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Table 1 Fuzzy if-then rules for classification 8 Sorting and Grading Methodology Using Fuzzy Logic Technique

Table 1 Fuzzy if-then rules for classification 8 Sorting and Grading Methodology Using Fuzzy Logic Technique

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Fig. 1 Proposed model of machine vision based automated mango fruit sorting and grading system
Fig. 1 Proposed model of machine vision based automated mango fruit sorting and grading system
Fig. 2. Images of five varieties mango having different maturity level. Ist row: “KU”, 2nd row: “SO”, 3rd row :“LA” and 4th row: “HI” and Sth row: “AM”. Images are taken with an interval of 2 days, shown in (a) Ist colomn: raw (M1) (b) 2nd colomn: semimatured (M2) (c) 3rd colomn: matured (M3) (d) 4th colomn: over-matured (M4)
Fig. 2. Images of five varieties mango having different maturity level. Ist row: “KU”, 2nd row: “SO”, 3rd row :“LA” and 4th row: “HI” and Sth row: “AM”. Images are taken with an interval of 2 days, shown in (a) Ist colomn: raw (M1) (b) 2nd colomn: semimatured (M2) (c) 3rd colomn: matured (M3) (d) 4th colomn: over-matured (M4)
Fig. 3 Extracted still frames from the video image with an interval of 5 frames, (a) frame no.10, sample entering into the imaging chamber (b) frame no.15, sample near the middle of the imaging chamber (c) frame no. 20, sample crossing the middle position of imaging chamber (d) frame no. 25, sample going out from imaging chamber Though the images were taken in controlled environment under fixed illumination of light of 120 lux with the help of tungsten filament lamp, but there were some noises in the picture. To remove these noises a simple median filter found to provide reasonable good performance but it is computationally intensive. For that pseudo-median filter [19] was used in the work, as it is computationally simpler and possesses many of the properties of the median filter. This filtering process often helped to obtain smooth continuous boundary of the mango.
Fig. 3 Extracted still frames from the video image with an interval of 5 frames, (a) frame no.10, sample entering into the imaging chamber (b) frame no.15, sample near the middle of the imaging chamber (c) frame no. 20, sample crossing the middle position of imaging chamber (d) frame no. 25, sample going out from imaging chamber Though the images were taken in controlled environment under fixed illumination of light of 120 lux with the help of tungsten filament lamp, but there were some noises in the picture. To remove these noises a simple median filter found to provide reasonable good performance but it is computationally intensive. For that pseudo-median filter [19] was used in the work, as it is computationally simpler and possesses many of the properties of the median filter. This filtering process often helped to obtain smooth continuous boundary of the mango.
Fig. 4 Filtered images of Mango, a raw mango image along with the obtained contour, b binary image of the same mango after removing the small patches, also shows the different positions and their name as used in the work In order to find the boundary or the contour of the mango, a graph contour tracking method based on chain-code was adopted. This algorithm found to work reasonable fast, as the boundary of the mango is not so complex. The details of algorithm can be found in [20]. Here a brief description is given. The algorithm first detects every run at each row and records every single run’s serial number and the corresponding start- pixel coordinates and end-pixel coordinates are stored in a table named as “ABS”. Through this method, the run-length code of the image is obtained. Then, a 3 x 3 pixel box is adopted to detect the relationship between the objective pixel and its eight-connected surrounding pixels. All the runs are categorized into 5 classes, and then each run’s serial number along with the corresponding class label was recorded in another table named as “COD”. Then, the algorithm searches the ABS-table and COD-table sequentially. Based on the coordinates and class of each run recorded on the table, the starting pixel of the contour is recognized and the contour is successfully followed. And the chain code is generated while following the contour. An image of mango along with the traced boundary is shown in Fig. 4.
