Application of Image Processing and Computer Vision on Rice Seed Germination Analysis

This paper presents a machine vision application designed for rice seed germination analysis by using image processing and computer vision technology. The application is called “Rice Seed Germination Analysis (RSGA)". RSGA consists of five main processing modules which are image acquisition, image preprocessing, feature extraction, quality control analysis and quality results. The experiments is conducted on six variation Thai rice seed species of CP111, RD41, Chiang Phatthalung, Sang Yod Phattalung, Phitsanulok 2 and Chai Nat 1 in Bangkok and Chiangmai province of Thailand. RSGA extracts four main features which are color, size, shape, and texture. Then, RSGA applies Artificial Neural Network techniques in crop germination prediction. The precision rate is 93.06 percent, with the speed 8.31 seconds per image. Introduction Thailand is one of agricultural countries which produces a hugh number of food products each year. Thailand is one of agricultural countries which produces a huge number of food products each year. Nowadays, the agricultural industry is probably more widespread in the world. One product that is highly widespread in the world especially in Thailand is rice. Oryza Sativa (Rice) is a vital worldwide agricultural product. Rice is a very popular exported product in the world. In Thailand, there are more than 114 well-known Thai rice species. It is essential to grade the quality of commodities in order to command the better price in the market competitions. The quality of the rice is mostly based on the quality of the seeds. Paddy rice is mostly offered based on the good quality of products. To measure the seed quality assessment, many factors is considered in the germination test, for instance, seed quality additives, species purity, physical purity, insect pests and diseases, seed germination and seed vigor. However, it is very difficult to identify which kind of seeds should be better used in seed germinations. Thus, this research is applied the image processing and computer vision technique instead of using only human vision. The traditional way to conduct the germination test is based on human abilities, it takes times and high labor to conduct the germination test in the seed quality control process. Nevertheless, it is very difficult to identify the quality of rice seeds by using only human vision because it is time-consuming and uses high labor to assess the quality control process in order to earn benefits from the rice productive. The germination test in this research was applied following the standard seed germination test in ISTA (1996) with the top of paper method for crop germination. Moreover, images have been collected to predict the germination of rice seed images by using image processing techniques which can identify the quality of products. Therefore, the objective of this research is to propose the novel technique by applying the image processing and computer vision technology to assess the crop germination prediction in order to reduce times and costs. Thus, the computer software which can predict rice seed image for crop germination by using image processing techniques is developed. Due to the advance of video camera technology, people can take a digital picture or digital video stream easily in any places and any time by a camera or by a mobile phone device. It is not only very easy to use a digital camera, but also it is inexpensive. Moreover, it is easy to transform and process by using a computer system. Thus, this research employs a digital camera to capture the image. Many researches have been conducted the rice seed classification and rice seed recognition systems based on varieties, shape and size [7-8, 11, 13-16, 18, 20-22, 24]. The system of this research aimed to allow users to load an unknown whole rice seed image into RSGA which the system attempts to predict which kind of seeds were germinated or non-germinated for crop germination. Finally, the RSGA displays the germination results on the system’s graphic user interface (GUI). Many experiments were conducted to evaluate the seed germination by using machine vision technology. Related Work Many researchers tried to classify and identify each rice seed by applying several techniques which can be classified into two categories: 2.1) Computer techniques and 2.2) Biological techniques. These below details show the information relating to the techniques used in this research. For computer techniques, Pornpanomchai [17] classified pattern recognition approaches into three main categories: 1) Statistical pattern recognition (StatPR), 2) Structural or syntactic pattern recognition (SyntPR) and 3) Neural-based pattern recognition (NeurPR). The details of each category are as follows: International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 9 (2016) pp 6800-6807 © Research India Publications. http://www.ripublication.com 6801 Statistical pattern recognition (StatPR) This approach has a statistical basis for classification of algorithms. The classifier design attempted to use statistical and a priori probabilities to build the recognition algorithms. The input features were extracted from a set of pattern characteristic measurements. Patil and Yadahalli [16] applied minimum distance and knearest neighbor for classifying 21 classes of different food grains by using color and texture features without performing preprocessing and segmentation. The precision rate was 83.66%. Pandey et al. [15] used content based image retrieval (CBIR) technique for identifying different seeds (wheat, rice and gram). The CBIR technique was used to identify and recognize the images based on color, shape, texture and size features. Finally, Euclidean Distance (ED) and artificial neural network techniques were applied in order to do seed identification. The training data set contained 150 images and the testing data set contained 50 images. The precision rates were 84.4% for ED and 95% for artificial neural network. Kaur and Singh [10] applied Multi-Class SVM to grade the rice kernels (Premium, Grade A, Grade B and Grade C) for different varieties of rice grains based on interior and exterior quality. Maximum variance method was used to extract the rice kernels from background and extract chalk from rice. Ten geometric features were used to determine the percentage of head rice, broken rice and brewers of the rice samples. The precision rate was more than 86%. Structural or syntactic pattern recognition (SyntPR) This approach was a structural entity used for classification and descriptions. The classification was based on measurements of structural pattern similarity. The structural or syntactic information was used to generate knowledge related to patterns by extracting the similarity of patterns in order to build pattern syntax or structural rules. The information of pattern syntax rules were used to explain, classify and recognize unknown patterns. This approach was classified into two subcategories which were Rule Based Expert Systems and Fuzzy Expert Systems. Punthimast et al. [34] applied a rule based system to identify rice seeds and stick rice seeds based on RGB color histogram. The precision rate was 96.34% for “Jasmine” rice seeds and 100% for stick rice seeds. Maheshwari and Jain [26] proposed a method for counting the number of Oryza sativa L (rice seeds) with long seeds as well as small seeds by comparing the degree of quality and quantifying these degrees for the rice seeds based on combined measurements. They applied a rule based system to construct the rules based on histogram of area, major axis, minor axis and eccentricity of the seeds in order to classify the rice seeds into normal, long and small seeds. Finally, they compared the results with the ground truth table based on Human Sensory Panel of various sample. Ajay et al. [1] proposed an algorithm of evaluating the automatic evaluation method for the determination of the quality of milled rice seed based on shape and geometric features. They applied rule based system of the length of the rice kernel to classify the broken rice and non-broken rice by using morphological features applied with the minimum bounding rectangle method. It was proved that this method was efficient. Moreover, this method was able to improve the traditional one. Sansomboonsuk and Afzulpurkar [20] developed a computer system for evaluating the quality of rice kernels. The system applied fuzzy logic to organize and classify the class of each kernel into broken rice and long grain rice by using the point and line touching features (area, perimeter, circularity and shape compactness). A rule based system was applied to generate the fuzzy set of rules in order to classify each rice kernel. The precision rate was 90% for evaluating the quality of rice compared with human inspection methods. Neural-based pattern recognition (NeurPR) This approach was a nonalgorithmic or black box strategy which was trainable. This approach also fed input features into neural network nodes to identify patterns. The NeurPR emulated knowledge of how biological neural system stored and manipulated information. The artificial neural system called “neural networks” could solve the problems of automatic reasoning including the pattern recognition problem. This approach classified patterns by predicting the properties of neural network. Guzman and Peralta [8] applied artificial neural networks to classify the grain samples of Philippine rice grains based on size, shape and 52 varieties of rice grains belonging to 5 varietal groups in the Philippines by using image processing techniques. The system used 3 data sets containing 110 rice grains for each size, shape and variety of rice grain identification. They achieved a precision rate of 98.76% for sizes and 96.67% for shape. The precision rate based on individual rice varietal types were 85.81, 94.58, 96.16 and 97.39% for the lowland irrigated, lowland