Rajkumar
Rajasekaran
Vellore
Institute of Technology, India
E-mail: vitrajkumar@gmail.com
Rajendra
Agarwal
Vellore
Institute of Technology, India
E-mail: rajendra.agrawal2151@gmail.com
Aditya
Srivastava
Vellore
Institute of Technology, India
E-mail: vit.rajkumar@gmail.com
Jolly
Masih
Prestige
Institute of Engineering Management and Research, India
E-mail: jollyiabm@gmail.com
Volodymyr
Ivanyshyn
State
Agrarian and Engineering University in Podilya, Ukraine
E-mail: volodymyrivanyshyn55@gmail.com
Iryna
Yasinetska
State
Agrarian and Engineering University in Podilya,Ukraine
E-mail: yasinetska55@gmail.com
Submission: 8/16/2020
Revision: 8/31/2020
Accept: 9/14/2020
ABSTRACT
Agriculture is a backbone of the economy for any country. Being a part of primary sector, all the other major sectors and industries depend on it for their raw materials. It satisfies the basic needs of human like food, clothing and shelter. However, due to climate change and other related problems, it is becoming increasingly difficult for farmers to keep pace with rising demands. As per estimate by Food and Agricultural Organization of United Nations, around 55 percent of India’s total land area is used for agricultural produce. India is also a leading producer and exporter of some of the major crops. Still there are concerns regarding food security in India by United Nations. For overcoming the natural hurdles, involvement of technology is required for better analysis and decision-making. Through this paper, we plan to propose a visualization technique, which can help farmers to make better decision regarding crop selection. The study proposes a novel framework where farmers can get detailed information about the crops grown in any particular district and also area, production and productivity of any particular crop. This web-based agri solution will help farmers to take smart farming decision by resource optimization and smart planning.
Keywords: Data Visualization; Crop search; Decision Making
1.
INTRODUCTION
Farming
is an activity that falls into agriculture. A large section of population
worldwide depends on the agriculture as their basic source of income. All the
other people depend on agricultural produce for satisfying their basic needs.
Farming is a combination of activities that involves, selecting the right crop,
preparing the field or farm, sowing, irrigation, harvesting and storage.
Selecting the right crop is one of the most crucial step. Being initial step,
all the other tasks will depend on it. Therefore, it is very important to
select the proper crop before starting any farming activity.
A
number of factors influence the crop that can be grown in a particular region.
For instance, some crops are irrigation intensive like rice and suitable for
coastal region, whereas some fruits like apple are more suitable to hilly
regions like Kashmir. Availability of water, average temperature, weather
pattern, soil type, rainfall pattern, location, terrain and topography are some
of the factors that influence the crop selection. Any mistake in crop selection
can largely affect the income of the farmer and disrupt the farming cycle.
Geological
location of a place is the combination of the latitude and longitude that
uniquely marks the location of the place on the map of entire world. This
geological location can be a very important cue when it comes to crop
selection. Two regions closer to each other are likely to have many factors,
which influence the crop selection, in common as compared to others that are
far apart. For instance, two regions closer to each other are likely to follow
common rainfall pattern, topography, terrain, weather pattern, etc.
Therefore,
closeness of two regions on map serves as an important input to farmer while
selecting proper crop. Apart from the geological closeness of two regions,
visualizing the locations on map add helps farmer with other inputs as well.
Suppose a farmer is from coastal region and wishes to know the crop he / she
can grow. Plotting districts on map will help him / her to locate other coastal
districts and then find the crops grown there for better decision - making.
There are two farming seasons in India namely, kharif(autumn) and rabi(spring).
Kharif season spans from July to October
during the south-west monsoon winds. Rabi season spans from October to March,
i.e. during winters. Crops grown during March to June are called as summer
crops. So me crops are suitable for sowing in either kharif or rabi. Some can
be grown entire year.
In
this paper, we propose a novel visualization technique to help farmers select
crop that is suitable to their region. There are two parts to this technique,
search by district and search by crop. In former, a farmer can select any
district on map and get a list of all the crops grown in that area. Selecting
the crop, farmer gets graph visualization of area, production and production
per unit area of given crop in selected district. This can help farmer to
explore district wise crop pattern. Search by crop, provides the farmer with a
list of all crops. Farmer can click on any crop and visualize the districts
where the selected crop is grown. Upon selecting the district, the farmer can
view graph-based visualizations as before.
Following
sections talk about literature survey, followed by description about dataset,
methodology and conclusion.
2.
