Published February 27, 2019 © GPL3+

Internet of Things and Data Analytics in Chili Plantation

Get detailed topographic information, fertility rates, homogeneity to soil temperature, and even measure weather in real-time.

AdvancedFull instructions providedOver 2 days1,920

Internet of Things and Data Analytics in Chili Plantation

Things used in this project

Hardware components

Espressif Wemos D1 Mini

Raspberry Pi 3 Model B

DHT22 Temperature Sensor

Seeed Studio Grove - Light Sensor

Maxim Integrated DS18B20 Programmable Resolution 1-Wire Digital Thermometer

SparkFun Soil Moisture Sensor (with Screw Terminals)

NXP Rapid IoT Prototyping Kit

Software apps and online services

Node-RED

Arduino IDE

Story

Currently research in agriculture has utilized a variety of modern technology tools that aim to help increase plant productivity to reach its maximum potential. One technology that is widely used is the Internet of Things (IoT). The application of IoT in the field of precision agriculture is able to facilitate the supervision of production land through internet networks that connect to other electronic devices such as smartphones. The use of sensors and internet networks that are applied in agriculture allows farmers in real-time to obtain detailed topographic information, fertility rates, acidity to soil temperature, and even be able to measure weather and predict weather patterns for the next few days.
Red chili is a type of vegetable which has high economic value. In recent years there has been a surge in the price of red chilli peppers on the market due to low supply caused by declining productivity due to the influence of climate change and attacks by plant pest organisms (OPT). OPT is still one of the obstacles in the cultivation of red chili both in the lowlands and in the highlands. Pests that commonly attack red chili in West Java are armyworms (Spodoptera litura) and trips as the main pests in the dry season, while in the rainy season are viral diseases and anthracnos (Colletotrichum) which can cause 100% damage to chili fruit. While the market demand for red chili from year to year continues to increase. However, the threat of an OPT attack can frustrate the harvest throughout the year. This is because the cultivation of red chili plants is generally carried out in open land which is susceptible to pest attacks. Erratic climatic conditions cause attacks of pests and diseases that occur all the time. In addition, red chili plants belonging to the C3 plant group do not require full sunlight. Increasing the temperature and intensity of sunlight causes the red chili plants to not grow optimally.
But not many results of research in the field of agriculture provide an interpretation of the relationship between one physical quantity with other measured physical quantities and their influence on the surrounding plants. For example, the dependence of air temperature on the content of water vapor around the plant has an impact on the plant's height. This interpretation plays an important rule in providing an understanding of what is needed by plants at a certain time, so that with this interpretation one can predict what needs to be given to the plant, so that the plants produced are expected to achieve maximum productivity. For this reason, in this study, we tried to interpret the relationship between a physical quantity as measured by other magnitudes on the growth of plants through the data we obtained from research. Especially for this study, the sample of the plants we took was chili. Indonesia ranks 4th as the world's largest chili producer, this is an interest for us to research and develop IoT applications to analyze the relationship between physical quantities in the air such as temperature, humidity, light intensity and physical quantities on the soil such as soil temperature and fertility on the growth of chili plants.

We try to implement the Internet of Things (IoT) technology that is capable of monitoring every day on the desired chili plant. The duration of monitoring can be set in such a way as well as the data from monitoring results are also directly connected and actually recorded on a laptop / pc. So we hope this IoT technology can update chili farmers' information about the condition of their chilli plants, and be able to provide early warning to farmers to immediately prevent the growth and development of pests and diseases.

In Figure, the tool provides data records that are quite good and in accordance with the real conditions in the field. The blue line informs the humidity of the air around the chili plants, where the air humidity in this location is quite volatile and tends to be of low value. From three days monitoring the value of air humidity tends to be in a low condition which is below 60%, while the ideal air humidity is at 60% to 80%. The yellow line shows the value of the intensity of sunlight. The recorded value is quite volatile as well because there are quite a lot of cloud conditions around the chilli plant area. Furthermore, for the temperature shown by the green line, the temperature conditions are quite hot, which is in the range of 30 to 40 Celsius. While the ideal condition of the temperature for chili plants is 21 to 27 centigrade. The value of this air temperature will be directly proportional to the temperature of the soil. The value of the soil moisture itself is inversely proportional to the temperature value. The higher the air temperature, the lower the value of soil moisture.

