COMPUTER SCIENCE AND APPLICATION, VOL. 1, NO. 2, FEBRUARY, 2012 4

Abstract—In this study, a fuzzy logic model was developed to

estimate the optimum shear strength of rice stem. The input parameters of the fuzzy model were stem moisture content, internode position and cutting blade loading rate. In order to write the fuzzy rules for the first linguistic variable, stem moisture content, four membership functions namely, very low (0-15), low (10-40), medium (35-65), and high (60-80), in terms of wet basis percentage were defined. Three membership functions including low (0-30), middle (25-70) and high (65-120) were considered for the second linguistic variable, stem internode position, all in cm. In the case of the third linguistic variable, cutting blade loading rate, three membership functions including low (0-5), medium (4-10) and high (9-15), were assigned in terms of mm/min. Three membership functions were also assigned to the output of the fuzzy system that was shearing energy, including low (0-150), middle (140-300), and high (280-450) in terms of mJ. In order to validate the fuzzy model, the shearing energy of rice stems obtained through primary experimental treatments were assessed and compared with those values acquired using the fuzzy logic rules. The results showed that the fuzzy model used in this study had an acceptable predictive ability to estimate the level of shear strength of rice stem. The model accuracy to range the shear strength of rice stem was 86%, 97% and 91%, respectively in high, middle and low ranges of shearing energy.

Index Terms—Fuzzy logic, Model, Rice stem, Shearing energy, Shear strength

I. INTRODUCTION uzzy logic is a method of rule-based decision making used for expert systems and process control. Logic is defined as

the science of the normative formal principles of reasoning. In this sense, fuzzy logic is related to formal principles of approximate reasoning, with precise reasoning viewed as a limiting case. In more specific terms, what is central about fuzzy logic is that, unlike classical logical systems, it aims at modeling the imprecise modes of reasoning that play an essential role in the remarkable human ability to make rational decisions in an environment of uncertainty and imprecision. This ability depends, in turn, on our ability to infer an approximate answer to a question based on a store of knowledge that is inexact, incomplete, or not totally reliable. For example, the shear energy to cut the stem of agricultural

Manuscript received January 30, 2012; accepted on February 18, 2012. H. Zareiforoush, A. Mahdavian and B. Hosseinzadeh are with the

Department of Mechanics of Agricultural Machinery, Tarbiat Modares University, P.O. Box 14115-111, Tehran 14114, Iran Corresponding author’s Email: a.mahdavian@modares.ac.ir

crops by mowers at lower levels of stem moisture content is more than that of higher moisture contents. In such a case, the question is that “how more is the cutting energy?”

Fuzzy logic addresses such problems in the following ways. First, the meaning of a lexically imprecise proposition is represented as an elastic constraint on a variable; and second, the answer to a query is deduced through a propagation of elastic constraints. During the past several years, fuzzy logic has found numerous applications in fields ranging from finance to engineering. The first implementation was pioneered by Mamdani and Assilian [1] in connection with the regulation of a steam engine. In the ensuing years, once the basic idea underlying fuzzy logic control became well understood, many applications followed. In Japan, in particular, the use of fuzzy logic in control processes is being pursued in many application areas. In most of the current applications of fuzzy logic, software is employed as a medium for the implementation of fuzzy algorithms and control rules.

Fuzzy logic may be viewed as an extension of multivalued logic. Its uses and objectives, however, are quite different. Thus, the fact that fuzzy logic deals with approximate rather than precise modes of reasoning implies that, in general, the chains of reasoning in fuzzy logic are short in length, and rigor does not play as important a role as it does in classical logical systems. In a nutshell, in fuzzy logic everything, including truth, is a matter of degree. The greater expressive power of fuzzy logic derives from the fact that it contains as special cases not only the classical twovalued and multivalued logical systems but also probability theory and probabilistic logic.

