ORCID Profile
0000-0002-5916-5745
Current Organisation
Universiti Putra Malaysia
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Publisher: Walter de Gruyter GmbH
Date: 16-04-2016
Abstract: This study seeks to investigate the effects of temperature (50, 60, 70 and 80 °C) and material thickness (3, 5 and 7 mm), on the drying characteristics of pumpkin ( Cucurbita moschata ). Experimental data were used to estimate the effective moisture diffusivities and activation energy of pumpkin by using solutions of Fick’s second law of diffusion or its simplified form. The calculated value of moisture diffusivity with and without shrinkage effect varied from a minimum of 1.942 × 10 –8 m 2 /s to a maximum of 9.196 × 10 –8 m 2 /s, while that of activation energy varied from 5.02158 to 32.14542 kJ/mol with temperature ranging from 50 to 80 °C and slice thickness of 3 to 7 mm at constant air velocity of 1.16 m/s, respectively. The results indicated that with increasing temperature, and reduction of slice thickness, the drying time was reduced by more than 30 %. The effective moisture diffusivity increased with an increase in drying temperature with or without shrinkage effect. An increase in the activation energy was observed due to an increase in the slice thickness of the pumpkin s les.
Publisher: Elsevier BV
Date: 11-2018
Publisher: MDPI AG
Date: 23-11-2021
DOI: 10.3390/AGRICULTURE11121179
Abstract: The quality of palm oil depends on the maturity level of the oil palm fresh fruit bunch (FFB). This research applied an optical spectrometer to collect the reflectance data of 96 FFB from unripe, ripe, and overripe classes for the maturity level classification. The spectrometer scanned the FFB from different parts, including apical, front equatorial, front basil, back equatorial, and back basil. Principal component analysis was carried out to extract principal components from the reflectance data of each of the parts. The extracted principal components were used in an ANOVA test, which found that the reflectance data of the front equatorial showed statistically significant differences between the three maturity groups. Then, the collected reflectance data was subjected to machine learning training and testing by using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The front equatorial achieved the highest accuracy, of 90.6%, by using SVM as classifiers thus, it was proven to be the most optimal part of FFB that can be utilized for maturity classification. Next, the front equatorial dataset was ided into UV (180–400 nm), blue (450–490 nm), green (500–570 nm), red (630–700 nm), and NIR (800–1100 nm) regions for classification testing. The UV bands showed a 91.7% accuracy. After this, representative bands of 365, 460, 523, 590, 623, 660, 735, and 850 nm were extracted from the front equatorial dataset for further classification testing. The 660 nm band achieved an 89.6% accuracy using KNN as a classifier. Composite models were built from the representative bands. The combination of 365, 460, 735, and 850 nm had the highest accuracy in this research, which was 93.8% with the use of SVM. In conclusion, these research findings showed that the front equatorial has the better ability for maturity classification, whereas the composite model with only four bands has the best accuracy. These findings are useful to the industry for future oil palm FFB classification research.
Publisher: Universiti Malaysia Pahang Publishing
Date: 30-12-2014
Publisher: Informa UK Limited
Date: 08-08-2017
Publisher: Elsevier BV
Date: 2018
Publisher: Wiley
Date: 04-02-2016
Publisher: Springer Science and Business Media LLC
Date: 03-06-2014
Publisher: Hindawi Limited
Date: 09-2022
DOI: 10.1155/2022/5385505
Abstract: Oil palm has become one of the largest plantation industries in Malaysia, but the constraints in terms of manpower and time to monitor the development of this industry have caused many losses in terms of time and expense of oil palm plantation management. The introduction to the use of drone technology will help oil palm industry operators increase the effectiveness in the management of oil palm cultivation and production. In addition, knowledge gaps on drone technology were identified, and suggestions for further improvement could be implemented. Therefore, this study reviews the application and potential of drone technology in oil palm plantation, and the limitation and potential of the methods will be discussed.
Publisher: MDPI AG
Date: 09-03-2022
DOI: 10.3390/AGRIENGINEERING4010020
Abstract: A combine harvester has been widely employed for harvesting paddy in Malaysia. However, it is one of the most challenging machines to operate when harvesting grain crops. Improper handling of a combine harvester can lead to a significant amount of grain loss. Any losses during the harvesting process would result in less income for the farmers. Grain loss sensing technology is automated, remote, and prospective. It can help reduce grain losses by increasing harvesting precision, reliability, and productivity. Monitoring and generating real-time sensor data can provide effective combine harvester performance and information that will aid in analyzing and optimizing the harvesting process. Thus, this paper presents an overview of the conventional methods of grain loss measurements, the factors that contribute to grain losses, and further reviews the development and operation of sensor components for monitoring grain loss during harvest. The potential and limitations of the present grain loss monitoring systems used in combine harvesting operations are also critically analyzed. Several strategies for the adoption of the technology in Malaysia are also highlighted. The use of this technology in future harvesting methods is promising as it could lead to an increase in production, yield, and self-sufficiency to meet the increasing demand for food globally.
Publisher: Elsevier BV
Date: 06-2013
Publisher: SAGE Publications
Date: 2013
DOI: 10.1255/JNIRS.1060
Abstract: The need for a reliable in-field quality measurement in the sugarcane industry is growing as the quality of sugarcane could vary significantly across the field. However, current monitoring systems in this industry only monitor crop yield and do not have the ability to measure the product quality. Thus, the potential of the visible/shortwave near infrared (vis/SW-NIR) spectroscopic technique as a low-cost alternative to predict sugar content from sugarcane stalks was investigated. Two hundred and ninety-two internode s les were extracted from three different sugarcane varieties to assess the ability of this technique. Each s le was cut into four sections and the spectra collected from the cross-sectional surface of each section were later correlated with its sugar content (°Brix). Partial least square (PLS) models were developed using calibration s les. The best model predicted s les in a prediction set had a coefficient of determination ( r 2 ) of 0.87 and root means square error of prediction ( RMSEP) of 1.45°Brix. The value of the ratio of the standard deviation to the standard error of prediction ( RPD) was 2. The variations of °Brix and prediction accuracy along the in idual internode were 8.7 and 13%, respectively. These results indicated the vis/SW-NIR spectroscopy could be applied to predict °Brix values from sugarcane stalks based on a cross-sectional scanning method.
Publisher: Elsevier BV
Date: 05-2019
Publisher: Elsevier BV
Date: 08-2010
Publisher: SPIE
Date: 17-05-2013
DOI: 10.1117/12.2029395
Publisher: Elsevier BV
Date: 03-2010
No related grants have been discovered for NAZMI MAT NAWI.