For more details we try to map the distribution of values from air humidity, sunlight intensity and air temperature.

From the 3 histogram images, it can be concluded that the chili crop land is more often in the condition of hot air with high sunlight intensity and relatively low air humidity, especially on the third day, which is shown by the green color on the histogram above. On the third day the air humidity is in the range of 25% to 35%, of course this is very far from the ideal condition (60% to 80%), the sun's heat on the third day is also very hot and the temperature is also quite high even more than 40 centigrade. Based on this condition, it can provide an early warning to farmers that chili plants are in a vulnerable condition to be attacked by pests. So that prevention needs to be done before pests begin to grow and develop.

Code

air and soil sensor

Arduino

The sensor network system consisting of the ESP8266 microcontroller and the Node-RED programmed in the Linux operating system (OS) on the computer is used to collect data on air temperature, air humidity, light intensity, soil temperature and soil fertility. Sensor data transmission uses Message Queuing Telemetry Transport (MQTT) type protocol to a gateway programmed using Node-RED. By using the MQTT protocol, communication between the microcontroller and mobile apps or computers can be done wirelessly.

#include <PubSubClient.h>
#include <ESP8266WiFi.h>
#include <Adafruit_Sensor.h>
#include <DHT.h>
#include <Wire.h>
#include <BH1750.h>
#include<OneWire.h>
#include<DallasTemperature.h>

//library JSON

#define DHTPIN D5
#define DHTTYPE DHT22
#define ONE_WIRE_BUS D4

OneWire oneWire(ONE_WIRE_BUS);
DallasTemperature sensors(&oneWire);

const char* ssid = "";
const char* password = "";
const char* mqtt_server = "";


WiFiClient espClient;
PubSubClient client(espClient);
long lastData = 0;
DHT dht(DHTPIN, DHTTYPE);
BH1750 lightMeter;


void setup_wifi() {

  delay(10);
  Serial.println();
  Serial.print("Connecting to ");
  Serial.println(ssid);
  //WiFi.mode(WIFI_STA);
  WiFi.begin(ssid, password);

  while (WiFi.status() != WL_CONNECTED) {
    delay(500);
    Serial.print(".");
  }

  randomSeed(micros());

  Serial.println("");
  Serial.println("WiFi connected");
  Serial.println("IP address: ");
  Serial.println(WiFi.localIP());
}

void callback(char* topic, byte* payload, unsigned int length) {

  Serial.print("Message arrived [");
  Serial.print(topic);
  Serial.print("] ");

  for (int i = 0; i < length; i++) {

    Serial.print((char)payload[i]);
  }
  Serial.println();


  if ((char)payload[0] == '1') {
    //digitalWrite(D2, HIGH);

  }

  if((char)payload[0] == '0'){
    
    //digitalWrite(D2, LOW);
  }

}

void reconnect() {

  while (!client.connected()) {

    Serial.print("Attempting MQTT connection...");
    String clientId = "ESP8266Client-";
    clientId += String(random(0xffff), HEX); 

    if (client.connect(clientId.c_str())) { 

      Serial.println("connected");
      //client.publish("output/bacaSensor", "sensor value");
      client.subscribe("input/kendaliAktuator");

    } else {
      
      Serial.print("failed, rc=");
      Serial.print(client.state());
      Serial.println(" try again in 5 seconds");

      delay(5000);
      
