Digital Image Based Identification of Rice Variety Using Image Processing and Neural Network

Abstract

The increased of consumer concern on the originality of rice variety and the quality of rice leads to originality certification of rice by existing institutions. Technology helps human to perform evaluations of food grains using images of objects. This study developed a system used as a tool to identify rice varieties. Identification process was performed by analyzing rice images using image processing. The analyzed features for identification consisted of six color features, four morphological features, and two texture features. Classifier used LVQ neural network algorithm. Identification results using a combination of all features gave average accuracy of 70,3% with the highest classification accuracy level of 96,6% for Mentik Wangi and the lowest classification accuracy of 30% for Cilosari.

Design of Door Security System Based on Face Recognition with Arduino

“…The many uses of digital cameras as a supporting tool in everyday human matters are supported by the many research and development of digital image processing technology in various fields such as robotics, medical, entertainment, and security. As technologies develop nowadays, digital image processing techniques and artificial intelligence can be used for analysis of the object on digital image such as for motorcycle detection [1], renal tumor detection [2], virtual reality robot [3], blood typing [4], rice variety identification [5], ship detection [6], and analysis of road damage [7].fruit planting time.…”

Detection of Ship Using Image Processing and Neural Network

“…From these recordings, ships detection is performed, notably those exercising illegal fishing. As technologies develop nowadays, digital image processing techniques and artificial intelligence can be used for analysis of the object on digital image such as for motorcycle detection [2][3][4], renal tumor detection [5], traffic monitoring [6], virtual reality robot [7], motorcyclist's helmet wearing detection [8], blood typing [9], cotton pests detection [10], rice variety identification [11] and analysis of road damage [12]. Meanwhile, the studies on ship detection have been complied by percolating several methods, such as Support Vector Machine (SVM) [13][14], visual saliency and histogram [15], Discrete Wavelet Transform (DWT) [16], combination statistical methods, mathematical morphology and other signalprocessing techniques [17], and sea surface analysis [18].…”