JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

More documents

Recommendations

Info

Norhashila Hashim et al. Post-hoc analysis showed that both the levels of temperature were statistically different (Table 3). This means that each level of temperature gives different effects to the CI symptoms. Bananas stored at 6°C were severely affected by CI, while bananas stored at 13°C were not affected and ripened normally. TABLE 1: ANOVA of CI factors Source DF Mean Square F Value Pr > F Treatment Days 2 134.5183099 138.71
CONCLUSION Computer Vision of Chilling Injury in Bananas The results of this study have successfully demonstrated the potential of applying computer vision in the detection of CI in bananas. In particular, treatment days and temperature level are important factors affecting the severity of the CI symptoms. The correlation between hue and visual assessment values indicates a high potential for the system to replace the conventional method and to offer objectivity in and automation possibility to the sorting process. ACKNOWLEDGMENTS The authors are grateful for the financial support received from the Ministry of Higher Education Malaysia, Universiti Putra Malaysia and Leibniz Institute for Agricultural Engineering Potsdam- Bornim (ATB), Germany, for this project. REFERENCES Hue 70 68 66 64 62 60 58 Abd El-Wahab, F. K., & Nawwar, M. A. M. (1977). Physiological and biochemical studies on chillinginjury of banana. Scientia Hoticulturae, 7(4), 373-376. Beyer, M., Hahn, R., Peschel, S., Harz, M., & Knoche, M. (2002). Analysing Fruit Shape in Sweet Cherry (Prunus avium L.). Scientia Horticulturae, 96(1-4), 139–150. Brosnan, T., & Sun, D.-W. (2004). Improving Quality Inspection of Food Products by Computer Vision-a Review. Journal of Food Engineering, 61, 3-16. Broughton, W. J., & Wu, K. F. (1979). Storage condition and ripening of two cultivars of banana. Scientia Horticulturae, 10(1), 83-93. Chen, Y. R., Chao, K., & Kim, M. S. (2002). Machine Vision Technology for Agricultural Applications. Computers and Electronics in Agriculture, 36, 173-191. Cheng, H. D., Jiang, X. H., Sun, Y., & Wang, J. (2001). Color Image Segmentation: Advances and Prospects. Pattern Recognition, 34(12), 2259-2281. Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) R = 0.852 56 0 1 2 3 4 5 Visual Assessment (Browning Scale) Fig.5: The correlation between hue values and visual assessment 117
Page 2 and 3:
Journal of Science & Technology Jou
Page 4 and 5:
International Advisory Board Adarsh
Page 6 and 7:
ut bovine milk allergy by far is th
Page 8 and 9:
Selected Articles from UPM-Malaysia
Page 11 and 12:
ISSN: 0128-7680 © 2013 Universiti
Page 13 and 14:
Aspect of Fatigue Analysis of Compo
Page 15 and 16:
Page 17 and 18:
Page 19 and 20:
Page 21 and 22:
Page 23 and 24:
Page 25 and 26:
Page 27 and 28:
Application of Anthropometric Dimen
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
Different Media Formulation on Bioc
Page 43 and 44:
Page 45:
Page 48 and 49:
Heng, S. C., Ibrahim, Z. B., Suleim
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
CONCLUSION Heng, S. C., Ibrahim, Z.
Page 56 and 57:
Y. Yusuf, J. M. Juoi, Z. M. Rosli,
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Surface Roughness Y. Yusuf, J. M. J
Page 64 and 65:
DISCUSSION Y. Yusuf, J. M. Juoi, Z.
Page 66 and 67:
Page 69 and 70:
Page 71 and 72:
Modelling of Motion Resistance Rati
Page 73 and 74:
The Empirical Method Modelling of M
Page 75 and 76: Modelling of Motion Resistance Rati
Page 85 and 86: ISSN: 0128-7680 © 2013 Universiti
Page 87 and 88: Assessment of Heavy Metal Pollution
Page 89 and 90: TABLE 1: Size of mussels in average
Page 93 and 94: TABLE 5: Heavy metal concentrations
Page 109 and 110: Physico-Chemical and Electrical Pro
Page 115 and 116: TABLE 1: (continued) Physico-Chemic
Page 119: Physico-Chemical and Electrical Pro
Page 122 and 123: Norhashila Hashim et al. effectiven
Page 124 and 125: Statistical Analysis Norhashila Has
Page 128 and 129: Norhashila Hashim et al. Cubero, S.
Page 130 and 131: S. Ismail and K. A. Mohd Atan 4 4 p
Page 132 and 133: S. Ismail and K. A. Mohd Atan The f
Page 134 and 135: Lemma 1.1 The equation S. Ismail an
Page 136 and 137: CONCLUSION S. Ismail and K. A. Mohd
Page 138 and 139: INTRODUCTION Tajau, R. et al. A hig
Page 140 and 141: Tajau, R. et al. the acrylate’s c
Page 142 and 143: Tajau, R. et al. double bond) and C
Page 144 and 145: Tajau, R. et al. Ulanski, P., & Ros
Page 146 and 147: Aji, I. S., Zinudin, E. S., Khairul
Page 148 and 149: Aji, I. S., Zinudin, E. S., Khairul
Page 150 and 151: CONCLUSION Aji, I. S., Zinudin, E.
Page 152 and 153: D. Bachtiar, S. M. Sapuan, E. S. Za
Page 156 and 157: TABLE 1: (continue) 4 D. Bachtiar,
Page 162 and 163: N. Maizatul, I. Norazowa, W. M. Z.
Page 168 and 169: REFERENCES N. Maizatul, I. Norazowa
Page 173 and 174: CBIR Using Colour and Shape Fused F
Page 175 and 176: CBIR Using Colour and Shape Fused F
Page 177 and 178:
CBIR Using Colour and Shape Fused F
Page 179 and 180:
Page 181 and 182:
Toward Automatic Semantic Annotatin
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
Issues on Trust Management in Wirel
Page 197 and 198:
Page 199 and 200:
Trust Value MF-ID 1 0.8 0.6 0.4 0.2
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
A Negation Query Engine for Complex
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
REFERENCES A Negation Query Engine
Page 215 and 216:
Page 217 and 218:
The Problem Association Rule Mining
Page 219 and 220:
Association Rule Mining threshold t
Page 221 and 222:
Association Rule Mining must be sor
Page 223 and 224:
Association Rule Mining On the othe
Page 225:
Association Rule Mining Quinlan, J.
Page 228 and 229:
Che Mustapha Yusuf, J., Mohd Su’u
Page 230 and 231:
Page 232 and 233:
Page 234 and 235:
Page 236 and 237:
Page 238 and 239:
Az Azrinudin Alidin and Fabio Crest
Page 240 and 241:
Page 242 and 243:
Page 244 and 245:
Page 246 and 247:
Page 248 and 249:
Page 250 and 251:
Yogan Jaya Kumar, Naomie Salim, Ahm
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
REFERENCES Yogan Jaya Kumar, Naomie
Page 258 and 259:
Hasiah Mohamed@Omar, Azizah Jaafar
Page 260 and 261:
Page 262 and 263:
Page 264 and 265:
Page 266 and 267:
Page 268 and 269:
Page 271 and 272:
Page 273 and 274:
The Role of Similarity Measurement
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
TABLE 5: Winners MRS The Role of Si
Page 283:
REFEREES FOR THE PERTANIKA JOURNAL
Page 286 and 287:
Guidelines for Authors Publication
Page 288 and 289:
table should be prepared on a separ
Page 290 and 291:
The Journal’s review process What
Page 293 and 294:
Usability of Educational Computer G
show all

JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

Create successful ePaper yourself

Delete template?

Save as template?