Aimi Athirah Aznan
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Aimi Athirah Aznan
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Aimi Athirah, Aznan
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Aimi Athirah Aznan
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57193027562

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Drying performance evaluation of a commercial batch mixed-flow seed drying system

2020-01-01 , Azmi M.Z. , Rukunudin I.H. , Hirun Azaman Ismail , Aimi Athirah Aznan

Information on the performance of commercial grain dryers in Malaysia especially for paddy seed drying in terms of published reports are still lacking. This study was therefore undertaken to evaluate the performance of a commercial, mixed-flow, batch type grain dryer at Loji Pemprosesan Benih Padi Sah (A) Telok Chengai, Kedah. Five type of performance indices under three different fan configurations were studied, namely drying efficiency (DE), heat utilization factor (HUF), effective heat efficiency (EHE), coefficient of performance (COP) and specific energy consumption (SEC). From the results obtained, the average values for the performance indices for the seed dryer measured as DE. HUF, EHE and COP were found to range between 0.521 to 0.596, 0.695 to 0.708, 0.477 to 0.515 and 0.292 to 0.305 respectively. Depending on the fan's configurations, the SEC values registered by the dryer ranged between 5.677 to 6.896 MJ/kg water removed. This study also showed that different performance indices are suited for different conditions of the drying. As for an instance, HUF, EHE and COP are more suited for in-field drying evaluation while DE is more suited for a comprehensive evaluation of a dryer. SEC, on the other hand provides the most comprehensible results among the methods.

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Review of technology advances to assess rice quality traits and consumer perception

2023 , Aimi Athirah Aznan , Claudia Gonzalez Viejo , Alexis Pang , Sigfredo Fuentes

The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.

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Identification of cultivated rice MR 263 seed and weedy rice seed variants using CCD camera based-machine vision system

2015 , Aimi Athirah Aznan

The main purpose of this study was to develop rice seed identification research prototype system to classify cultivated rice and weedy rice seeds variants using machine vision system through the extraction of morphological, colour, and textural features of the seeds. Five different types of weedy rice seeds variants samples of open panicle, close panicle and awn type were collected from several commercial farms in Kedah. The MR 263 seed was obtained from a commercial rice seed bag from a local supplier. In this study, seed samples were consisted of 600 seeds of MR 263 and 600 seeds from weedy rice seed variants group. Images of the rice seed samples were acquired using a charge coupled device (CCD) colour camera. Laboratory Virtual Instrument Engineering Workbench (LabVIEW) development environment was used to program the image processing, features extraction and the classification analysis. There was 12 morphological, 6 colour and 5 textural features were extracted from the seed images. Four types of classification model namely morphology, colour, texture and morphologycolour- texture models were established based on the extracted data. Each of the models was analyzed for feature selection using stepwise discriminant analysis (SDA) to develop the optimized features model. Then, the original and optimized features models were analyzed using 3 classifiers; discriminant function analysis (DFA), support vector machine (SVM) and neural network (NN). Analysis of variance (ANOVA) was conducted on the 3 classifiers to evaluate the mean classification accuracy levels of the 8 extracted features models developed. The ANOVA showed that there is no significant difference of mean classification accuracies between the 3 classifiers. The classification results using morphology-colour-texture features model was found to obtain higher classification accuracy levels as compared to the single feature models. An identification system was developed in the LabVIEW to classify the cultivated rice MR 263 and weedy rice seed groups using optimized features of the morphology-colourtexture model in DFA. The developed system was able to classify both seed groups at 99.4% accuracy level using testing data set.

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