Intelligent systems based on the fuzzy logic are often used in sorting processes for detecting defects in biological and medical sciences. Fuzzy logic can improve grading processes by using fuzzy sets to define the degrees of overlap. Moreover, application the “if-then” logics can improve the interpretation and explanation the results and provide a widespread view on the construction of decision process [2]. Zadeh [3] introduced the concept of fuzzy sets as a means for describing complex systems without the requirements for precision. Zadeh [4] proclaimed a principle called the “principle of incompatibility”, which states that complexity and precision are incompatible due to the inability of the human mind to comprehend complex systems in a detailed manner. By reducing the need for precision it is possible to more easily express known qualitative relationships about complex systems. Zadeh [4] noted that this method for dealing with uncertainty would have particular

An Approach to Estimate the Shear Strength of Rice Stem using a Fuzzy Logic Model

Hemad Zareiforoush, Alireza Mahdavian, Bahram Hosseinzadeh

F

COMPUTER SCIENCE AND APPLICATION, VOL. 1, NO. 2, FEBRUARY, 2012 5

applicability in soft systems such as psychology, sociology, and economics.

Fuzzy logic may also be useful for descriptive systems, those that fall somewhere between hard systems and soft systems, such as biology and agriculture. Fuzzy logic approaches provide a suitable framework for modeling these systems due to their ability to handle “fragmentary, uncertain, qualitative and blended knowledge typically available for biological systems” [5]. Verma [6] developed a fuzzy decision support system (DSS) to aid decisions related to quality sorting of tomatoes. Six fuzzy models were developed and linked to develop the DSS. The outputs of fuzzy DSS predicting quality and the day of the highest quality was very accurate when compared with the data provided by an expert. Some other examples of reported fuzzy logic applications includes a model to predict the effects of multiple stresses on tree growth [7], organizing bioengineering knowledge in fuzzy models that can be used for prediction [8, 9], predicting right soil moisture for land preparation [10], feeding strategies on large dairy farms [11], and grading beef quality [12]. Based on the growing evidence from the literature showing successful use of fuzzy logic for modeling, DSS and controls, it appears that applications to biological and agricultural systems are inevitable.

In many agricultural machines, a knife is used to cut plant material and therefore the cutting force must be supported. Often cutting is accomplished by shearing the material between a moving knife and a stationary countershear which is called counter-edge cutting (as used in a combine harvester). In counter-edge cutting, the support force can be provided entirely by the countershear. Sometimes, there is no countershear (as used in flail mowers and rotary mowers), therefore the support force must be provided entirely by the plant itself through the bending strength of the stem below the cut and the inertia of the plant above the cut. The resulting cut is called impact cut, inertia cut, or free cut. As clearance with a countershear increases, the plant strength and inertia come increasingly into play; thus, impact cutting is similar to countershear cutting with very large clearance. Impact cutting is usually used for forage harvesting, while counter-edge cutting is used for cereal harvesting (like rice) as well as forage harvesting. Proper equipment design in order to accomplish the cutting, will maintain the quality of the harvested product while minimizing the force and energy needed to accomplish the task [13].

The objective of this study was to model the shear strength of rice stem according to the product harvest conditions using fuzzy logic.

II. MATERIALS AND METHODS

A. Samples preparation The rice stems of Hashemi variety used for the current

study. The stem samples were from the prevalent varieties of rice in Iran and were obtained from the agronomy farm of the Rice Research Institute, Rasht, Iran. The stems were collected at harvesting time and their internodes were separated according to their position down from the ear. Leaf blades and sheaths were removed prior to any treatment or measurement.

To determine the average moisture content of the rice stems, the samples were weighed and oven-dried at 103°C for 24 h [14] and then reweighed. The average moisture content of Hashemi was 71.6% (w.b.). In order to evaluate the effect of moisture content on the shearing strength of rice stems, four ranges of moisture were made in the samples, including 10%, 30%, 50% and 70% (w.b.). To reach the target moisture contents for the other samples (10%, 30% and 50%), values for dry matter and initial weight were required. Dry matter was estimated by using values for the dry matter and the moisture content from the fresh-cut sample. When by successive weighing, the target moisture contents were reached the samples were then tested [15].

