Rice seed image classification based on HOG descriptor with missing values imputation

Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach

PhosopNet: An improved grain localization and classification by image augmentation

Rice is a staple food for around 3.5 billion people in eastern, southern and south-east Asia. Prior to being rice, the rice-grain (grain) is previously husked and/or milled by the milling machine. Relevantly, the grain quality depends on its pureness of particular grain specie (without the mixing between different grain species). For the demand of grain purity inspection by an image, many researchers have proposed the grain classification (sometimes with localization) methods based on convolutional neural network (CNN). However, those papers are necessary to have a large number of labeling that was too expensive to be manually collected. In this paper, the image augmentation (rotation, brightness adjustment and horizontal flipping) is appiled to generate more number of grain images from the less data. From the results, image augmentation improves the performance in CNN and bag-of-words model. For the future moving forward, the grain recognition can be easily done by less number of images

Varietal classification of rice seeds using RGB and hyperspectral images.

Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identify their species and assess the cleanness of the batch. In the quest to automate the screening process through machine vision, a variety of approaches utilise appearance-based features extracted from RGB images while others utilise the spectral information acquired using Hyperspectral Imaging (HSI) systems. Most of the literature on this topic benchmarks the performance of new discrimination models using only a small number of species. Hence, it is unclear whether or not model performance variance confirms the effectiveness of proposed algorithms and features, or if it can be simply attributed to the inter-class/intra-class variations of the dataset itself. In this paper, a novel method to automatically screen and classify rice seed samples is proposed using a combination of spatial and spectral features, extracted from high resolution RGB and hyperspectral images. The proposed system is evaluated using a large dataset of 8,640 rice seeds sampled from a variety of 90 different species. The dataset is made publicly available to facilitate robust comparison and benchmarking of other existing and newly proposed techniques going forward. The proposed algorithm is evaluated on this large dataset and the experimental results show the effectiveness of the algorithm to eliminate impure species by combining spatial features extracted from high spatial resolution images and spectral features from hyperspectral data cubes

Digital Image Analysis Using Flatbed Scanning System For Purity Testing Of Rice Seed And Confirmation By Grow Out Test

The common method used for purity testing of rice seed is human visual observation. This method, however, has a high degree of subjectivity when dealing with different rice varieties which have similar morphology. Digital image analysis with flatbed scanning for purity testing of rice seed was proposed by investigating the morphology of rice seeds and confirmation by grow out test (GOT) in the field. Two extra-long seed varieties were used in this study including a red rice Aek Sibundong and an aromatic rice Sintanur. The identification on 14 parameters of morphological characteristics indicated that only six parameters were correlated, i.e. area, feret, minimum feret, aspect ratio, round, and solidity. The purity of rice seed can be effectively determined using digital image analysis of spikelet color and shape. Based on the discriminant analysis of the digital image the recognition rate of rice seed purity was higher than 99.2% for shape and 93.55% for color. The method, therefore, has a potential to be used as a complement in rice seed purity testing to increase the accuracy of human visual method and it is more sensitive than GOT

A combined method of image processing and artificial neural network for the identification of 13 Iranian rice cultivars

Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars

DIGITAL IMAGE ANALYSIS USING FLATBED SCANNING SYSTEM FOR PURITY TESTING OF RICE SEED AND CONFIRMATION BY GROW OUT TEST

The common method used for purity testing of rice seed is human visual observation. This method, however, has a high degree of subjectivity when dealing with different rice varieties which have similar morphology. Digital image analysis with flatbed scanning for purity testing of rice seed was proposed by investigating the morphology of rice seeds and confirmation by grow out test (GOT) in the field. Two extra-long seed varieties were used in this study including a red rice Aek Sibundong and an aromatic rice Sintanur. The identification on 14 parameters of morphological characteristics indicated that only six parameters were correlated, i.e. area, feret, minimum feret, aspect ratio, round, and solidity. The purity of rice seed can be effectively determined using digital image analysis of spikelet color and shape. Based on the discriminant analysis of the digital image the recognition rate of rice seed purity was higher than 99.2% for shape and 93.55% for color. The method, therefore, has a potential to be used as a complement in rice seed purity testing to increase the accuracy of human visual method and it is more sensitive than GOT