    }
  }
}

void setup() {

  pinMode(D5, OUTPUT);
  Serial.begin(9600);
  dht.begin();
  Wire.begin();
  lightMeter.begin();
  sensors.begin();
  setup_wifi();
  client.setServer(mqtt_server, 1883);
  client.setCallback(callback);
}

void loop() {

  if (!client.connected()) {
    
    reconnect();
  }

  client.loop();

  float h = dht.readTemperature();
  float t = dht.readHumidity();
  
  if(isnan(h) || isnan(t)){

    Serial.println("Failed to read from DHT sensor");
    return;
  } 
  
  float tS = sensors.getTempCByIndex(0);
  float l = lightMeter.readLightLevel();
  int adcSoil = analogRead(A0);
  sensors.requestTemperatures();
  float soilHumidity = 0;
  soilHumidity = ((1023.0 - adcSoil)/1023.0)*100;
  
  String temperature = String(t);
  String humidity = String(h);
  String intensity = String(l);
  String soilTemp = String(tS);
  String soilMoisture = String(soilHumidity);
  
  
  String payload = "{\"airTemperature\":";
  payload += temperature;
  payload += ",\"airHumidity\":";
  payload += humidity;
  payload += ",\"intensity\":";
  payload += intensity;
  payload += ",\"soilTemperature\":";
  payload += soilTemp;
  payload += ",\"soilMoisture\":";
  payload += soilMoisture;
  payload += "}";
  
  char dataSensor[250];
  
  long now = millis();

  if (now - lastData > 15000) {

    lastData = now;
    payload.toCharArray(dataSensor, 250);
    client.publish("output/bacaSensor", dataSensor);
    Serial.println(dataSensor);

   
  }
}

# Get this figure: fig = py.get_figure("https://plot.ly/~widya.meiriska/35/")
# Get this figure's data: data = py.get_figure("https://plot.ly/~widya.meiriska/35/").get_data()
# Add data to this figure: py.plot(Data([Scatter(x=[1, 2], y=[2, 3])]), filename ="all in", fileopt="extend")
# Get y data of first trace: y1 = py.get_figure("https://plot.ly/~widya.meiriska/35/").get_data()[0]["y"]

# Get figure documentation: https://plot.ly/python/get-requests/
# Add data documentation: https://plot.ly/python/file-options/

# If you're using unicode in your file, you may need to specify the encoding.
# You can reproduce this figure in Python with the following code!

# Learn about API authentication here: https://plot.ly/python/getting-started
# Find your api_key here: https://plot.ly/settings/api

