=Paper=
{{Paper
|id=Vol-3058/paper77
|storemode=property
|title=Plant Recommendation System With The Use Of Weather Forecasting
|pdfUrl=https://ceur-ws.org/Vol-3058/Paper-109.pdf
|volume=Vol-3058
|authors=Maulik Dhamecha
}}
==Plant Recommendation System With The Use Of Weather Forecasting==
https://ceur-ws.org/Vol-3058/Paper-109.pdf
Plant Recommendation System with the Use of Weather
Forecasting
Maulik Dhamecha1
1
Department of Computer Engineering, VVP Engineering College, Rajkot, Gujarat, India
Abstract
Planting is enjoyed by many people, but somehow their plants do not grow properly, one of
the reasons for the poor growth of their own plants is the favorable weather conditions for the
plants. Climate factors affect the growth rate of plants. The effect of weather on plants varies
between plant species during their lifetime. For each species, there is a defined range of
maximum, maximum and minimum temperatures for their growth. It is therefore important to
know which plant is suitable for growth in these climatic conditions. The weather changes as
the user's location changes. In this article, an architecture is proposed that recommends plants
based on the monthly weather of the user's location. In addition, different climatic effects
such as the effect of temperature, the effect of humidity, the effect of rainfall on different
plants are discussed.
Keywords 1
Climate, temperature, humidity
1. Introduction
Plants are more responsive to climate fluctuations than animals. They are also unable to find hot or
cold places. As the temperature rises, the plants grow taller to cool. The stems of plants are longer and
their leaves shrink and grow further, which reduces the nutritional value of plants and their function.
In addition, extreme temperatures, declining water availability, and changes in soil conditions actually
make plant growth more difficult. Climate change is expected to limit plant growth. Climate change
affects the various changes that determine the amount of plants that can grow. According to the study,
which relies on analysis of satellite data and weather forecasts, a 7% drop in the average number of
cold days will actually contribute to plant growth. The tropics can lose up to 200 days per year.
Climate change can affect agriculture through the effects of crops, weeds, soil, pests and diseases.
In crops, the major climate changes that are involved are temperature, sunlight, precipitation and
atmospheric CO2 concentration. In this article, we propose an architecture that tells the user which
plant is suitable for growing in a user-defined area. In nature, there is an interaction between climate
factors and they all affect each other. In a controlled environment, ie in a nursery, temperature is the
most influential factor in this interaction. In extreme temperatures and humidity, or when it is too dry
or humid or too hot or too cold, growth stops, which can kill plants if conditions persist. Therefore,
environmental conditions play an important role in the growth potential of plants and in the general
health of plants.
Plant growth is declining day by day and it has been observed that the main cause of most plants is
the influence of climate factors on them. Thus, it is important to plant in an area where the
environment is conducive to them. Our architecture determines the optimal temperature and plants of
International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07, 2021,
NITTTR Chandigarh, India
EMAIL: mvdhamecha@mail.com (A. 1)
©️2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
a particular area by mining data. We use GPS to find the geographical coordinates of the user's
location in the form of verticals and latitudes.
We use the Weather Forecast API to find the client parameters for that particular location provided
by the user. From this setting and using the database, our model will recommend plants for that
particular location. The main purpose behind creating this model is to encourage people who really
want to plant so that they do not get frustrated if their plant does not give much effect and growth
even after planting a lot. '' Information and especially because this type of situation is an unfavorable
climate for their plants. So this model will help these people to plant their plants.
2. Method and Architecture
In our model, we use the Weather Forecast API from Openweathermap.org. There are other
weather forecasting APIs, but the main disadvantage of all these APIs is that they cannot predict the
current month's weather. In addition, some APIs cannot predict the weather in all cities. When user
will turn on his location, from GPS system model will get the current location of the user. Extracted
location from GPS system will be given to weather forecast API.
