its protected telemetry path better suits watering commands

Show answer

Answer: B Correct!

Retrieval practice

Recall check 4 of 6

Test Tessa says: answer from memory, then check your reasoning.

Q4For the Smart Environment Monitor, you need to choose between the DHT22 ($5) and BME280 ($15) for temperature/humidity sensing. The BME280 also includes barometric pressure. Which factor should MOST influence your decision?

AAlways choose the cheaper option to minimize cost

BUse BME280 when I2C wiring and pressure context reduce risk

CUse the BME280 mainly because pressure readings may become interesting later

DUse the DHT22 because examples online remove most integration uncertainty

Show answer

Answer: B Correct!

Retrieval practice

Recall check 5 of 6

Test Tessa says: answer from memory, then check your reasoning.

Q5Looking at the MQTT topic structure above, why is each sensor value published to a separate topic (e.g., environment/{device_id}/temperature) rather than all values in a single JSON payload to environment/{device_id}/all?

ASingle-value topics use less bandwidth than JSON

BSubscribers can filter by sensor type without parsing unused fields

CMQTT brokers cannot route messages that contain multiple sensor fields

DQoS levels can only be configured when each value has its own topic

Show answer

Answer: B Correct!

Retrieval practice

Recall check 6 of 6

Test Tessa says: answer from memory, then check your reasoning.

Q6The Environment Monitor stores sensor data in InfluxDB (a time-series database) rather than a general relational schema. With 5 sensors publishing every 5 minutes, you'll have 525,600 data points per year. Which requirement most directly supports evaluating a time-series-oriented design?

AInfluxDB is free while PostgreSQL requires a license

BUse time-series retention, compaction, and downsampling

CPostgreSQL cannot store numeric sensor values

DInfluxDB provides built-in visualization that PostgreSQL lacks

Show answer

Answer: B Correct!

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Answers

Answer key.

B · Correct!
B · Correct!
B · Correct!
B · Correct!
B · Correct!
B · Correct!