import plotly.plotly as py
from plotly.graph_objs import *
py.sign_in('username', 'api_key')
trace1 = {
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  "y": ["53.7", "52.1", "49.5", "46.8", "45.9", "46.8", "44.5", "43.1", "42.9", "40.7", "40.7", "40.1", "38.8", "39.2", "39.8", "40.1", "41.8", "40.2", "37.5", "39.2", "38.6", "39.3", "36.8", "37.1", "38.8", "39.1", "39.3", "39.1", "39.6", "41.2", "43.1", "44.5", "45.4", "47.3", "49.4", "51", "52.9", "51.9", "53.8", "54.5", "53.7", "53.1", "53.5", "53.5", "53.8", "55", "56.6", "56.6", "56.5", "56.7", "57.7", "57.6", "57.1", "58.3", "58.5", "58.3", "58.9", "58.1", "59", "59.5", "59.1", "60.2", "61.1", "65.1", "64.1", "63", "60", "62.7", "58.9", "57.3", "55.8", "56.5", "57.4", "59.2", "58.7", "58.8", "56.4", "54.5", "53.2", "52.5", "54.6", "54.7", "55", "54", "55.2", "53.9", "53.7", "54.5", "54.5", "57.5", "56", "54", "55.1", "54.7", "51.8", "51.3", "51.3", "53.6", "52.7", "52.9", "53", "54", "55.9", "56.7", "55.7", "55.4", "55.7", "56.3", "56", "56.1", "55.6", "55.3", "55.8", "55.1", "56.3", "55.6", "55.2", "55.4", "56", "54.8", "54.6", "54", "53.2", "52.6", "52.9", "52.6", "52.6", "53.1", "52.6", "51.8", "51.6", "52.2", "51.3", "51.8", "53", "52.4", "52.7", "51.9", "51.4", "52.6", "52.7", "53.8", "54.5", "54.6", "56.8", "56", "55.7", "55.2", "54.5", "54.1", "53.3", "53.8", "53.8", "55.2", "55.5", "55.9", "54.8", "55.5", "55.8", "57.1", "58.8", "62.4", "59.6", "57.6", "56.4", "56.2", "56.7", "56.5", "55.9", "56.7", "57.5", "57.9", "58.7", "58.4", "58.8"], 
  "mode": "lines", 
  "name": "Kelembaban udara ke 1", 
  "type": "scatter", 
  "xsrc": "widya.meiriska:32:f5f5ce", 
  "ysrc": "widya.meiriska:32:5e5dea"
}
trace2 = {
  "x": ["8/15/2018 10:38:00.710000000 AM", "8/15/2018 10:38:22.330000000 AM", "8/15/2018 10:38:43.950000000 AM", "8/15/2018 10:39:05.570000000 AM", "8/15/2018 10:39:27.190000000 AM", "8/15/2018 10:39:48.810000000 AM", "8/15/2018 10:39:59.620000000 AM", "8/15/2018 10:40:21.240000000 AM", "8/15/2018 10:40:42.860000000 AM", "8/15/2018 10:41:04.480000000 AM", "8/15/2018 10:41:26.100000000 AM", "8/15/2018 10:41:47.720000000 AM", "8/15/2018 10:42:09.340000000 AM", "8/15/2018 10:42:20.150000000 AM", "8/15/2018 10:42:41.770000000 AM", "8/15/2018 10:43:03.390000000 AM", "8/15/2018 10:43:25.010000000 AM", "8/15/2018 10:43:46.630000000 AM", "8/15/2018 10:44:08.250000000 AM", "8/15/2018 10:44:29.870000000 AM", "8/15/2018 10:44:40.680000000 AM", "8/15/2018 10:45:02.300000000 AM", "8/15/2018 10:45:23.920000000 AM", "8/15/2018 10:45:45.540000000 AM", "8/15/2018 10:46:07.160000000 AM", "8/15/2018 10:46:28.780000000 AM", "8/15/2018 10:46:50.400000000 AM", "8/15/2018 10:47:12.020000000 AM", "8/15/2018 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  "y": ["20.88", "20.88", "20.88", "20.88", "20.88", "20.88", "20.94", "20.94", "20.94", "20.94", "20.94", "20.94", "20.94", "20.94", "20.94", "20.94", "21", "21", "21", "21", "21", "21", "21", "21.03", "21.06", "21.06", "21.06", "21.06", "21.06", "21.06", "21.06", "21.12", "21.09", "21.12", "21.12", "21.12", "21.12", "21.12", "21.19", "21.19", "21.19", "21.19", "21.19", "21.19", "21.19", "21.19", "21.19", "21.25", "21.25", "21.25", "21.25", "21.25", "21.25", "21.25", "21.28", "21.31", "21.31", "21.31", "21.31", "21.31", "21.34", "21.31", "21.37", "21.37", "21.37", "21.37", "21.37", "21.37", "21.37", "21.37", "21.37", "21.37", "21.37", "21.44", "21.44", "21.37", "21.44", "21.44", "21.44", "21.44", "21.44", "21.44", "21.44", "21.44", "21.44", "21.5", "21.47", "21.5", "21.5", "21.5", "21.5", "21.5", "21.5", "21.5", "21.5", "21.5", "21.53", "21.56", "21.56", "21.56", "21.56", "21.56", "21.56", "21.56", "21.59", "21.62", "21.62", "21.62", "21.62", "21.62", "21.62", "21.62", "21.69", "21.655", "21.655", "21.69", "21.69", "21.69", "21.69", "21.69", "21.69", "21.69", "21.75", "21.75", "21.75", "21.75", "21.75", "21.75", "21.75", "21.78", "21.78", "21.81", "21.81", "21.81", "21.81", "21.81", "21.81", "21.81", "21.81", "21.81", "21.84", "21.81", "21.81", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.87", "21.905", "21.905", "21.94", "21.94", "21.94", "21.94", "21.94", "21.94", "21.94", "21.94", "21.94", "22", "22", "22", "22", "22", "22", "22", "22", "22", "22", "22", "22", "22", "22.03", "22.03", "22.06", "22.03", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.06", "22.13", "22.13", "22.13", "22.13", "22.13", "22.13", "22.13", "22.13", "22.13", "22.19", "22.16", "22.19", "22.19", "22.19", "22.19", "22.19", "22.19", "22.19", "22.22", "22.25", "22.25", "22.25", "22.25", "22.25", "22.25", "22.25", "22.25", "22.25", "22.25", "22.25", "22.25", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.31", "22.34", "22.34", "22.37", "22.37", "22.37", "22.37", "22.37", "22.37", "22.37", "22.405", "22.44", "22.44", "22.44", "22.44", "22.44", "22.44", "22.44", "22.44", "22.44", "22.5", "22.5", "22.5", "22.5", "22.5", "22.5", "22.5", "22.5", "22.5", "22.5", "22.5", "22.53", "22.56", "22.56", "22.56", "22.56", "22.56", "22.56", "22.56", "22.56", "22.56", "22.56", "22.56", "22.56", "22.62", "22.59", "22.62", "22.62", "22.62", "22.62", "22.62", "22.62", "22.62", "22.62", "22.62", "22.62", "22.655", "22.69", "22.655", "22.69", "22.69", "22.69", "22.69", "22.69", "22.69", "22.69", "22.72", "22.75", "22.75", "22.75", "22.75", "22.75", "22.75", "22.75", "22.75", "22.75", "22.75", "22.75", "22.78", "22.81", "22.81", "22.81", "22.81", "22.81"], 
  "line": {
    "color": "rgb(255, 127, 14)", 
    "dash": "dot"
  }, 
  "mode": "lines", 
  "name": "Temperatur tanah hari ke 3", 
  "type": "scatter", 
  "xsrc": "widya.meiriska:34:4399bc", 
  "ysrc": "widya.meiriska:34:63c6fb"
}
data = Data([trace1, trace2, trace3, trace4, trace5, trace6, trace7, trace8, trace9, trace10, trace11, trace12, trace13, trace14, trace15])
layout = {
  "autosize": False, 
  "height": 700, 
  "hovermode": "closest", 
  "margin": {"b": 100}, 
  "showlegend": True, 
  "title": "<b>Monitoring Data Tanaman Cabe di Balitsa</b>", 
  "titlefont": {"size": 18}, 
  "width": 1200, 
  "xaxis": {
    "autorange": True, 
    "nticks": -1, 
    "range": [0, 810], 
    "tickangle": "auto", 
    "title": "<b>Waktu</b>", 
    "type": "category"
  }, 
  "yaxis": {
    "autorange": True, 
    "range": [1.78566666667, 68.4323333333], 
    "title": "<b>Nilai yang terukur</b>", 
    "type": "linear"
  }
}
fig = Figure(data=data, layout=layout)
plot_url = py.plot(fig)

