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International Journal of Advances in Engineering & Technology, Nov. 2013.
©IJAET
ISSN: 22311963

CROP PREDICTION AND DETECTION USING FUZZY LOGIC IN
MATLAB
Kartik Ingole1, Kavita Katole2, Ashwin Shinde3, Minal Domke3
1

Electronics & Communication Engineering,
Babasaheb Ambedkar College of Engineering & Research,
RTM Nagpur University, Nagpur, India.
2
Electronics Engineering,
Babasaheb Ambedkar College of Engineering & Research,
RTM Nagpur University, Nagpur, India.
3
Information Technology,
Babasaheb Ambedkar College of Engineering & Research,
RTM Nagpur University, Nagpur, India.

ABSTRACT
The purpose of this paper is to explore the dynamics of fuzzy in forecasting crop (wheat) yield using remote
sensing and other data. Our paper is crop prediction using fuzzy logic. Since fuzzy logic has the ability to mimic
human being in reasoning, it is good alternative Fuzzy control rules are either synthesized through a careful
analysis of the nature of the parameters of land or automatically generated during the control process. By this it
can predict which crop is suitable for the particular condition. The two sensing mechanisms are used in the
system light sensor and temperature sensor. The effectiveness of the fuzzy control system has been verified
through experiments. The advantage of fuzzy set theory is it has the property of relativity, variability and
inexactness in the definition of its elements or it entertains imprecise information, therefore every scientific
discipline based on experiment and measurements can make use of fuzzy sets in mathematical modeling and in
analytical solutions to improve the generality i.e. allows multiple solution of varying possibilities of one crisp
exact solution. It helps in selecting proper crop according to climatic conditions. This helps in increasing the
crop yield.

KEYWORDS: Fuzzy control, light sensor, temperature sensor.

I.

INTRODUCTION

All over the world agriculture is an important industry. To know which crop to cultivate according to
the climatic and surrounding conditions is a tough job. Cultivation of proper crop helps in increase in
productivity and also enhances the quality of soil. The project is useful for prediction of suitable crop
for the given environmental conditions. The parameters are given to the crop prediction engine and
using fuzzy logic the results i.e. the name of crop is predicted. It is important to get the name of the
crop so as to get better yield and meet the requirements’ of increasing population. Several flavours of
neuro-fuzzy systems have been applied to the classification of remotely sensed imagery.
A comparison of the most commonly used ones can be found in Stathakis We briefly mention here
that NEFCLASS (Nauck, 2003) has been used in the classification of remotely sensed data to obtain
land use and land cover classes (Stathakis et al., in press b). ANFIS (Jang, 1993) has been used in the
same context (Benediktsson et al., 1999). Fuzzy ARMAP (Carpenter et al, 1992) has been applied into
global land cover mapping
(Gopal et al., 1999). The use of a neuro-fuzzy system for crop yield
estimate has several appealing characteristics. All the variables that are input into the system are

2006

Vol. 6, Issue 5, pp. 2006-2012

International Journal of Advances in Engineering & Technology, Nov. 2013.
©IJAET
ISSN: 22311963
associated with varying degrees of accuracy. The ambiguity steams from measurement error and
generalisation. Using fuzzy sets instead of the actual values as inputs, we aim at shifting to the
semantics of the data rather than its measure (Zadeh, 1999).

II.

CROP PRODUCTION TECHNOLOGIES

The nutrient management technologies food production by the year 2020 needs to be increased by
50 percent more than the present levels to satisfy the needs of around 8 billion people. Much of the
increase would have to come from intensification of agricultural production. Importance of wise
usage of water, nutrient management, and tillage in the agricultural sector for sustaining agricultural
growth and slowing crop production depends on the successful implementation of the soil, water,
down environmental degradation calls for urgent attention of researchers, planners, and policy
makers. Crop models enable researchers to promptly speculate on the long-term consequences of
changes in agricultural practices. In addition, cropping systems, under different conditions, are
making it possible to identify the adaptations required to respond to changes. This book adopts an
interdisciplinary approach and contributes to this new vision. Leading authors analyze topics related
to crop production technologies. The efforts have been made to keep the language as simple as
possible, keeping in mind the readers of different language origins. The emphasis has been on
general descriptions

Fig. 1.

III.