Figure 1: Basic flow of system
The open weather map compares all the historical measurements of the current month with the
climate measurements for the month. So by using web scrapping in python we will give input in lat
and lon variable as latitude and longitude geographic coordinates of user location and based on that
we can fetch the monthly information using result set in that average temperature using variable
temp:mean, average humidity using variable humidity:mean , average precipitation using
precipitaion:mean. Depends on the climatic condition, we are considering three parameters which are
important factor for the growth of plant.
• Humidity is the amount of water vapor in the air at a given temperature, and is expressed
as a percentage. Moisture levels are important to allow the plant to continue its metabolic
processes at the required rates. Seeds germinate faster at higher humidity levels, as in the
case of cuttings.
• If heat and light, which cause the temperature to rise, are not properly controlled, plants
can suffer heat loss. Heat is used to increase humidity in rooms, by wetting trays, and to
wet the floor.
• Rainfall can determine how fast a crop grows from seed, including soil health. Some
plants need a good amount of rain, while others do not.
3. Experiment
Now using the ranking algorithm, we are going to categorize each feature which is temperature,
humidity and precipitation and give ratings based on its value. Rating will be given in 5 categories
which are Extreme, High, Medium, Low and Very Low. For example, if the temperature is around 20
degrees Celsius, this is considered a moderate range.
Table 1
Grading of temperature and humidity
Parameter Temperature range Humidity
(in kelvin) (in percentage)
Extreme 313 + 70+
High 298 – 313 55-70
Moderate 285 – 298 45-55
Low 263-285 30-45
Very low Less than 263 Less than 30
Categorized data available from grading algorithm is used for data mining of plants database.
Recommender system is used for extracting the plants which is suitable to grow at categories climate
measurement (Temperature, Humidity and Precipitation).
There are three types of recommendation systems: content-based recommendation, collaboration-
based recommendation, and hybrid recommendation. In our architecture, a content-based referral
system is used. If additional information is available, the recommended template content-based
method is used. And we have climatic measurements classified as user's current location and
additional information. Two other support-based recommendation methods are used if similarities are
identified between users based on their ratings and hybrid recommendation is a combination of two
other recommendation systems. Grouping is done for future analysis. The recommender will then
make the best recommendations for the plant that will suit the user's weather and if the user wants,
they can filter to their liking.
4. Result Analysis
For result we are considering the specific location that is Ahmedabad. From GPS, the geographic
coordinates for the location are stored in the variable latitude and longitude. And by using statistics
API in openweathermap and using web scraping we gave lat in lon as input variable in the query
string of the link and extract the temp:mean, humidity:mean and precipitaion:mean value.
Table 2
Variable information
Variable Value Input/output Description
lat 22.258651999999998 Input Latitude
lon 71.1923805 Input longitude
temp:mean 300.02 Output Mean
temperature in
kelvin
humidity:mean 81 Output Mean humidity in
percentage
precipitaion:mea 674 Output Mean
n precipitation in
mm
By grading the output, according to table 1, we are having temperature as high grade and humidity
with extreme grade. Crops are highly dependent on precipitation and indoor plants and succulents are
most of the independent of humidity. On analyzing the sample data table which is shown in below
table, we have found our match as neem tree and Downy Jasmine. Then recommender will give the
recommend from the database.
Table 3
Sample database
Plant name Type Temperature Humidity
Jade plant Succulent Plant High, Moderate Low
Northern red oak Tree Low, Moderate High, Extreme
Plum tree Tree Low Extreme
Neem tree Tree Moderate, High Extreme
Four O'Clock Plant Herbaceous
Extreme Low, Moderate
perennial
Euphorbia milli Succulent Shrub Low Low
Downy Jasmine Elegant Plant High, Moderate Extreme
5. Conclusion
According to our specific location, the recommended plants are neem and downy jasmine plants. If
the user wants, he can filter to his liking. For example, if you want to recommend a tree more than a
neem tree and if you want a more beautiful plant than jasmine.
But as we can see, soil plays a very important role, especially in planting crops. Soil temperatures
are calculated from the Earth's Surface Temperature (LST) and the Normalized Difference Vegetation
Index (NDVI) obtained from images of the MODIS satellite sensor aboard NASA's Aqua / Terra
spacecraft.