# Get this figure: fig = py.get_figure("https://plot.ly/~widya.meiriska/2/")
# Get this figure's data: data = py.get_figure("https://plot.ly/~widya.meiriska/2/").get_data()
# Add data to this figure: py.plot(Data([Scatter(x=[1, 2], y=[2, 3])]), filename ="humidity", fileopt="extend")
# Get x data of first trace: x1 = py.get_figure("https://plot.ly/~widya.meiriska/2/").get_data()[0]["x"]

# Get figure documentation: https://plot.ly/python/get-requests/
# Add data documentation: https://plot.ly/python/file-options/

# If you're using unicode in your file, you may need to specify the encoding.
# You can reproduce this figure in Python with the following code!

# Learn about API authentication here: https://plot.ly/python/getting-started
# Find your api_key here: https://plot.ly/settings/api

import plotly.plotly as py
from plotly.graph_objs import *
py.sign_in('username', 'api_key')
trace1 = {
  "x": [53.7, 52.1, 49.5, 46.8, 45.9, 46.8, 44.5, 43.1, 42.9, 40.7, 40.7, 40.1, 38.8, 39.2, 39.8, 40.1, 41.8, 40.2, 37.5, 39.2, 38.6, 39.3, 36.8, 37.1, 38.8, 39.1, 39.3, 39.1, 39.6, 41.2, 43.1, 44.5, 45.4, 47.3, 49.4, 51, 52.9, 51.9, 53.8, 54.5, 53.7, 53.1, 53.5, 53.5, 53.8, 55, 56.6, 56.6, 56.5, 56.7, 57.7, 57.6, 57.1, 58.3, 58.5, 58.3, 58.9, 58.1, 59, 59.5, 59.1, 60.2, 61.1, 65.1, 64.1, 63, 60, 62.7, 58.9, 57.3, 55.8, 56.5, 57.4, 59.2, 58.7, 58.8, 56.4, 54.5, 53.2, 52.5, 54.6, 54.7, 55, 54, 55.2, 53.9, 53.7, 54.5, 54.5, 57.5, 56, 54, 55.1, 54.7, 51.8, 51.3, 51.3, 53.6, 52.7, 52.9, 53, 54, 55.9, 56.7, 55.7, 55.4, 55.7, 56.3, 56, 56.1, 55.6, 55.3, 55.8, 55.1, 56.3, 55.6, 55.2, 55.4, 56, 54.8, 54.6, 54, 53.2, 52.6, 52.9, 52.6, 52.6, 53.1, 52.6, 51.8, 51.6, 52.2, 51.3, 51.8, 53, 52.4, 52.7, 51.9, 51.4, 52.6, 52.7, 53.8, 54.5, 54.6, 56.8, 56, 55.7, 55.2, 54.5, 54.1, 53.3, 53.8, 53.8, 55.2, 55.5, 55.9, 54.8, 55.5, 55.8, 57.1, 58.8, 62.4, 59.6, 57.6, 56.4, 56.2, 56.7, 56.5, 55.9, 56.7, 57.5, 57.9, 58.7, 58.4, 58.8], 
  "autobinx": True, 
  "autobiny": True, 
  "name": "Hari ke 1", 
  "opacity": 0.75, 
  "type": "histogram", 
  "uid": "9c4ed06a-aa95-11e8-ba3f-c4461925a71b", 
  "xbins": {
    "end": 66, 
    "size": 2, 
    "start": 36
  }, 
  "xsrc": "widya.meiriska:3:84dd94"
}
trace2 = {