Fig. 4 Filtered images of Mango, a raw mango image along with the obtained contour, b binary image of the same mango after removing the small patches, also shows the different positions and their name as used in the work In order to find the boundary or the contour of the mango, a graph contour tracking method based on chain-code was adopted. This algorithm found to work reasonable fast, as the boundary of the mango is not so complex. The details of algorithm can be found in [20]. Here a brief description is given. The algorithm first detects every run at each row and records every single run’s serial number and the corresponding start- pixel coordinates and end-pixel coordinates are stored in a table named as “ABS”. Through this method, the run-length code of the image is obtained. Then, a 3 x 3 pixel box is adopted to detect the relationship between the objective pixel and its eight-connected surrounding pixels. All the runs are categorized into 5 classes, and then each run’s serial number along with the corresponding class label was recorded in another table named as “COD”. Then, the algorithm searches the ABS-table and COD-table sequentially. Based on the coordinates and class of each run recorded on the table, the starting pixel of the contour is recognized and the contour is successfully followed. And the chain code is generated while following the contour. An image of mango along with the traced boundary is shown in Fig. 4.
Fig. 5 Variation of R, G and B value along the longitudinal axis
Fig. 5 Variation of R, G and B value along the longitudinal axis
Fig. 6 a Variation of average R of entire mango with maturity level(day), b variation of average R in apex region with maturity level(day), ¢ variation of difference of average R and G in equator with maturity level(day), d variation of average R to average G with four different maturity level(i.e. M1, M2, M3 and M4 of KU)
Fig. 6 a Variation of average R of entire mango with maturity level(day), b variation of average R in apex region with maturity level(day), ¢ variation of difference of average R and G in equator with maturity level(day), d variation of average R to average G with four different maturity level(i.e. M1, M2, M3 and M4 of KU)
Fig. 7 a PDF distribution with average value of B and G of entire mango, b PDF distribution with the data set of mango collected from different batches and different gardens, and ¢ box-whiskers plots for most corelated features
Fig. 7 a PDF distribution with average value of B and G of entire mango, b PDF distribution with the data set of mango collected from different batches and different gardens, and ¢ box-whiskers plots for most corelated features
Fuzzy rule based algorithm is used to sort the mango fruit into four quality grades ike poor (Q1), medium (Q2), good (Q3) and very good (Q4) based on their maturity evel and size. The grading system has two inputs (maturity level and size) and one yutput (quality). Total 16 fuzzy if-then rules are created in order to sort the mango ruits into four different quality levels (Q1, Q2, Q3, and Q4). The fuzzy rule base s shown in Table 1. Examples of some rules are illustrated as in Table2. The fuzzy nference system is shown in Fig. 10. The rule viewer shown in Fig. 13 consists of the system’s inputs and output col-
Fuzzy rule based algorithm is used to sort the mango fruit into four quality grades ike poor (Q1), medium (Q2), good (Q3) and very good (Q4) based on their maturity evel and size. The grading system has two inputs (maturity level and size) and one yutput (quality). Total 16 fuzzy if-then rules are created in order to sort the mango ruits into four different quality levels (Q1, Q2, Q3, and Q4). The fuzzy rule base s shown in Table 1. Examples of some rules are illustrated as in Table2. The fuzzy nference system is shown in Fig. 10. The rule viewer shown in Fig. 13 consists of the system’s inputs and output col-
Fig. 10 Fuzzy inference systems
Fig. 10 Fuzzy inference systems
Fig. 8 Flowchart of sorting and grading process
Fig. 8 Flowchart of sorting and grading process
Fig. 9 Flowchart of Fuzzy Inference System
Fig. 9 Flowchart of Fuzzy Inference System
Fig. 11 Input membership functions representing maturity level (a) and size (b)
Fig. 11 Input membership functions representing maturity level (a) and size (b)
Fig. 12 Membership function representing quality output Table 2 Fuzzy if-then rules
Fig. 12 Membership function representing quality output Table 2 Fuzzy if-then rules
Table 3 Fuzzification algorithm
Table 3 Fuzzification algorithm
Fig. 13 Defuzzification results from rule viewer
Fig. 13 Defuzzification results from rule viewer
Table 4 Performance analysis
Table 4 Performance analysis
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