B. Experimental procedure The shearing strength of rice stem were assessed using a

shearing test similar to those described by İnce et al. [16], Nazari Galedar et al. [15] and Tavakoli et al. [17]. The measurements were made using a proprietary tension/compression testing machine (Instron Universal Testing Machine /SMT-5, SANTAM Company, Tehran, Iran). Three internodes of the rice stems, namely, lower, middle and upper internodes were studied in this research. The highest and lowest stem internodes from the ear were not considered because these internodes are usually left on the field.

The shearing strength was measured in double shear using a shear box consisting essentially of two fixed parallel hardened steel plates 6 mm apart, between which a third plate can slide freely in a close sliding fit (Fig. 1). A series of holes with diameters ranging from 1.5 to 5 mm were drilled through the plates to accommodate internodes of differing diameters. Shearing force was applied to the stem specimens by mounting the shear box in the tension/compression testing machine. The sliding plate was loaded in a rate of 5 to 15 mm min-1 and, as for the shear test, the applied shearing force was measured by a strain-gauge load cell and a force-time record obtained up to the specimen failure. The shearing energy (Es), was calculated by integrating the area under curves of shear force and displacement [15, 18, 19] using a standard computer program (vers. 5, SMT Machine Linker, SANTAM Company, Tehran, Iran).

C. Experimental design and statistical analysis In order to validate the significance of the evaluated factors

(stem moisture content, stem internode position and cutting blade loading rate), this study was planned as a completely randomized block design. The shear strength of rice stem was determined with five replications in each treatment. Experimental data were analysed using analysis of variance (ANOVA) and the means were compared at the 1% and 5% levels of significance using the Duncan’s multiple range tests in SPSS software (vers. 15, SPSS, Inc., Chicago, IL, USA).

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Fig. 1. Schematic of the Apparatus for Measuring the Shear

Strength of Rice Stem

D. Fuzzy sets Fuzzy logic starts with the concept of fuzzy set. A fuzzy set

is defined as a system without certain member that has a clear boundary. The fuzzy set can include all of the elements of the universe of discourse only by one relative degree of membership [3]. In other words, a fuzzy set is defined as a set of ordered pairs in the following form:

F = {(x, µD(x)) x�X, µD(x) � [0, 1]} (1)

Where x is a member of the X, that is to say the universe of discourse, and F is a fuzzy set in the X. In Eq. (1), µD (x) is the membership function of F which indicates the degree and/or order in which each x element of X belongs to F. This definition assigns a natural number (µD (x)) to each x element of F in [0 1] interval. The higher values of µD (x) indicates the higher degrees of membership. The number of fuzzy rules which are necessary to develop a fuzzy control system is directly proportional to the number of each experimental factor, e.g. cutting height [20].

The membership function (MF) is defined as a graphical representation of the magnitude of participation of each input. It associates a weighting with each of the inputs that are processed, define functional overlap between inputs, and ultimately determines an output response. The rules use the input membership values as weighting factors to determine their influence on the fuzzy output sets of the final output conclusion. Once the functions are inferred, scaled, and combined, they are defuzzified into a crisp output which drives the system. There are different membership functions associated with each input and output response. Triangular membership function is common, but bell, trapezoidal, haversine and, exponential types have also been used. More complex functions are possible but require greater computing overhead to implement. In this study, the triangular and trapezoidal membership functions were evaluated. Triangular and trapezoidal functions are simple and most frequently used in fuzzy sets. A trapezoid membership function is defined as a function having four points in the variation range of a parameter (Fig. 2). The trapezoid membership function is mathematically defined as the following equation:

( ) )2(1

0

,,,,

dxccxbbxa

ax

cdxd

abax

dcbaxTrapmf

pp

pp

pp

≤

⎪⎪

⎩

⎪⎪

⎨

⎧

−−

−−

=

Eq. (2) can be written in the compact form:

( ) )3(0,,1,minmax,,,, ⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎠⎞

⎜⎝⎛

−−

−−

=cdxd

abaxdcbaxTrapmf

Where, x is input vector, and a, b, c, and d are numbers which are obtained by measurements to define fuzzy ranges.