6. References
[1] Ali Raza,Ali Razzaq,Sundas Saher Mehmood, Xiling Zou,Xuekun Zhang,Yan
Lv,and Jinsong Xu, “Impact of Climate Change on Crops Adaptation and Strategies to
TackleIts Outcome,Plants(Basel)”,v.8(2),2019,publish online.
[2] Goral Godhani, Maulik Dhamecha, “A Study on Movie Recommendation System Using Parallel
MapReduce Technology”, IJEDR (2017)
[3] Maulik Dhamecha, Dr. Amit Ganatra, Dr. C. K. Bhensadadiya, “Comprehensive Study of
Hierarchical Clustering Algorithm and Comparison with Different Clustering Algorithms”, CiiT
(2011)
[4] F.O.Isinkaye, Y.O.Folajimi,B.A.Ojokho,Recommendation systems:Principles,methods
andevaluation,Egyptian Informative Journal,v(16),2015,261-273.
[5] Muzamil Malik1,Amna Ikram2,Syeda Naila Batool2 , and Waqar Aslam “A Performance
Assessment Of Rose Plant Classification Using Machine Learning” Springer 2019
[6] Hossam M. Zawbaa, Mona Abbass, Sameh H. Basha, Maryam Hazman, Abul Ella Hassenian
“An Automatic Flower Clssification Approach Using Machine Learnings”,IEEE,1 Dec 2014
International Conference On Advance in Computing, Communication and Imformatics,24-27
Sep. 2014
[7] Dhaval Chandarana, Maulik Dhamecha, “A Survey for Different Approaches of Outlier
Detection in Data Mining”, IEEE (2015)
[8] Maulik Dhamecha, Dr. Tejas Patalia, “Fundamental Survey of Map Reduce in Bigdata with
Hadoop Environment”, Spinger – CCIS (2018)
[9] Krishna Parmar, Nivid Limbasiya, Maulik Dhamecha, “Feature based Composite Approach for
Sarcasm Detection using MapReduce”, IEEE (2018)
[10] Neda Ahmadi , Mehrbakhsh Nilashib , Sarminah Samad , Tarik A. Rashid , Hossein Ahmadi “An
intelligent method for iris recognition using supervised machine learning techniques” Elsevier
2019
[11] Riddhi H. Shaparia1 , Dr. Narendra M. Patel2 and Prof. Zankhana H. Shah3 “Flower
Classification Using Texture and Colour Features”,International Conference on Research and
Innovations in Science, Engineering &Technology ,ICRISET 2017
[12] Maulik Dhamecha, Dr. Tejas Patalia, “MapReduce Foundation of Big data with Hadoop
environment”, ELSEVIER - SSRN (2018)
[13] Maulik Dhamecha, Krupa Dobaria, Dr. Tejas Patalia, “A Survey on Recommendation System for
Bigdata using MapReduce Technology”, IEEE (2019)
[14] Busra Rumesya Mete, Tolga Ensari “Flower Classification with Deep CNN and Machine
Learning Algorithm ”, IEEE 2019
[15] Maulik Dhamecha, Dr. Tejas Patalia, “Comparative study of Dynamic Load Balancing algorithm
in large scale data (Big data)”, IJAST (2020)
[16] Jhon Edwin Vera Vera, Sergio Mora Martinez, Andrés Torres Pérez, Jonathan Avendano
“Classification of Gerbera Type Flowers Based in Decision Tree Rules” IEEE 2019
[17] Riddhi H.Shaparia1* , Narendra M. Patel 2 , Zankhana H.Shah 3 “Flower Classification using
Different Color Channel” International Journal of Scientific Research in Computer Science and
Engineering ,IJSRCSE April 2019
[18] https://en.wikipedia.org/wiki/Anaconda_(Python_distribution)
[19] https://docs.conda.io/en/latest/
[20] https://en.wikipedia.org/wiki/Project_Jupyter
[21] Python-(programming language - Wikipedia