物联网在养殖业领域的应用—

物联网在养殖业领域的应用——案例分析

作者主页:

知孤云出岫在这里插入图片描述

- ==作者主页==:
- 物联网在养殖业领域的应用——案例分析
- - 背景
  - 技术架构
  - 硬件设置
  - - 连接多种传感器到微控制器
  - 代码实现
  - - 1. Arduino代码：采集多种传感器数据并上传到Thingspeak
    - 2. Python代码：从Thingspeak获取数据并进行综合分析和可视化
  - 结果分析
  - 持续优化
  - 结论

物联网在养殖业领域的应用——案例分析

背景

养殖业在全球食品供应链中占有重要地位。然而，传统养殖方法面临诸多挑战，如疾病传播、饲料浪费、环境污染和高劳动成本。物联网（IoT）技术的应用为这些问题提供了新的解决方案，通过实时监控和数据分析，提高养殖效率和动物福利。本文将以一个综合案例展示物联网在养殖业中的应用，涵盖环境监测、健康监测、饲喂管理和数据分析。

技术架构

传感器：温湿度传感器（DHT22）、空气质量传感器（MQ135）、光照传感器（BH1750）、运动传感器（加速度计）。
微控制器：ESP8266，用于读取传感器数据并上传到云平台。
云平台：Thingspeak或类似平台进行数据存储和可视化。
数据分析：使用Python进行数据分析和可视化。

硬件设置

连接多种传感器到微控制器

使用DHT22传感器、MQ135空气质量传感器、BH1750光照传感器和ADXL345加速度计传感器与ESP8266微控制器进行数据采集。

DHT22 Sensor        ESP8266
----------------------------
VCC                 3.3V
GND                 GND
Data                D2 (GPIO4)MQ135 Sensor        ESP8266
----------------------------
VCC                 3.3V
GND                 GND
A0                  A0BH1750 Sensor       ESP8266
----------------------------
VCC                 3.3V
GND                 GND
SCL                 D1 (GPIO5)
SDA                 D2 (GPIO4)Accelerometer       ESP8266
----------------------------
VCC                 3.3V
GND                 GND
SCL                 D1 (GPIO5)
SDA                 D2 (GPIO4)

代码实现

1. Arduino代码：采集多种传感器数据并上传到Thingspeak

#include <ESP8266WiFi.h>
#include "DHT.h"
#include <Wire.h>
#include <BH1750.h>
#include <Adafruit_MQ135.h>
#include <Adafruit_MLX90614.h>
#include <Adafruit_Sensor.h>
#include <Adafruit_ADXL345_U.h>#define DHTPIN 4 // D2 pin
#define DHTTYPE DHT22const char* ssid = "your_SSID";
const char* password = "your_PASSWORD";
const char* server = "api.thingspeak.com";
const char* apiKey = "your_THINGSPEAK_API_KEY";DHT dht(DHTPIN, DHTTYPE);
WiFiClient client;
BH1750 lightMeter;
Adafruit_MQ135 mq135(A0);
Adafruit_ADXL345_Unified accel = Adafruit_ADXL345_Unified(12345);void setup() {Serial.begin(115200);delay(10);dht.begin();Wire.begin();lightMeter.begin();accel.begin();Serial.println("Connecting to WiFi...");WiFi.begin(ssid, password);while (WiFi.status() != WL_CONNECTED) {delay(500);Serial.print(".");}Serial.println("WiFi connected");
}void loop() {float h = dht.readHumidity();float t = dht.readTemperature();float lux = lightMeter.readLightLevel();float mq135Value = mq135.read();sensors_event_t event; accel.getEvent(&event);if (isnan(h) || isnan(t) || isnan(lux) || isnan(mq135Value)) {Serial.println("Failed to read from sensors!");return;}if (client.connect(server, 80)) {String postStr = apiKey;postStr += "&field1=";postStr += String(t);postStr += "&field2=";postStr += String(h);postStr += "&field3=";postStr += String(lux);postStr += "&field4=";postStr += String(mq135Value);postStr += "&field5=";postStr += String(event.acceleration.x);postStr += "&field6=";postStr += String(event.acceleration.y);postStr += "&field7=";postStr += String(event.acceleration.z);postStr += "\r\n\r\n";client.print("POST /update HTTP/1.1\n");client.print("Host: api.thingspeak.com\n");client.print("Connection: close\n");client.print("X-THINGSPEAKAPIKEY: " + String(apiKey) + "\n");client.print("Content-Type: application/x-www-form-urlencoded\n");client.print("Content-Length: ");client.print(postStr.length());client.print("\n\n");client.print(postStr);Serial.println("Temperature: " + String(t) + " °C");Serial.println("Humidity: " + String(h) + " %");Serial.println("Light: " + String(lux) + " lx");Serial.println("Air Quality: " + String(mq135Value));Serial.println("Acceleration X: " + String(event.acceleration.x));Serial.println("Acceleration Y: " + String(event.acceleration.y));Serial.println("Acceleration Z: " + String(event.acceleration.z));}client.stop();delay(20000); // 20 seconds delay between updates
}

2. Python代码：从Thingspeak获取数据并进行综合分析和可视化

首先，安装所需的Python库：

pip install requests pandas matplotlib

然后，使用以下Python脚本从Thingspeak获取数据，并进行综合数据分析和可视化：

import requests
import pandas as pd
import matplotlib.pyplot as plt# Thingspeak API URL
channel_id = 'your_CHANNEL_ID'
read_api_key = 'your_READ_API_KEY'
url_template = f'https://api.thingspeak.com/channels/{channel_id}/fields/{{field}}.json?api_key={read_api_key}&results=8000'# Fetch data from Thingspeak
def fetch_data(field):response = requests.get(url_template.format(field=field))data = response.json()timestamps = [entry['created_at'] for entry in data['feeds']]values = [float(entry[f'field{field}']) for entry in data['feeds'] if entry[f'field{field}']]return pd.DataFrame({'Timestamp': pd.to_datetime(timestamps), f'Field{field}': values}).set_index('Timestamp')temperature_df = fetch_data(1)
humidity_df = fetch_data(2)
light_df = fetch_data(3)
air_quality_df = fetch_data(4)
acceleration_x_df = fetch_data(5)
acceleration_y_df = fetch_data(6)
acceleration_z_df = fetch_data(7)# Merge dataframes
df = temperature_df.join([humidity_df, light_df, air_quality_df, acceleration_x_df, acceleration_y_df, acceleration_z_df])# Plot data
plt.figure(figsize=(15, 10))plt.subplot(3, 1, 1)
plt.plot(df.index, df['Field1'], label='Temperature (°C)')
plt.plot(df.index, df['Field2'], label='Humidity (%)')
plt.legend()plt.subplot(3, 1, 2)
plt.plot(df.index, df['Field3'], label='Light (lx)')
plt.plot(df.index, df['Field4'], label='Air Quality')
plt.legend()plt.subplot(3, 1, 3)
plt.plot(df.index, df['Field5'], label='Acceleration X')
plt.plot(df.index, df['Field6'], label='Acceleration Y')
plt.plot(df.index, df['Field7'], label='Acceleration Z')
plt.legend()plt.xlabel('Time')
plt.suptitle('Environmental Monitoring in Pig House')
plt.show()