Fig. 2. Overall Form of a Trapezoid Membership Function

E. Fuzzy classification Fuzzy classifiers are one application of fuzzy theory. Expert

knowledge is used and can be expressed in a very natural way using linguistic variables, which are described by fuzzy sets. Then the expert knowledge for linguistic variables can be formulated as a rule base. Linguistic rules describing the control system consist of two parts; an antecedent block (between the IF and THEN) and a consequent block (following THEN). Depending on the system, it may not be necessary to evaluate every possible input combination, since some may rarely or never occur. By making this type of evaluation, usually done by an experienced operator, fewer rules can be evaluated, thus simplifying the processing logic and perhaps even improving the fuzzy logic system performance. The inputs are combined logically using the AND operator to produce output response values for all expected inputs. The active conclusions are then combined into a logical sum for each membership function. A firing strength for each output membership function is computed. All that remains is to combine these logical sums in a defuzzification process to produce the crisp output.

In fuzzy logic, fuzzy sets and fuzzy operators are acts and actuators. If fact, the “if-then” rules formulize the necessary conditions for fuzzy logic to decide and grading. A single fuzzy logic is written as below:

)4(, BisyTHENAisxIF

Where A and B are defined linguistic variables for x and y variables by fuzzy sets in the range of X and Y, the universes of discourse. Fuzzy logics could also be written in combined form by use of AND operator:

COMPUTER SCIENCE AND APPLICATION, VOL. 1, NO. 2, FEBRUARY, 2012 7

)5(,....,3,2,1,: niCisZTHENBisyANDAisxIFR iiiiiii =

Where Ai and Bi are fuzzy sets for xi and yi inputs which assign linguistic variables such as low, middle and high and n is the number of rules [1]. In the current study, Ci includes the linguistic variables to determine the level of output factors.

F. Defuzzification process Defuzzification is the process of producing a quantifiable

result in fuzzy logic, given fuzzy sets and corresponding membership degrees. It is typically needed in fuzzy control systems. These will have a number of rules that transform a number of variables into a fuzzy result, that is, the result is described in terms of membership in fuzzy sets. In other words, defuzzification is interpreting the membership degrees of the fuzzy sets into a specific decision or real value. By use a deffuzifier, the output variable converts to a real value. There are many different methods of defuzzification such as center of gravity (COG), mean of maximum (MOM), center of maximum (COM), and first of maximum (FOM) [21]. In this study, the center of gravity (COG) approach was used for conducting the processes. The COG method is a common and useful defuzzification technique [22].

After measurement the shear strength of rice stem at different levels of experimental evaluated factors, the fuzzy model was developed. The three evaluated factors in this study (stem moisture content, internode position and loading rate) were considered as the three input linguistic variables to the fuzzy logic model. These parameters were initially in the form of real values. First, these crisps were converted to fuzzy values by the fuzzy system. The fuzzy logics were applied using the Mamdani product (minimum) interface engine [1]. Then the fuzzy logics were processed. Finally, the fuzzy system defuzzified the results using center of maximum (COM) defuzzifier and provided a real value indicating the level of stem shear strength.

III. RESULTS AND DISCUSSION

A. Shearing Energy Based on the statistical analysis, the effects of moisture

content, internode position and loading rate were all significant on the shearing energy of rice stem at 1% probability level (P<0.01). The mean values of rice stem shearing energy at different levels of moisture content; loading rate and stem

internode position are presented in (Table I). The values of shearing energy for the rice stem varied from 101.31 to 426.91 mJ. The shearing energy decreased significantly towards the upper internode. The shearing energy was higher in the lower internodes, possibly due to the accumulation of more mature fibres in the stem. This effect of stem height on shearing energy was also reported by Nazari Galedar et al. [15] for alfalfa stem and Tavakoli et al. [17] for barley stem. The shearing energy increased with increases in the moisture content for all internodes. Similar trends were also reported by Annoussamy et al. [23] and Chen et al. [19] for wheat stem and hemp stalk, respectively. At all of the evaluated moisture contents, the shearing energy of rice stem increased by increasing loading rate (Table I).

