Optimization of Gray Level Co-occurrence Matrix (GLCM) Texture Feature Parameters in Determining Rice Seed Quality
Abstract
Rice seed quality assessment is a critical measure in promoting agricultural productivity, as high-quality seeds directly influence crop yield and resilience. One of method for evaluating seed quality is texture analysis, which leverages the Gray Level Co-occurrence Matrix (GLCM) to extract meaningful features from seed images, providing insights into their condition and potential performance. This research aims to determine the optimal performance of GLCM parameters in identifying the texture characteristics of rice seed quality. The experiments were conducted using four angles (0°, 45°, 90°, and 135°) and three-pixel distances (1, 2, and 3), evaluating features such as homogeneity, contrast, dissimilarity, and energy. The results indicate that certain parameter configurations significantly affect the discriminative power of the extracted features, with the Support Vector Machine (SVM) classifier achieving the highest performance at a pixel distance of 1, with an accuracy of 0.73, precision of 0.79, recall of 0.73, and F1-score of 0.72. These findings demonstrate that optimizing GLCM parameter settings directly contributes to improved classification performance, highlighting the method's potential for enhancing rice seed quality assessment.
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S. S. Hidayat, D. Rahmawati, M. C. Ardi Prabowo, L. Triyono, and F. T. Putri, Determining the Rice Seeds Quality Using Convolutional Neural Network, Int. J. Informatics Vis., Vol. 7, No. 2, 2023.
S. K. P et al., Development of coreset of aromatic rice (Oryza sativa L. Indica) based on molecular and morphological diversity, Genet. Resour. Crop Evol., Vol. 68, No. 2, 2021.
V. Santhy, N. Sandra, K. V. Ravishankar, and B. Chidambara, Molecular Techniques for Testing Genetic Purity and Seed Health, Seed Science and Technology: Biology, Production, Quality, 2023.
S. Sundareswaran, P. R. Choudhury, C. Vanitha, and D. K. Yadava, Seed Quality: Variety Development to Planting—An Overview, Seed Science and Technology: Biology, Production, Quality, 2023.
S. Ogawa, K. Yamamoto, K. Uno, N. C. Thuan, T. Togami, and S. Shindo, Optimal Water Level Management for Mitigating GHG Emissions through Water-Conserving Irrigation in An Giang Province, Vietnam, Sensors, Vol. 22, No. 21, 2022.
T. Alahmad, M. Neményi, and A. Nyéki, Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review, Agronomy, Vol. 13, No. 10, 2023.
S. Mogilicharla and U. K. Mummadi, Grain Quality Analysis from the Image Through the Approaches of Segmentation, AIP Conference Proceedings, 2024.
S. Aouat, I. Ait-hammi, and I. Hamouchene, A new approach for texture segmentation based on the Gray Level Co-occurrence Matrix, Multimed. Tools Appl., Vol. 80, No. 16, 2021.
M. T. Habib, A. Majumder, A. Z. M. Jakaria, M. Akter, M. S. Uddin, and F. Ahmed, Machine vision based papaya disease recognition, J. King Saud Univ. - Comput. Inf. Sci., Vol. 32, No. 3, 2020.
A. Setiawan, K. Adi, C. E. Widodo, Rice Variety Identification Using Edge Feature Extraction and Texture-Based Segmentation, 2023 International Seminar on Application for Technology of Information and Communication: Smart Technology Based on Industry 4.0: A New Way of Recovery from Global Pandemic and Global Economic Crisis, iSemantic 2023, 2023.
W. Xie, Q. Chai, Y. Gan, S. Chen, X. Zhang, and W. Wang, Strains classification of Anoectochilus roxburghii using multi-feature extraction and Stacking ensemble learning, Nongye Gongcheng Xuebao/Transactions Chinese Soc. Agric. Eng., Vol. 36, No. 14, 2020.
K. S. Subramanian, S. Vairachilai, and T. Gebremichael, Features extraction and dataset preparation for grading of ethiopian coffee beans using image analysis techniques, Smart Innovation, Systems and Technologies, 2019.
M. Sobhana, P. Dabbara, G. Ravulapalli, and K. S. Kakunuri, Seed Quality Prediction using Computer Vision and Convolutional Neural Networks, 4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022 - Proceedings, 2022.
K. W. V. Geollegue, E. R. Arboleda, and A. A. Dizon, Seed of rice plant classification using coarse tree classifier, IAES Int. J. Artif. Intell., Vol. 11, No. 2, pp. 727-735, 2022.
S. Harini, S. Chakrasali, and G. N. Krishnamurthy, Analysis of Indian Rice Quality Using Multi-class Support Vector Machine, Lecture Notes in Electrical Engineering, 2023.
N. Ansari, S. S. Ratri, A. Jahan, M. Ashik-E-Rabbani, and A. Rahman, Inspection of paddy seed varietal purity using machine vision and multivariate analysis, J. Agric. Food Res., Vol. 3, 2021.
M. Mehdizadeh, K. Tavakoli Tafti, and P. Soltani, Evaluation of histogram equalization and contrast limited adaptive histogram equalization effect on image quality and fractal dimensions of digital periapical radiographs, Oral Radiol., Vol. 39, No. 2, 2023.
R. Sharma and A. Kamra, A Review on CLAHE Based Enhancement Techniques, Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, 2023.
M. Aykut and S. M. Akturk, An Improvement on GrabCut with CLAHE for the Segmentation of the Objects with Ambiguous Boundaries, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018.
M. Abdu, M. M. Mokji, and U. U. Sheikh, Machine learning for plant disease detection: An investigative comparison between support vector machine and deep learning, IAES Int. J. Artif. Intell., Vol. 9, No. 4, pp. 670-683, 2020.
S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, Comparative analysis of deep learning image detection algorithms, J. Big Data, Vol. 8, No. 1, 2021.
M. Yalsavar, P. Karimaghaee, A. Sheikh-Akbari, M. H. Khooban, J. Dehmeshki, and S. Al-Majeed, Kernel Parameter Optimization for Support Vector Machine Based on Sliding Mode Control, IEEE Access, Vol. 10, 2022.
K. Singh, S. V. N. Sreenivasu, U. S. B. K. Mahalaxmi, H. Sharma, D. D. Patil, and E. Asenso, Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier, Journal of Food Quality, 2022.
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