LITERATURE REVIEW
Odisha
is primarily dependent on agriculture. Although there has been a shift in the
state’s GDP ratio, with service sector accounting for 54.4%, around 60% of the
population of the state is still dependent on agriculture. This paper is a
study of cropping pattern in Odisha over a period of around 25-30 years, since
1980. During this period, the area under cereals such as wheat, bajra, rabi
pulses, oil seeds and cash crops declined, whereas certain cereals such as
maize and rice, increased drastically (DUKU; ZWART; HEIN, 2018).
Salem, located between 11.14º and 12.53º North and 77.44º
and 78.50º East is a land locked area of 5245sq. Km. There are a variety of
crops grown in this region, including, paddy, cholam, maize, cotton, etc.
Coffee is alone grown in around the area of Yercaud. All other crops are more
or less uniformly distributed around the district (LAKSHMINARAYANA;
RAJAGOPALAN, 1977).
Climate
change is having its own effect in affecting cropping patterns around the
world. Currently about 41% of the cultivated area in Upper Oueme can grow
rainfed sequential cropping. However, by 2050, it will decrease to 2-16%.
Farmers thus will need to shift to single cropping systems, short cycle
cultivars or adopt improved agronomic practices. Conversion of forested areas
to crop lands will have negative impacts on water availability for irrigation.
Even if there is no change in woodlands, 50% of irrigation potential will be
lost due to climate change (LEFF; RAMANKUTTY; FOLEY, 2004). Cropping pattern is
further affected by the involvement of pesticides and high yielding varieties
of the crops. It has been found that the area under the crops such as maize,
cotton and other vegetables has increased. The uses of HYVs are further subject
to the availability of fertile soils. These are also used to suppress the
pesticides and improve higher growth. The over exploitation of water going on
in Indo Gangetic plain, particularly in Punjab and Haryana, may lead to adverse
environmental issues (MAJHI; KUMAR, 2018).
It
is not so that the interest of Indian farmers is dying from agriculture.
Rather, they now increasingly cultivate more cash crops such as spices,
oilseeds, fibres, etc when compared to cereals. This may differ in different
states as per the demand and land quality (MANDAL; BEZBARUAH,
2013).
On
observing closely, it has been found that 18 major crops (barley, maize, millet,
rice, rye, sorghum, wheat, cassava, potatoes, sugar beets, sugar cane, pulses,
soybeans, groundnuts/peanuts, rapeseed/canola, sunflower, oil palm fruit, and
cotton) are the representative of the agriculture of most regions of the world.
Rice dominates the production in Asia. Approximately 24% of the cropland in
Asia is used for the production of the rice. Pulses are grown largely in
western India. In Asia as a whole, they constitute 6% of the cropland, but in
India 12%, which is the third maximum after rice and wheat (MANJUNATH; PANIGRAHY, 2009).
Kerala
is a unique state in itself because of its agro-climatic variations and
cropping patterns. The trend of mono-cropping is at a rise in the state, as
there has been a decline in both the area and production for food crops and in
favor of crops such as coffee, banana and rubber (NAYAK, 2016).
In
states such as Assam, where natural affects such as flood play a major role,
farmers adopta system of Crop Diversification. Crop diversification has an
important role in enhancing the farm income (RAJAGOPAL et al., 2015).
Not
only are weather and climate influencers of the cropping pattern, factors such
as availability of water, water levels, etc. also play a decisive role in the
crop selection. Both the surface and ground water are used for determine an
optimal pattern and release for maximizing the net benefits (REJULA; SINGH, 2015).
Among
all the crops grown in India, Rice is most produced. India stands first in
total area for rice production, where as it is second in terms of production.
It is generally grown in two major seasons, dry and wet. Among all the states
producing rice, it is most produced in the states of West Bengal, Andhra
Pradesh, Tamil Nadu and Orissa (SHETTY
et al., 2007).
The purpose of the article. The past study suggest that
agriculture is highly dependent on climatic condition and landscape of a place
therefore, in this research we have tried to suppose data visualization
technique which could help farmers to make agriculture related online searches
by district and by crop. If online searches made on the basis of district then
farmers will get detailed list of the crop grown in that area.
On the other hand if farmer makes online search about a
particular crop then he will get the detailed of production area and
productivity which could help him to understand the cropping patten of that
crop. Hence this research will help farmers in planning and implementation of
smart farming activities by incorporating artificial intelligence and visualization
techniques.
3.
METHODOLOGY
3.1.
Flow of work
The flow for building the web application is explained in
the block diagram below (see Figure 1):
Figure 1: Flowchart of
methodology for web application to predict the cropping pattern
Source:
composed by authors
In
our visualization solution, we have used two datasets.