Based on the results from this study, for cutting rice stem, doing this work at higher levels of the stem is recommended to minimize the shearing force and shearing energy requirements.

B. Fuzzy model establishment The process of the fuzzy logic model establishment in

MATLAB software is shown in Fig. 3 to Fig. 7. In Fig. 3, the overall form of the fuzzy model in MATLAB software is illustrated. In order to write the fuzzy rules for the first linguistic variable, stem moisture content, four membership functions namely, very low [0 15], low [10 40], medium [35 65], and high [60 80], all in terms of wet basis percentage were defined (Fig. 4). In the case of second linguistic variable, stem internode position, three membership functions including low [0 30], middle [25 70] and high [65 120], all in cm were defined (Fig. 5). For the third linguistic variable, cutting blade loading rate, three membership functions including low [0 5], medium [4 10] and high [9 15], all in terms of mm/min were assigned (Fig. 6). Three membership functions were also assigned to the output of the fuzzy system that was shearing energy. The three levels assigned to the shearing energy were low [0 150], middle [140 300], and high [280 450] in terms of mJ (Fig. 7). The ranges for all of the input and output variables were determined by an expert based on the values obtained through the experimental data (Table I).

Table I. Shearing Energy of Rice Straw at Different Moisture Contents, Loading Rates and Internode Positions.

Moisture content (%w.b.) Loading rate (mm/min) Shearing energy (mJ) Internode position*

Upper Middle Lower

10 5 101.31p 180.28kml 239.51jgih

10 122.76omp 228.18kjih 236.06jih

15 118.78omp 239.14jgih 256.02fegih

30 5 110.92op 200.34kjl 294.12fegd

10 132.88omnp 250.13fjgih 292.17fegd

15 127.73omnp 261.11fegh 311.19ed

50 5 136.49omnp 226.67kjih 300.08fed

10 161.52omln 279.99fegdh 297.33fed

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Fig. 3. Overall Form of Fuzzy Model for Estimating Shear Strength of Rice Stem

Fig. 4. The Membership Functions Used to Give a Description Range of Stem Moisture Content

Fig. 5. The Membership Functions Used to Give a Description Range of Stem Internode Position

Fig. 6. The Membership Functions Used to Give a Description Range of Cutting Blade Loading Rate

15 155.18omlnp 293.51fegd 321.39cd

70 5 169.90mln 308.72ed 402.45ab

10 198.78kjl 364.44cb 408.83ab

15 202.53kjl 382.51ab 426.91a

* Mean values with common index are not significantly different (P>0.05) according to Duncan’s multiple range test.

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Fig. 7. The Membership Functions Used to Give a Description Range of Stem shearing energy

The determination process of fuzzy rules for shearing

energy of the rice stem in MATLAB software is shown in Fig. 8. Combining the assigned membership functions and based on the results of the experimental measurements, 36 fuzzy rules were totally obtained using AND operator in fuzzy sets. The rules are listed in Table II. These rules were used to establish a relationship between the input and output variables.

Fig. 8. Determination of Fuzzy Rules for Shearing Energy of Rice Stem in MATLAB Software

C. Fuzzy model assessment After establishment of the fuzzy sets and fuzzy rules

determination, in order to validate the fuzzy model, the shearing energy of rice stems obtained through the experimental treatments were assessed and compared with those values acquired using the fuzzy logic rules. Fig. 9 shows the assessment of the applied rules in the fuzzy model in MATLAB software. The figure indicate the output of the fuzzy model considering the stem moisture content, internode position and loading rate at low, middle and high ranges, respectively. In such a condition, the model output is placed in

middle range. This is consistent with primary definitions of fuzzy rules in (See rule No. 14 in the Table II).

Fig. 10 presents a sample surface viewer of fuzzy system considering stem moisture content and internode position as input variables. The results of the model assessment by an expert person are given in Table III. The results showed that the fuzzy model used in this study had an acceptable prediction ability to determine the level of shear strength of rice stem. The model accuracy to estimate the shear strength of rice stem was 86%, 97% and 91%, respectively in high, middle and low ranges of shearing energy.

