Development of an expert vision-based system for inspecting rice quality indices

Payman, Sayed Hossein; Bakhshipour, Adel; Zareiforoush, Hemad

doi:10.3920/qas2017.1109

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…Since the RGB images of plants contained field soil and little residues in the background, it was necessary to separate the plants from the image background before performing the feature extraction processes. The Red (R), Green (G), and Blue (B) colour components were firstly extracted from the RGB image and the luminance component (Y) was calculated using equation 1 [48,49], which was used to calculate the green colour difference image (Cg) by equation 2 [50,51].…”

Section: B Image Preparationmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

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Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M1 and LogitBoost algorithms was evaluated to enhance the plant classification performance of four classifiers, namely Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Random Forest (RF), and Support Vector Machine (SVM). Four feature filtering techniques including Correlationbased Feature Selection (CFS), Information Gain (IG), Gain Ratio (GR), and OneR were applied to the imageextracted features and 10 of the most significant features were selected and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M1 and LogitBoost algorithms improved the classification accuracy. Regarding the performance values, the LogitBoost-IG-RF structure, which provided a classification accuracy of 99.58%, a kappa (k) of 0.9948, and a Root Mean Squared Error (RMSE) of 0.0688 on training dataset, was selected as the most appropriate classifier for plant discrimination in peanut fields. The accuracy, k, and RMSE criteria of this combination on test dataset were 95.00%, 0.9375, and 0.1591, respectively. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

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Section: B Image Preparationmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

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“…In addition to the K-means clustering approach, threshold based approaches have been used for chalkiness identification and quantification. For example, a multi-threshold approach based on maximum entropy was used for chalky area calculation [ 35 ] and another threshold-based approach was used to detect broken, chalky and spotted rice grains [ 36 ]. However, such approaches need extensive fine-tuning to identify the right thresholds and are not easily transferable to seeds of different types or to images taken under different conditions.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

et al. 2022

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Background Rice is a major staple food crop for more than half the world’s population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed. Results We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area. Conclusions We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.

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“…Payman et al (2018) developed an expert system to detect the size, breakage and crack of rice, achieving an accuracy of 96%. In this system, a scanner was used to capture the images of rice and a traditional image processing method was used to extract the features from the images [ 11 ]. Chen et al (2019) used computer vision combined with a support vector machine model to evaluate the breakage and chalkiness of red indica rice, and the accuracy was more than 93% [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model

Sun

Zhang

Tong

et al. 2022

Foods

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This study aims to develop a high-speed and nondestructive mildewed rice grain detection method. First, a set of microscopic images of rice grains contaminated by Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea are acquired to serve as samples, and the mildewed regions are marked. Then, three YOLO-v5 models for identifying regions of rice grain with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in microscopic images are established. Finally, the relationship between the proportion of mildewed regions and the total number of colonies is analyzed. The results show that the proposed YOLO-v5 models achieve accuracy levels of 89.26%, 91.15%, and 90.19% when detecting mildewed regions with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in the microscopic images of the verification set. The proportion of the mildewed region area of rice grain with contamination of Aspergillus niger/Penicillium citrinum/Aspergillus cinerea is logarithmically correlated with the logarithm of the total number of colonies (TVC). The corresponding determination coefficients are 0.7466, 0.7587, and 0.8148, respectively. This study provides a reference for future research on high-speed mildewed rice grain detection methods based on MCV technology.

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Development of an expert vision-based system for inspecting rice quality indices

Cited by 13 publications

References 27 publications

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model

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