This
data set is available at Indian Government’s website for sharing data -
data.gov.in. Data being from government’s website is expected to be correct. It
has seven features or columns namely State, District, Crop year, Season, Area,
Production. State denotes one of the 29 Indian states. District denotes one
among several districts in the given state. Season has three possible values
namely, “kharif”, “rabi” and “whole year”. Area and Production attributes
respectively denotes the area of land cultivated and amount of production
obtained for a given crop in a given year in a particular district (see Figure
2 for details). This is the main data set that contains all the information,
which will be visualized for better decision making. Given below is a small
clip of dataset for better understanding. It has 2,46,092 rows.
Figure 2: Prototype of Crop Area and Production Dataset
Source:
composed by authors
This
dataset contains latitude and longitude information of all the state &
district combination in our previous dataset. It is used to plot the districts
on the map. It has four columns namely, State, District, Latitude, Longitude.
Since, we required the latitude and longitude information for our custom list
of districts, the dataset was not available andhad to be prepared. Python
scripts were used to create the dataset. It has 653 rows.
Firstly,
the previous dataset was read and parsed and list of state, along with
districts in that state was created. Then for each state and district
combination, Open Cage Geo coder fetched the latitude and longitude values
using the API call.
Once,
all the data was fetched, it was stored in excel file and dataset was created.
Some on the python packages used were, open pyxl, for reading and writing to
excel files and requests for calling the API and fetching the results (see Figure
3). Given below is a snippet of the dataset.
Figure 3: Prototype of Latitude
and Longitude Dataset
Source:
composed by authors
Initially
both the datasets were in csv format. Our representation needed the data in
form that could be easily used to view it on the mapona web inter face. While
working with web inters face and plotting maps and graphs on web, Java Script,
HTML, CSS are the commonly used languages. However, loading data from csv
format in JavaScript and web interface for visualization is not a very efficient
option. Therefore, the data need to be converted to form that would be easy to
work with in web interface. So, the data was converted to Java Script Object
Notation or popularly known as JSON format. Python scripts were used to convert
the data to required JSON files. Four JSON files were created for fast and
better visualizations.
The
JSON file consists of list of objects with each object have four attribute
value pairs. The attributes were State, District (dis), latitude (lat) and
longitude (long). It is used to model the latitude and longitude data of all
states analyst format. When plotting the districts on map, the entire list is
traversed to plot on the districts on the map. In addition, the list can be
searched for a given state and district and the location data for that district
could be found out. ‘xlrd’ and ‘json’ are the Python packages used for creating
the file. Latitude and Longitude dataset is used to create this JSON file.
Schema of the file is as follows:
[{
“state”: “Andaman and Nicobar
Islands”, “dis”: “Nicobars”,
“lat”:8,
“long”:93.5
},
{
“state”: “Andaman and Nicobar
Islands”, “dis”: “North And Middle Andaman”, “lat”:12.6112387,
“long”:92.8316541
},...]
b. Crop list JSON file
This
file consists of a list of all the crops. This file has unique, non-repetitive
names of all the crops, about which data is present in the dataset. It is used
in the part of visualization where search by crop name is used. This list is
traversed to make buttons for all the crops. “pandas” and “json” are the python
packages used to make Python script for creating the given file. Crop area and
production dataset is used to make this JSON file. Schema of the file is as
follows:
[
“Arecanut”,
“Other Kharif pulses”, “Rice”,
“Banana”,
…]
This
file contains all the data about the crops produced in different major
districts of India. The data is organised in the form of array of objects,
where each state is an object, having further districts as their object. Each
district contains the year wise data in the form of arrays. For each element of
the array, first element is the year, second is the type of crop, quantity of
production of the crop and area in which the production was carried out. This
JSON is the heart and soul of the project, which forms the basis of
thisproject.
Schema
of the file is as follows:
[
{
“Andaman
and Nicobar Islands”:{ “NICOBARS”: {
“Arecanut”:
[ [
2000,
“Kharif”,
1254,
2000
],
[
2001,
“Kharif”,
1254,
2061
],
This
file has a JSON object in which the attributes are names of all the crops that
are present in the crop list. The value of each crop name is list consisting of
names of all the states followed by underscore and name of district where the
given crop is grown. This data is useful for faster marking on map, when using
search by crop name option. “pandas” and “JSON” are the python packages used to
make Python script for creating the given file. Crop area and production
dataset is used to make this JSON file.
Schema
of the file is as follows:
{
“Arecanut”:
[
“Andaman
and Nicobar Islands_Nicobars”,
“Andaman
and Nicobar Islands_NorthAnd Middle Andaman”, “Andaman and Nicobar
Islands_SouthAndamans”,
“Andhra
Pradesh_Anantapur”,
…],
…
}
4.