Effects of drought on farming areas

EFFECTS OF DROUGHT ON FARM PRODUCTION AND LIVESTOCK
HOLDINGS

The main direct effects of drought on the farming sector are summarized in Figure 1. The most
immediate consequence of drought is a fall in crop production, due to inadequate and poorly
distributed rainfall. Farmers are faced with harvests that are too small to both feed their families and
fulfill their other commitments. Livestock sales act as a buffer in times of hardship, farmers
disinvesting in these assets to buy food. The first animals to be sold are usually those which make the
least contribution to farm production, such as sheep and goats. However, as the period of droughtinduced food deficit lengthens, farmers will have to start selling transport and draft animals, such as
oxen and donkeys, as well as breeding stock, which constitute the basis of the household's wealth. In
the Ethiopian highlands, stock are usually disposed of in the following order: sheep and goats, then
younger cattle, with horses, donkeys and work oxen being sold as a last resort (Wood, 1976), since the
latter are essential for land preparation.6. Where crops have been badly affected by drought, pasture
production is also likely to be reduced although output from natural pastures tends to be less

2007

Vol. 6, Issue 5, pp. 2006-2012

International Journal of Advances in Engineering & Technology, Nov. 2013.
©IJAET
ISSN: 22311963
vulnerable to drought than crop production. Low rainfall causes poor pasture growth and may also
lead to a decline in fodder supplies from crop residues. Insufficient levels of fodder around the village
lead to weigh less and increased deaths among stock, especially where immigrant herds put further
pressure on limited local pastures. While the response of most pastoral groups to fodder shortage is to
move themselves and their herds elsewhere, this is not an option so easily followed by livestockowning farmers. Typically, farmers own fewer animals and have less familiarity with regular
transhumance than pastoralists, both of which act as constraints on migration. In addition, few farm
households will have sufficient labor to both take their animals to other grazing areas and continue
with necessary farming operations. These high losses were caused by pasture shortages exacerbated
by herds from further north on their way to southern pastures, by the sedentary character of livestock
holdings amongst farmers in this area and by the normal dependence of horses (and to some extent
donkeys) on a daily grain ration to supplement natural grazing, a supplement which is no longer given
because of poor harvests.

IV.

WAYS IN WHICH FARMERS CAN ACHIEVE CROP RECOVERY

There are a number of options that farmers can pursue in order to restore levels of crop production
and their holdings of draft animals. These include the sharing of animals between households, use of
other stock for pulling the plough, hand cultivation of soils, hire of tractor services, changes in crop
composition, purchase of fertilizer, supplementation of remaining stock, turning to income earned
elsewhere or waiting for livestock holdings to re-grow. Each of these is discussed below.13. Sharing
of animals between households may be possible where overall losses have been slight. Such animal
loans are common in many farming systems in normal years, the loan of a plough team often being
repaid with so many days of weeding labour. Alternatively, two households with a single ox each can
arrange to take turns in using the oxen pair, as described by Gryseels and Anderson (1983) for
Ethiopia. However, where oxen losses have been heavy, loans ill be less easy to arrange for those who
have lost their draft animals and the cost of such loans are likely to increase.14. The use of other
animals for draft may be possible where, for example, losses among horses and donkeys have been
less severe than work oxen. The former will have a lower productivity but their availability will
partially compensate for the loss of trained oxen. In extreme cases, even human labour has been used
for pulling the plough, as in the period following the great rinderpest epidemic in Ethiopia in the
1890s when an estimated 90% of the country's draft oxen were lost (Wolde Mariam, 1984). However,
if work oxen holdings have been badly affected by drought it is likely that other stock will also have
suffered high mortality or will have been sold to purchase food grains.15. Hand cultivation of part of
the farmer's land may be possible in the absence of draft animals. However, this will be at the cost of
lower crop output due both to the smaller area cultivated and the lower effectiveness of hand
cultivation as opposed to plough techniques. Estimates of the land area whICh can be cultivated by
hand vary from 10% to 50% of that which can be managed by a plough team, depending on the nature
of soils and the time available for land preparation. Although uncommon where weeding is also done
by plough, resort to hand techniques will lead to lower yields from the less optimal timing of this
operation.16. The hire of tractor services is only open to a limited number of farmers with access to
this service at reasonable cost. Hire of a tractor is usually more expensive than hire of a plough team
for the same work and, in the case of Botswana, will normally require a cash outlay rather than
repayment in labor or other services (Vie rich and Sheppard, 1979). For this reason, farmers who find
themselves without work oxen will often also be without the funds to hire a tractor.17. A change in
the composition of crops grown can reduce the farmer's tillage requirements. For example, in the case
of Ethiopia, while teff needs a finely prepared seed bed, pulses can be sown on land that has received
a more rudimentary tillage. Similarly, in Mali, millets can be sown on unplugged land whereas
groundnuts require a prepared seedbed. The possibility of farmers moderating the impact of draft
animal losses by switching to less tillage-intensive crops depends on their access to seed, their
family's consumption needs and the prospects for marketing different crops, be made in the
intervening years either to obtain food or to borrow draft power from elsewhere.18. Fertilizer
purchases can moderate the fall in crop output arising from a decline in area cultivated by raising
yields on the area actually farmed. The effectiveness of this option depends on crop response to
fertilizer use and the relative costs of purchase, transport and application of fertilizer. Lack of cash at