Sistem Deteksi Cepat Mutu Organoleptik Beras Berbasis Android

Penelitian ini bertujuan untuk mengembangkan alat deteksi cepat mutu organoleptik beras berbasis pada pemanfaatan aplikasi Android agar pengujian mutu organoleptik beras dapat dilakukan secara cepat dan akurat. Bahan penelitian yang digunakan adalah beras varietas Ciherang dan Tarabas. Metode yang digunakan adalah dengan menggunakan realtime image processing berbasis Android dan Java. Hasil penelitian menunjukkan bahwa lamanya penyimpanan beras sangat mempengaruhi citra beras (Red Green Blue/RGB). Selama penyimpanan beras, nilai Blue menghasilkan nilai perubahan yang nyata dibandingkan nilai Red dan Green. Nilai Blue ini berkorelasi positif terhadap perubahan kadar amilosa selama penyimpanan dan mutu organoleptiknya. Aplikasi deteksi cepat mutu organoleptik beras juga telah berhasil dibuat dan dapat diuji validitasnya dengan memperhatikan perubahan karakateristik citra, perubahan amilosa, dan mutu organoleptiknya. Kesimpulannya, aplikasi deteksi cepat ini berhasil dikembangkan dengan berbasis Android yang dapat digunakan sebagai alat uji mutu organoleptik berasRapid Detection System for Organoleptic Quality of Rice using the Android ApplicationAbstractThe research was aimed at developing rapid detection tool of rice upon organoleptic quality based on the Android application, so the testing may be done quickly and accurately. Ciherang and Tarabas rice varieties were used in this research. Realtime image processing based on Android and Java were used as method in this research. The results showed that the storage affected the rice image value (Red Green Blue/RGB). During storage, the value of the blue (B) produced a proper marked which was positively correlated to the changes in amylose content. Application of rapid detection of organoleptic quality of rice was carried out by observing changes in image characteristics, changes in amylose, and changes in organoleptic properties. As conclusion, the application may functioning properly and can be used as a tool to test the organoleptic quality of rice and its shelf life

Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug

The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops, compromising agri-food production. Field monitoring procedures are fundamental to perform risk assessment operations, in order to promptly face crop infestations and avoid economical losses. To improve pest management, spectral cameras mounted on Unmanned Aerial Vehicles (UAVs) and other Internet of Things (IoT) devices, such as smart traps or unmanned ground vehicles, could be used as an innovative technology allowing fast, efficient and real-time monitoring of insect infestations. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens on different vegetal backgrounds, overcoming the problem of BMSB mimicry. Hyperspectral images of BMSB were acquired in the 980-1660 nm range, considering different vegetal backgrounds selected to mimic a real field application scene. Classification models were obtained following two different chemometric approaches. The first approach was focused on modelling spectral information and selecting relevant spectral regions for discrimination by means of sparse-based variable selection coupled with Soft Partial Least Squares Discriminant Analysis (s-Soft PLS-DA) classification algorithm. The second approach was based on modelling spatial and spectral features contained in the hyperspectral images using Convolutional Neural Networks (CNN). Finally, to further improve BMSB detection ability, the two strategies were merged, considering only the spectral regions selected by s-Soft PLS-DA for CNN modelling.Comment: Accepted manuscrip

Detection of Fusarium Head Blight in Wheat Grains Using Hyperspectral and RGB Imaging

In modern agriculture, it is imperative to ensure that crops are healthy and safe for consumption. Fusarium Head Blight (FHB) can cause significant damage to wheat grains by reducing essential components such as moisture, protein, and starch, while also introducing dangerous toxins. Therefore, accurately distinguishing between healthy and FHB-infected wheat grains is essential to guarantee stable and reliable wheat production while limiting financial losses and ensuring food safety. This thesis proposes effective methods to classify healthy and FHB infected wheat grains using Hyperspectral Imaging (HSI) and Red Green Blue (RGB) images. The approach includes a combination of Principal Component Analysis (PCA) with morphology, in addition to dark and white reference correction, to create masks for grains in each image. The classification for the hyperspectral images was achieved using a Partial Least Squares Discriminant Analysis (PLS-DA) model for hyperspectral images and a Convolutional Neural Network (CNN) model for RGB images. Both object-based and pixel-based approaches were compared for the PLS-DA model. The results indicated that the object-based approach outperformed the pixel-based approach and other well-known machine learning algorithms, including Random Forest (RF), linear Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) calibrated one-vs-all and DecisionTree. The PLS-DA model using the object-based method yielded better results when tested on all wheat varieties, achieving an F1-score of 99.4%. Specific wavelengths were investigated based on a loading plot, and four effective wavelengths were identified, 953 nm, 1373 nm, 1923 nm and 2493 nm, with classification accuracy found to be similar to the full spectral range. Moreover, the moisture and water content in the grains were analyzed using hyperspectral images through an aquagram, which demonstrated that healthy grains exhibited higher absorbance values than infected grains for all Water Matrix Coordinates (WAMACS). Furthermore, the CNN model was trained on cropped individual grains, and the classification accuracy was similar to the PLS-DA model, with an F1- score of 98.1%. These findings suggest that HSI is suitable for identifying FHB-infected wheat grains, while RGB images may provide a cost-effective alternative to hyperspectral images for this specific classification task. Further research should consider to explore the potential benefits of HSI for deeper investigations into how water absorption affects spectral measurements and moisture content in grains, in addition to user-friendly interfaces for deep learning based image classification