Development of PHilMech Computer Vision System (CVS) for Quality Analysis of Rice and Corn

Manual analysis of rice and corn is done by visually inspecting each grain and classifying according to their respective categories.  This method is subjective and tedious leading to errors in analysis.  Computer vision could be used to analyze quality of rice and corn by developing models that correlate shape and color features with various classification. The PhilMech low-cost computer vision system (CVS) was developed to analyze the quality of rice and corn.  It is composed of an ordinary scanner as the image acquisition device and a computer with image-processing software. The performance of the CVS was compared to the traditional manual method being adopted by the National Food Authority (NFA) and the Agricultural Machinery Testing and Evaluation Center (AMTEC). The performance testing and evaluation showed that the accuracy of obtaining the results in classifying rice and corn using the CVS was comparable to the manual method of analysis. But, the processing time to complete the analysis using the CVS technology (6-7 minutes) was 5-8 times faster compared to the manual method (30-60 minutes). The developed CVS will automate the existing practice in determining the milling quality of brown rice, milled rice and yellow corn and minimize the tedious and subjective manual method of evaluation

Edge Detection Techniques for Rice Grain Quality Analysis using Image Processing Techniques

In agricultural countries like the Philippines, rice grain is considered the most important crop in the world for human consumption as daily food and in the food market, thus quality control must be considered. Rice grain quality evaluation is done manually, which is non-reliable, time-consuming and costly. The quality of rice grain is categorized by the combination of physical and chemical characteristics. Grain appearance, color, size and shape, chalkiness, whiteness, degree of milling, bulk density, foreign matter content, and moisture content are some physical characteristics, while amylose content of the endosperm, gelatinization temperature of the endosperm starch, and Na content are chemical characteristics. This paper presents a solution for the grading and evaluation of rice grains on the basis of grain size and shape using Scilab Image Video Progressing (SIVP) techniques. Specifically, an edge detection algorithm is used to find out the region of the boundaries of each grain. This method requires a minimum of time and is more affordable. Edge detection is vital for its reliability and security, as well as for providing a better understanding of automatic identification in computer vision applications. This study determines the best techniques among the edge detection algorithms

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

Rice is the source of Pakistan agriculture industry and food. For agriculture, industry and the oldest sector in the world use rice for different purpose. There are many challenges in the particular sector such as their analysis. This analysis mostly often related to its texture, color, shape, grain etc. In this study, Vision system used to check the quality of rice using some texture features such as color, shape and characteristics. In this study Computer Vision Image Processing tool applied on three different types of rice. Using this tool we apply pattern classification using nearest neighbor and K-nearest neighbor algorithm. Using these algorithms we get results of three varieties of rice Bastmati, Jasmine and White rice. In both algorithms white rice result show best from Basmati rice and Jasmine rice. White rice result is 93.75 % which is best as quality wise. Other tool also available like as MATLAB, Mazda etc for future more best prediction. Keywords: RST-Invariant features, Histogram features, Texture features, Nearest Neighbor algorithm, K-nearest neighbor algorithm DOI: 10.7176/CEIS/11-2-01 Publication date: February 29th 202

Physical and Chemical Characterization of Rice Using Microwave and Laboratory Methods

Two main species of cultivated rice in the world are Oryza sativa (Asian rice) and Oryza glaberrima (African rice). The Oryza sativa species, which is grown worldwide, is far more widely utilized compared with the Oryza glaberrima species, which is grown in West Africa. Recently, the annual rice production has reached almost 480 million tonnes, and this demand is expected to rise to 550 million tonnes in 2035. Thus, this increases the need to characterize and maintain the quality of rice and hence to determine the price of rice appropriately. Obviously, modern technologies that can provide fast and accurate measurement are essential in the large-scale industrial rice processing. In this chapter, several technologies and instruments used for rice processing are reviewed. The principle of the measurement for each technology is briefly described. The strength of this chapter is to introduce the application of microwave technology during rice processing, such as rice dying process, rice moisture detection, broken rice measurement and rice insect control. The pros and cons of the microwave method will be discussed in detail. Hence, some standard test laboratory for monitoring of carbohydrate, protein, fat and trace elements content is also described in this chapter