2008

Vol. 6, Issue 5, pp. 2006-2012

International Journal of Advances in Engineering & Technology, Nov. 2013.
©IJAET
ISSN: 22311963
the farm level in the post-drought period prevents this option being widely pursued, in the absence of
extensive government subsidies for the purchase and distribution of this input. Even then, farmers
may still consider the use of fertilizer in poor seasons as involving too high a risk.19. Surviving draft
animal may be given supplementary feed in order to increase their working capacity. This fodder
could come from crop by-products or natural pasture and browse, both of whICh are likely to be in
short supply following drought. Additional feed may be available from agro-industrial by-products,
such as cotton seed, molasses and bran. Supplies of these products will be limited and their prices
high where these are normally exported, (as in the case of many Sahelian countries) unless the
government gives special priority to their local use.20. Incomes earned elsewhere can be used to buy
replacement oxen. For example, migration earnings are a major source of cash incomes for many
farming areas in southern Africa and the Sahel. Migration may be seasonal or involve the absence of a
male household member for a number of years, during which time cash remittances are sent back to
the farm sector for the purchase both of food and farm inputs. The ease with which these earnings can
be used to finance the purchase of new work animals depends on the relative value of the remittance,
the price of work oxen and the urgency of other calls upon cash income. In times of drought, urban
labor markets are usually flooded with job-seekers leading to low real wage levels. For this reason,
the size of migration earnings is likely to be low in the post-drought period and possibilities for
acquiring the funds to purchase work oxen more limited than in normal times. It will also be harder
for farmers to rebuild work oxen holdings where both the arable and the farm sector have been hit by
drought.

V.

CIRCUIT DIAGRAM

Fig 2.

VI.

Hardware circuit assembling layout

WORKING

Temperature sensor is connected to pin no.26 and light sensor is connected to pin no.27. IC 555 is a
timer IC is connected to pin no 10. IC 555 works in two modes monostable and astable. Here we are
using astable mode as in generates the frequency. This IC is a monolithic timing circuit that can
generates accurate and highly stable time delay or oscillation. It is used to give delays to ADC.Select
line i.e. pin 25 of ADC is connected to pin 13 of microcontroller. If 0 o is given temperature data is
given to ADC and if 1 is given light data is given to ADC. Start of conversion pulse is given through
pin no. 6 and 22. after completion of conversion end of conversion pulse get 1 and 8 bit data is
transferred to microcontroller and displayed on LCD and PC through MAX 232 Power on reset circuit
is connected to pin no.9. Power on reset circuit is essential because when the switch on the supply
execution of program inside microcontroller starts from 00H MAX 232 is connected to pin no. 10 and
11 of microcontroller. It converts signal from RS232 serial port to signal suitable for use in TTL
compatible digital logIC circuit.

2009

Vol. 6, Issue 5, pp. 2006-2012

International Journal of Advances in Engineering & Technology, Nov. 2013.
©IJAET
ISSN: 22311963

VII.