Fig. 9. Fuzzy Rules Assessments in MATLAB Software

Fig. 10. Surface viewer of fuzzy system considering stem moisture content and internode position as input variables

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Table II. Fuzzy Rules Obtained Through AND Operator in the Fuzzy Sets 1. If (Moisture is Very Low) and (Internode is Lower) and (Loading rate is Low) then (Shearing Energy is Medium) 2. If (Moisture is Very Low) and (Internode is Lower) and (Loading rate is Medium) then (Shearing Energy is Medium) 3. If (Moisture is Very Low) and (Internode is Lower) and (Loading rate is High) then (Shearing Energy is Medium) 4. If (Moisture is Very Low) and (Internode is Middle) and (Loading rate is Low) then (Shearing Energy is Medium) 5. If (Moisture is Very Low) and (Internode is Middle) and (Loading rate is Medium) then (Shearing Energy is Medium) 6. If (Moisture is Very Low) and (Internode is Middle) and (Loading rate is High) then (Shearing Energy is Medium) 7. If (Moisture is Very Low) and (Internode is Upper) and (Loading rate is Low) then (Shearing Energy is Low) 8. If (Moisture is Very Low) and (Internode is Upper) and (Loading rate is Medium) then (Shearing Energy is Low) 9. If (Moisture is Very Low) and (Internode is Upper) and (Loading rate is High) then (Shearing Energy is Low) 10. If (Moisture is Low) and (Internode is Lower) and (Loading rate is Low) then (Shearing Energy is High) 11. If (Moisture is Low) and (Internode is Lower) and (Loading rate is Medium) then (Shearing Energy is High) 12. If (Moisture is Low) and (Internode is Lower) and (Loading rate is High) then (Shearing Energy is High) 13. If (Moisture is Low) and (Internode is Middle) and (Loading rate is Low) then (Shearing Energy is Medium) 14. If (Moisture is Low) and (Internode is Middle) and (Loading rate is Medium) then (Shearing Energy is Medium) 15. If (Moisture is Low) and (Internode is Middle) and (Loading rate is High) then (Shearing Energy is Medium) 16. If (Moisture is Low) and (Internode is Upper) and (Loading rate is Low) then (Shearing Energy is Low) 17. If (Moisture is Low) and (Internode is Upper) and (Loading rate is Medium) then (Shearing Energy is Low) 18. If (Moisture is Low) and (Internode is Upper) and (Loading rate is High) then (Shearing Energy is Low) 19. If (Moisture is Medium) and (Internode is Lower) and (Loading rate is Low) then (Shearing Energy is High) 20. If (Moisture is Medium) and (Internode is Lower) and (Loading rate is Medium) then (Shearing Energy is High) 21. If (Moisture is Medium) and (Internode is Lower) and (Loading rate is High) then (Shearing Energy is High) 22. If (Moisture is Medium) and (Internode is Middle) and (Loading rate is Low) then (Shearing Energy is Medium) 23. If (Moisture is Medium) and (Internode is Middle) and (Loading rate is Medium) then (Shearing Energy is Medium) 24. If (Moisture is Medium) and (Internode is Middle) and (Loading rate is High) then (Shearing Energy is High) 25. If (Moisture is Medium) and (Internode is Upper) and (Loading rate is Low) then (Shearing Energy is Low) 26. If (Moisture is Medium) and (Internode is Upper) and (Loading rate is Medium) then (Shearing Energy is Medium) 27. If (Moisture is Medium) and (Internode is Upper) and (Loading rate is High) then (Shearing Energy is Medium) 28. If (Moisture is High) and (Internode is Lower) and (Loading rate is Low) then (Shearing Energy is High) 29. If (Moisture is High) and (Internode is Lower) and (Loading rate is Medium) then (Shearing Energy is High) 30. If (Moisture is High) and (Internode is Lower) and (Loading rate is High) then (Shearing Energy is High) 31. If (Moisture is High) and (Internode is Middle) and (Loading rate is Low) then (Shearing Energy is High) 32. If (Moisture is High) and (Internode is Middle) and (Loading rate is Medium) then (Shearing Energy is High) 33. If (Moisture is High) and (Internode is Middle) and (Loading rate is High) then (Shearing Energy is High) 34. If (Moisture is High) and (Internode is Upper) and (Loading rate is Low) then (Shearing Energy is Medium) 35. If (Moisture is High) and (Internode is Upper) and (Loading rate is Medium) then (Shearing Energy is Medium) 36. If (Moisture is High) and (Internode is Upper) and (Loading rate is High) then (Shearing Energy is Medium)