Economic Fluctuations in Japan during the Interwar Period -- Re- estimations of the LTES Personal

To date, most research on Interwar Period economic fluctuations in Japan has been based on Estimates of Long-term Economic Statistics of Japan (LTES), edited by Kazushi Ohkawa et al. Regardless, the LTES data are just one set of estimations. They require scrutiny, especially for the measurement of personal consumption, which has a high weight in GNE. This paper re-estimates the LTES personal consumption expenditures by adjusting the estimation methods for certain expense categories and deducting imputations (which may have a large measurement error), and then calculates real GDP (adjusted real GDP) focusing on the market economy. The re-estimation presents no major changes from the LTES in the shape of the economic fluctuations of the 1920s, when the Japanese economy continuously posted gunbalanced growth.h From the Showa Depression forward, however, while the LTES shows continued positive real GDP growth, the re-estimation indicates negative growth in adjusted real GDP in 1931. These findings remain robust after considering the bias from the deflator formula. Given the characteristics of national accounts and the measurement error, these re-estimation results suggest that the severity of the Showa Depression may have been underestimated in the prior research.Japanese Economy, Interwar Period, Showa Depression, Great Depression, Personal Consumption, National Accounts, Deflator, Imputation

A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice

The evaluation of yield-related traits is an essential step in rice breeding, genetic research and functional genomics research. A new, automatic, and labor-free facility to automatically thresh rice panicles, evaluate rice yield traits, and subsequently pack filled spikelets is presented in this paper. Tests showed that the facility was capable of evaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440 plants per continuous 24 h workday

Preserving Rice Quality: Fine-Mapping and Introgressing a Fissure Resistance Locus

Rice (Oryza sativa L.) kernel fissuring is a major concern of both rice producers and millers. Fissures are small cracks in rice kernels that increase breakage of kernels when milled, and decrease the value of processed rice. This study employed molecular gene tagging methods to fine-map a fissure resistance (FR) locus found in ‘Cybonnet’, a semidwarf tropical japonica cultivar, as well as transfer this trait to rice genotypes of taller, non-semidwarf plant height that are better adapted to some rice production systems. Three QTLs for FR were previously reported; the FR locus with strongest effect resides near the semidwarf sd-1 locus on the long arm of chromosome 1, explaining associations observed between increased FR and reduced plant height. This study began with F2 progeny from a cross between a U.S. inbred breeding line with non-semidwarf (Sd-1/Sd-1) plant height and poor milling yields, and Cybonnet, which is semidwarf (sd-1/sd-1) and noted for having improved milling quality due to increased FR. Simple sequence repeat (SSR) molecular markers were used to select 11 F2 progeny plants that retained at least one copy of the Sd-1 allele, but also contained evidence of genetic recombination in the region of chromosome 1, known to contain Sd-1 and qFIS1-2, so that the positon of qFIS1-2 relative to Sd-1 could be determined more precisely, and so that FR allele could be recombined with the Sd-1 allele. Three of the 11 selected plants were also homozygous at the two known FR QTLs that are not closely linked to sd-1; another four plants were homozygous at one but not both of the two additional FR loci. The F2:3 progeny generated were genotyped prior to being phenotyped; only individuals homozygous for the new recombination underwent extensive evaluation for FR. Progeny from three of 11 populations have been phenotyped. Marker-trait linkages observed in the first two populations indicated that qFIS1-2 resides distal to RM1068. Research efforts were then focused on just those populations whose recombination points were distal to RM1068 (i.e., at a base pair location higher 1:38439184). Results from the three populations observed to date indicate that the qFIS1-2 locus resides distal to RM1068 at 1:38439184 but anterior to RM3482 at 1:39720039, or approximately 6 to 10 cM distal to sd-1 on chromosome 1. The recombination documented in this study verifies that the previously identified qFIS1-2 is linked to, but not pleiotropic with, sd-1 and thus can be recombined with Sd-1 during introgression breeding to increase the FR of rice cultivars having non-semidwarf stature