FUZZY LOGIC INTRODUCTION

Fuzzy logic was initiated in 1965, by lot .A.Zadeh, professor for computer science at the University of
California in Berkeley. Basically, fuzzy logic (FL) is a multivalve logic that allows intermediated
values to be defined between conventional evaluations like true/false, yes/no, high/low etc. Notions
like rather tall or very fast can be formulated mathematically and processed by computer, in order to
apply a more humane like way of thinking in the programming of computers. Fuzzy systems are an
alternative to traditional notions of set of membership and logic that has its origins in ancient Greek
philosophy. The precision of mathematics owes its success in large part to the efforts of Aristotle and
the philosophers who preceded him. In their efforts to devise a concise theory of logic, and later
mathematics, the so- called “Laws of Thought” were posited. One of these, the “Law of the Excluded
Middle”, states that every proposition must either be true or false. Even when Parmenides proposed
the first version of this law (around 400 B.C.) there were strong and immediate objections: for
example, Heraclitus proposed that things could be simultaneously true and not true. It was Plato who
laid the foundation for what would become fuzzy logic, indicating that there was a third region
(beyond True and False) where these opposites “tumbled about”. Others more modern philosophers
echoed his sentiments , notably Hegel, Marx and Engels. But it was LukasiewICz who first proposed
a systematic alternative to the bi-valued logic of Aristotle. Even in the present time some Greeks are
still outstanding examples for fussiness and fuzziness,(note: the connection to logic got lost
somewhere during the last 2millenniums).Fuzzy logic has emerged as a profitable tool for the
controlling and steering of systems and complex industrial processes, as well as for household and
entertainment electronics, as well as for other experts systems and applications like the classification
of SAR data.

Fig 3

Fuzzy logic classification

Fig 4 Fuzzy logic interpretation

2010

Vol. 6, Issue 5, pp. 2006-2012

International Journal of Advances in Engineering & Technology, Nov. 2013.
©IJAET
ISSN: 22311963

VIII.

RESULTS

Fig 5 Parameter of GUI for fuzzy output

Fig 6 Fuzzy classification for various parameters

Fig 7 Fuzzy Rule Base for Various Parameter

IX.

CONCLUSIONS

Thus we have find out the Erosion model using spatial data on soils, land use and crop cover, weather
and topological features was developed using GIS techniques. Finally, it would be very interesting to
explore the knowledge learned by the fuzzy system by looking thoroughly into the rules created in the
rule base. Perhaps this would provide an insight with respect to which are the most important factors
in crop yield prediction using a data driven approach. We believe that parameters such as the
number of fuzzy sets, the type of membership functions as well as the option to have different
parameters per input, should receive more experimental effort.

X.

FUTURE SCOPE

A possible way forward is to use a genetic algorithm to select the optimal values, that means we will
implement all these parameters using genetic algorithm and will simulate this design using neural
logic system. ANFIS has only one output node, the yield. In other words a single number is sought.

2011

Vol. 6, Issue 5, pp. 2006-2012

International Journal of Advances in Engineering & Technology, Nov. 2013.
©IJAET
ISSN: 22311963
An additional difficulty in predicting yield is that remote sensing data do not go long back in time.
Hence any predicting effort is forced to use a very limited number of past years in order to construct a
model to forecast future values. The system is trained by leaving one year out and using all the other
data. We then evaluate the deviation of our estimate compared to the yield of the year that is left out.

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AUTHORS
Kartik Ingole has completed M Tech in communication From Priyadarshini College of
Engineering and working as a Asst Prof in Dr. Babasaheb Ambedkar College of Engineering
Nagpur. He has done his B.E. (Etrx) in 2009 from G.H. Raisoni College of Engineering
Nagpur. His special field of interest includes Reconfigurable Computing, Wireless computing
designing
Minal Domke has completed M. Tech in (Computer Science Engg.)From Yashwantrao Chavan
College of Engineering, Nagpur (YCCE),.She is working as a Lecturer in Information
Technology Dept. of Dr. Babasaheb Ambedkar College of Engineering And Research
of Meghe Group of Institutions from 10/06/2009
Kavita Katole is presently working as Assistant Professor in Electronics Engineering
Department of Dr. Babasaheb Ambedkar College of Engineering, Nagpur. She has done her
M.Tech. (Electronics) in 2011 from G.H.Raisoni College of Engineering Nagpur. Her special
field of interest includes Embedded System and signal processing.

Ashwin A. Shinde did his Polytechnic diploma in IT from MSBTE. He received his B.E. degree
in Information Technology from Rashtrasant Tukdoji Maharaj Nagpur University, Maharashtra,
India in 2009 and completed his M.Tech in IT from LNCT institute, Bhopal, Rajiv Gandhi
Prodyogiki Vishvavidyalaya University, Bhopal, India

2012

Vol. 6, Issue 5, pp. 2006-2012


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