  "x": [34.7, 35.05, 35.15, 36, 36.15, 35.45, 34.25, 33.75, 34.5333333333, 33.6, 33.6, 34, 33.8, 34.95, 35.2, 34.8, 33.95, 32.45, 32.55, 31.9, 30.85, 30.6, 31.35, 31.4, 32.1333333333, 31.6, 31.1, 31.8, 31.4, 31.1, 30.3, 30.3, 30.4, 30.75, 31.7, 31.35, 31.45, 30.7, 30.45, 31.45, 32.2, 33.85, 34.05, 34.55, 34.6, 34, 34.1, 33.8666666667, 33.05, 32.6, 33.6, 34.9333333333, 35.7, 33.4333333333, 33.8, 33.4, 33.75, 33.3, 33.1, 33.8, 34.3, 34.9, 35.85, 36.3, 35.95, 37.4, 38.05, 37.95, 37.8, 37.7, 38.9, 39.95, 39.9, 41.25, 41.5, 41.8, 42.3, 42.4, 42.8, 42.5, 43.1, 43.6, 43.5, 43.45, 43.45, 43.75, 43.8, 43.65, 42.6, 41.8, 41.6, 43.4, 45.3, 46.1, 44.45, 44.9, 44.5, 43.55, 41.75, 41.1, 41.6, 42.7, 43.55, 44, 42.45, 40.5, 40.3, 39, 36.85, 36.4666666667, 37, 36, 35.7, 37.05, 37.05, 37, 35.15, 35.15, 35.2, 35.9, 34.85, 34.55, 34.7, 34.9, 36.9, 37.4, 37.65, 37.05, 36.25, 38.2, 40.05, 39.7, 40.3666666667, 41.9, 42.15, 42.55, 42.35, 42.6, 42.8, 43.2, 43.95, 45.5, 46.45, 46.65, 46.65, 47.45, 48.8, 49.1, 49.85, 50.15, 51.25, 52.6666666667, 52.7, 53.7, 53.8, 54.15, 54.5, 55, 55.55, 56, 56.2333333333, 56.7, 57.35, 57.5, 57.75, 58.05, 58.25, 59, 59.8, 59.75, 60, 60.1, 60.4, 60.9, 60.3, 59.5, 57.65, 56.95, 57.3, 56.4, 56.65, 56.7, 57.3333333333, 57.5, 58.1, 57.1, 56.8, 57.2, 57.7, 57.4, 57.75, 58.2, 58.45, 58.85, 59.6, 60.55, 60, 59.35, 60.5, 60.85, 62.1, 61, 61.05, 61.9, 62, 61.8, 61.9, 60.75, 60.4, 60.55, 61.85, 61.8, 62, 62.55, 60.8, 62.35, 60.6, 60.1, 59.45, 55.6, 54.9, 53.65, 52.6666666667, 52.5666666667, 55.1666666667, 55.25, 54.9, 55.25, 56, 56.3, 54.3, 53.7, 53.55, 52.1, 52.35, 54.75, 56.45, 57.3, 56.4, 56.55, 57.2, 56.05, 55.2, 56.1, 55.75, 53.95, 51.5, 51.9, 52.15, 50.75, 50.15, 50.4, 50.5, 47.5, 46.8, 47.2, 46.4, 45.8, 43.5, 43.1, 42.5333333333, 44.75, 44.95, 44.4, 44.25, 43.05, 41.2, 41.14, 40.75, 39.2, 39.2, 39, 39.8, 39.8, 40.05, 39.9333333333, 39.75, 40.35, 40.1, 40.15, 40.3, 38.95, 39.3, 38.6, 37.85, 38.1, 37.7, 37.4, 38.6666666667, 39, 38.15, 38.1, 38.6, 38.5, 37.8, 38.6, 37.4, 37.65, 37.9, 36.55, 37.1, 37.5], 
  "autobinx": True, 
  "autobiny": True, 
  "name": "Hari ke 2", 
  "opacity": 0.75, 
  "type": "histogram", 
  "uid": "9c4ed06b-aa95-11e8-ba3f-c4461925a71b", 
  "xbins": {
    "end": 65, 
    "size": 5, 
    "start": 30
  }, 
  "xsrc": "widya.meiriska:3:87d54f"
}
trace3 = {
  "x": [29, 28.55, 27.35, 26.2, 25.75, 25.3, 25.05, 24.7, 24.95, 24.9, 26.45, 26.35, 29.15, 30.95, 31.7, 32.2333333333, 33, 33.1, 32.2, 31.95, 31.95, 31.75, 32.05, 32.25, 31.9, 31.35, 29.95, 28.9, 29.65, 30.15, 31.1, 32.0666666667, 31, 30.15, 29.55, 29.6, 29.75, 29.75, 30.55, 31.05, 31.1, 30.55, 31.05, 31.3, 31.2, 31.45, 28, 26.15, 25.85, 25.3, 26.2, 26.9, 27, 27.05, 26.4, 27.1, 29.4, 30.1, 29.7, 31.4, 32, 32.3, 31.65, 32.35, 32.75, 31.8, 31.15, 30.65, 29.85, 27.9, 27.05, 26.35, 26.4, 27.25, 26.75, 26.2, 27.75, 29.15, 