RESULTS AND DISCUSSIONS
The
aim of the visualization technique is to make crop selection easier and better
for farmers. So, to visualize the information there are two available options.
One is Search by District and other is Search by Crop. Let us understand one by
one.
In
this part, the flow goes as follows: All the districts are marked on the Indian
mapby a red marker. When the marker for a given district is selected, a list of
crops grown in that district is loaded below the map. The user can click on any
given crop. When user selects a given crop, three plots are loaded –
Production, Area and Production per unit area. Plots denote the pattern of
production, area and production per unit area overyear.
To
start with, we first need to include a map. Out of all available options here,
Leaflet is used to include the map. Leaflet is a popular open – source Java
Script based library which is used to include maps in web applications. We
initialize the map and add the layer to display the names and routes, which is
a basic layer. We also, set the view of the map, showing Indianregion.
After
including the map, we need to represent all the districts with a marker.
Districts are represented by a red circle. Here the Latitude and Longitude JSON
file comes into use. Java Script’s looping functions are used to loop through
the list of objects representing state and district along with geolocation
data. The markers for all the districts in the list are created. In the marker
object of each district, the state and district name is added as separate
fields. In addition, an “on click” event listener is added to each marker to
call the function when the marker is clicked. When the district marker is
clicked, the function in on click event listener is called. The state and
district information stored in marker object helps identity the district and
state in the called function.
Once
the unique information about the district and state is received, it is further
used to get the data out of crop data, where these are used as nested keys to
get the nested array of crop information. From here, we can get all the crops
produced in that region, which are then displayed in the form ofbuttons.
After
we choose any of these crops for the selected district, an on click listener
calls the function, which then plots the graph. For plotting the graph, we are
using a Java Script library called Plotly.js, which requires us to pass the
values to it in the form of production, year and the season, or type of crop.
We
are plotting 3 graphs for proper visualization of the data. The first graph
represents the production of a particular crop in the selected district over
the years. Similarly, second graph shows the area used for production in the
particular district, dedicated to the crop over years. And in addition to
these, third graph provides production per unit area, which helps farmers to
understand about the climate change or other issues which may be causing growth
or decline in the production of the crop and take suitable decision for the
future (see Figure 4).
Figure
4: District wise Search
Source: composed by authors
In
this part, the flow is follows: Initially there is series of buttons for all
the crops cultivated in India. When a crop is clicked, the maps gets loaded
with markers for all the districts in which the selected crop is cultivated.
When the marker of a district is clicked, three plots are loaded showing the
area, production and production per unit area of the crop in the selected
district.
Firstly,
we need to add the buttons for all the crops cultivated in India. Crop list
JSON file is used to get a list of all the crops. Using Java Script looping
function, loops through the entire crop list and create button for each crop.
In button, we add an on click event listener along with function to be called.
Function takes name of the crop as the parameter to identify which crop
isselected.
Once
user selects the crop, function with crop name as parameter is called. Using
the Crop-wise State list JSON file, a list of the state, district combination
cultivating the selected crop is obtained. Using Leaflet we include the map
along with layers as explained before. Loop through all the districts; find the
geolocation data of each using Latitude and Longitude JSON file and then mark
the district with a red circle marker on the map. Here, state, district and
crop name added as separate fields. On click event listener is added to marker
as before. As the user clicks on district marker, the call-back function of
event listener is opened.
Similar
as before, once we have received the district and crop information from the user,
we can easily plot the data in the form of graphs (see Figure 5).
Figure 5: Crop Area Wise
Search
Source: composed by authors
5.
CONCLUSION AND DISCUSSION
India is an agronomic nation. Providing employment to 50%
of the country’s workforce, agriculture sector accounts for 18% of the
country’s Gross Domestic Product (GDP). Thus, it is very important, not only
from a farmer perspective, but for nation as a whole. Visualizing the crop
production, crop area and production per unit area for several years, does help
farmer to make better decisions with regard to crop selection.
Also, farmers get flexibility to either search by
district or search by crop. With these options, one can easily visualize how
the topology and geography of a place affects the crop produced in an area.
Various insights can be drawn from these visualizations like, Coffee is only
grown in considerate amounts in the hilly districts of Kerala, namely, Wayanad,
Idukki and Palakkad.
Although the production of Coffee is on a decline in all
the districts, Idukki shows a remarkable increase in production per unit area
for coffee. Tea on the other hand is also produced in the north-eastern states
of Nagaland and regions near Assam.
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