Table III. Assessment of the Fuzzy Logic Model Accuracy by an Expert Output MF Expert Fuzzy Accuracy (%) High 21 18 86 Middle 36 35 97 Low 23 21 91

IV. CONCLUSION

The aim of this study was to develop a fuzzy logic model to estimate the shear strength of rice stem considering the crop different harvest conditions. First, an experimental test set up a proprietary tension/compression testing machine (Instron Universal Testing Machine /SMT-5, SANTAM Company, Tehran, Iran) was made to obtain the real values of shearing energy for the stems in terms of stem moisture content, internode position and cutting blade loading rate. Based on the statistical analysis, the effects of the tested factors were all significant on the shearing energy of rice stem at

1% probability level (P<0.01). The results showed that the values of shearing energy for the rice stem varied from 101.31 to 426.91 mJ. The shearing energy decreased significantly towards the upper internode. It was higher at lower internodes, possibly due to the accumulation of more mature fibres in the stem. The shearing energy increased with increases in the moisture content for all internodes. At all of the evaluated moisture contents, the shearing energy of rice stem increased by increasing loading rate.

After measurement the shear strength of rice stem at different levels of experimental factors, the fuzzy model was developed. The three evaluated factors in this study (stem moisture content, internode position and loading rate) were considered as the three input linguistic variables to the fuzzy

COMPUTER SCIENCE AND APPLICATION, VOL. 1, NO. 2, FEBRUARY, 2012 11

logic model. The fuzzy logics were applied and processed using the Mamdani product (minimum) interface engine. After establishment of the fuzzy sets and fuzzy rules determination, in order to validate the fuzzy model, the shearing energy of rice stems obtained through the experimental treatments were assessed and compared with those values acquired using the fuzzy logic rules. The results showed that the fuzzy model used in this study had an acceptable prediction ability to determine the level of shear strength of rice stem. The model accuracy to estimate the shear strength of rice stem was 86%, 97% and 91%, respectively in high, middle and low ranges of shearing energy.

ACKNOWLEDGMENT The authors would like to thank the Rice Research Institute

of Iran (RRII) for providing the test stems of rice straw and University of Tehran for providing the laboratory facilities and financial support for this project.

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Hemad Zareiforoush received the B.S. and M.S. degrees in Mechanical Engineering of Agricultural Machinery from Urmia University, Urmia, Iran, in 2008 and 2010, respectively. He is currently pursuing his Ph.D. degree in Mechanical Engineering of Agricultural Machinery at the Tarbiat Modares University, Tehran, Iran. His research interests include application of automatic control systems, machine vision and postharvest technologies in food processing.

Alireza Mahdavian received the B.S. degree in Mechanical Engineering of Agricultural Machinery from Ferdowsi University of Mashhad, Mashhad, Iran, in 2009. He is currently pursuing his M.S. degree in Mechanical Engineering of Agricultural Machinery at the Tarbiat Modares University, Tehran, Iran. His research interest is Agrobotic.

Bahram Hosseinzadeh received the B.S. degree in Mechanical Engineering of Agricultural Machinery from Kerman University, Kerman, Iran, in 2006, the M.S. degree in Mechanical Engineering of Agricultural Machinery from Shahrekord University, Shahrekord, Iran, in 2009. He is currently pursuing his Ph.D. degree in Mechanical Engineering of Agricultural Machinery at the Tarbiat Modares University, Tehran, Iran. His research interests include application of artificial neural networks and automatic control systems in food processing.