29.3, 28.7, 27.75, 26.8, 26.7, 26, 24.85, 24.55, 24.55, 24.8, 25.05, 25.5, 25.95, 26.45, 26.85, 26.9, 27.2, 27.45, 27.25, 27, 27.05, 26.85, 27.5, 27.75, 27.8, 27.6, 27.15, 26.9, 26.8666666667, 26.3, 26.45, 26, 25.8, 26.6, 26.7, 27.15, 27.3, 27.45, 27.7, 28.35, 29.35, 30.85, 31.85, 32.8, 32.75, 32.7, 32.35, 32, 32.4, 31.9, 31.7, 31.3, 30.2, 29.05, 28.85, 28.65, 28.75, 28.95, 28.8, 28.8, 29, 28.5, 28.4, 29.1, 30.25, 30.4, 30.15, 30.1, 28.95, 27.75, 27.7, 27.85, 27.55, 27.65, 28.1, 28.15, 28.4, 28.35, 28.25, 28, 28.05, 27.95, 28.2, 29, 28.8, 29.2, 29.1, 28.9, 27.8, 27.2, 26.95, 27.35, 27.35, 27.25, 27.1, 27.8, 28.2, 28.5, 28.65, 28.55, 27.4, 26.8, 26.7, 27.15, 27.3, 27.3, 26.95, 26, 25.4, 25.15, 24.6, 24.25, 24.65, 25.45, 25.75, 25.3, 25.75, 25.6, 25.35, 25.5, 25.3, 25.3, 25.4, 25.75, 26.5, 27.5, 29.05, 29, 29.1, 29.2, 28.55, 28.15, 28.6, 29.6, 30.55, 32, 32.5, 32.15, 31.9, 30.9, 30.45, 30.4, 30.1, 29.75, 29.7, 29.45, 29.4, 28.7, 27.6, 27.2, 27.05, 27.2, 27.5, 27.75, 27.5, 26.1, 26.2, 24.75, 23.75, 23.3, 23.3, 22.95, 23.15, 23.6, 23.85, 23.65, 24.3, 25.6, 26.4, 26.7, 25.25, 24.5, 25.4, 25.8, 26.15, 26.65, 26.5, 27.15, 26.05, 25.9, 26.85, 26.95, 27.95, 27.9, 28.65, 28.4, 28.6, 28.6, 28.2, 27.4, 28.15, 28.15, 28.15, 28.7, 28.75, 29.25, 30.05, 29.7, 29.55, 29.45, 28.25, 27.95, 27.75, 28.15, 28, 28.5, 27.85, 27.05, 26.3, 26.9, 28, 26.65, 27.05, 26.8, 27, 28.75, 29.05, 29.4, 29.1, 28.45, 28.25, 27.7, 27.05, 27.35, 27.6, 27.65, 27.15, 27.7, 27.6, 27.45, 27.15, 26.85, 27.15, 27.7333333333, 26.7, 26.5, 26.2, 25.75, 25.65, 25.8, 25.85, 25.4, 25.15, 25.55, 25.4, 26.3, 26.15, 26.35, 27.1, 27.2, 28.2, 29.05, 27.7, 27.1, 27.35, 27.6], 
  "autobinx": True, 
  "autobiny": True, 
  "name": "Hari ke 3", 
  "opacity": 0.75, 
  "type": "histogram", 
  "uid": "9c4ed06c-aa95-11e8-ba3f-c4461925a71b", 
  "xbins": {
    "end": 33.5, 
    "size": 0.5, 
    "start": 22.5
  }, 
  "xsrc": "widya.meiriska:3:3cc987"
}
data = Data([trace1, trace2, trace3])
layout = {
  "barmode": "overlay", 
  "dragmode": "pan", 
  "legend": {
    "x": 1.00476454294, 
    "y": 0.538235294118
  }, 
  "title": "&nbsp;&lt;b&gt;Persebaran Nilai Kelembaban Udara pada Tanaman Cabai di Balitsa&lt;/b&gt;&nbsp;", 
  "titlefont": {"size": 18}, 
  "xaxis": {
    "autorange": True, 
    "range": [22.5, 66], 
    "title": "<b>Kelembaban Udara</b>", 
    "type": "linear"
  }, 
  "yaxis": {
    "autorange": True, 
    "range": [0, 69.4736842105], 
    "title": "<b>Frekuensi</b>"
  }
}
fig = Figure(data=data, layout=layout)
plot_url = py.plot(fig)