Expert system for diagnosing diseases in corn plants using the navies bayes method
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Published:
May 30, 2024
Keywords:
Agriculture, Corn Plants, Disease Diagnosis, Expert System, Naïve Bayes Algorithm
Section
Computer Science
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Abstract
This research introduces an expert system using the Naive Bayes method to diagnose corn plant diseases, aiming to provide an automated, accurate, and scalable diagnostic tool. Traditional methods are often inefficient and error-prone, relying on expert knowledge and manual inspection. This study employs a quantitative approach, incorporating experimental design, data analysis, and model validation. Data on humidity, temperature, and soil conditions were collected from agricultural research centers and online databases. After preprocessing, key variables influencing disease occurrence were selected. The Naive Bayes model was optimized using cross-validation and implemented in Python, achieving an average accuracy of 92%. The model's performance, evaluated through accuracy, precision, recall, and F1-score, demonstrated the effective distinction between similar symptoms—the system's simplicity and computational efficiency suit resource-constrained environments like rural farms. By combining visual symptoms and environmental factors, the system minimizes dependency on expert knowledge, offering a comprehensive and scalable solution for disease management in agriculture
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How to Cite
Gunawan, G., Sya’bani, A. Z. . and Anandianshka, S. . (2024) “Expert system for diagnosing diseases in corn plants using the navies bayes method”, Jurnal Mantik, 8(1), pp. 849-859. doi: 10.35335/mantik.v8i1.5265.
References
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Unal, Z. (2020). Smart Farming Becomes even Smarter with Deep Learning - A Bibliographical Analysis. IEEE Access, 8, 105587–105609. https://doi.org/10.1109/ACCESS.2020.3000175
Valle-Torres, J., Ross, T. J., Plewa, D., Avellaneda, M. C., Check, J., Chilvers, M. I., Cruz, A. P., Dalla Lana, F., Groves, C., Gongora-Canul, C., Henriquez-Dole, L., Jamann, T., Kleczewski, N., Lipps, S., Malvick, D., McCoy, A. G., Mueller, D. S., Paul, P. A., Puerto, C., … Cruz, C. D. (2020). Tar spot: An understudied disease threatening corn production in the Americas. Plant Disease, 104(10), 2541–2550. https://doi.org/10.1094/PDIS-02-20-0449-FE
Whirl-Carrillo, M., Huddart, R., Gong, L., Sangkuhl, K., Thorn, C. F., Whaley, R., & Klein, T. E. (2021). An Evidence-Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine. Clinical Pharmacology and Therapeutics, 110(3), 563–572. https://doi.org/10.1002/cpt.2350
Yu, H., Liu, J., Chen, C., Heidari, A. A., Zhang, Q., Chen, H., Mafarja, M., & Turabieh, H. (2021). Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning. IEEE Access, 9, 143824–143835. https://doi.org/10.1109/ACCESS.2021.3120379
Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., & Zhao, C. (2020). A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. In Remote Sensing (Vol. 12, Issue 19, pp. 1–34). MDPI AG. https://doi.org/10.3390/rs12193188
Antwi-Agyei, P., Dougill, A. J., & Abaidoo, R. C. (2021). Opportunities and barriers for using climate information for building resilient agricultural systems in Sudan savannah agro-ecological zone of north-eastern Ghana. Climate Services, 22. https://doi.org/10.1016/j.cliser.2021.100226
Balafas, V., Karantoumanis, E., Louta, M., & Ploskas, N. (2023). Machine Learning and Deep Learning for Plant Disease Classification and Detection. In IEEE Access (Vol. 11, pp. 114352–114377). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2023.3324722
Bazargani, K., & Deemyad, T. (2024). Automation’s Impact on Agriculture: Opportunities, Challenges, and Economic Effects. In Robotics (Vol. 13, Issue 2). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/robotics13020033
Bhargava, A., Shukla, A., Goswami, O. P., Alsharif, M. H., Uthansakul, P., & Uthansakul, M. (2024). Plant Leaf Disease Detection, Classification, and Diagnosis Using Computer Vision and Artificial Intelligence: A Review. IEEE Access, 12, 37443–37469. https://doi.org/10.1109/ACCESS.2024.3373001
Bhat, S. A., & Huang, N. F. (2021). Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. IEEE Access, 9, 110209–110222. https://doi.org/10.1109/ACCESS.2021.3102227
Bilal, M., Ali, G., Iqbal, M. W., Anwar, M., Malik, M. S. A., & Kadir, R. A. (2022). Auto-Prep: Efficient and Automated Data Preprocessing Pipeline. IEEE Access, 10, 107764–107784. https://doi.org/10.1109/ACCESS.2022.3198662
Blanquero, R., Carrizosa, E., Ramírez-Cobo, P., & Sillero-Denamiel, M. R. (2021). Variable selection for Naïve Bayes classification. Computers and Operations Research, 135. https://doi.org/10.1016/j.cor.2021.105456
Chen, H., Yu, P., Hailey, D., & Cui, T. (2020). Identification of the essential components of quality in the data collection process for public health information systems. Health Informatics Journal, 26(1), 664–682. https://doi.org/10.1177/1460458219848622
Deepa, N., Khan, M. Z., Prabadevi, B., Vincent, D. R. P. M., Maddikunta, P. K. R., & Gadekallu, T. R. (2020). Multiclass model for agriculture development using multivariate statistical method. IEEE Access, 8, 183749–183758. https://doi.org/10.1109/ACCESS.2020.3028595
Fitriyani, N. L., Syafrudin, M., Alfian, G., & Rhee, J. (2020). HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System. IEEE Access, 8, 133034–133050. https://doi.org/10.1109/ACCESS.2020.3010511
Haider, W., Rehman, A. U., Durrani, N. M., & Rehman, S. U. (2021). A Generic Approach for Wheat Disease Classification and Verification Using Expert Opinion for Knowledge-Based Decisions. IEEE Access, 9, 31104–31129. https://doi.org/10.1109/ACCESS.2021.3058582
Lv, M., Zhou, G., He, M., Chen, A., Zhang, W., & Hu, Y. (2020). Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet. IEEE Access, 8, 57952–57966. https://doi.org/10.1109/ACCESS.2020.2982443
Maurya, R., Mahapatra, S., & Rajput, L. (2024). A Lightweight Meta-Ensemble Approach for Plant Disease Detection Suitable for IoT-Based Environments. IEEE Access, 12, 28096–28108. https://doi.org/10.1109/ACCESS.2024.3367443
Mesías-Ruiz, G. A., Pérez-Ortiz, M., Dorado, J., de Castro, A. I., & Peña, J. M. (2023). Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. In Frontiers in Plant Science (Vol. 14). Frontiers Media S.A. https://doi.org/10.3389/fpls.2023.1143326
Nelson, R. (2020). International plant pathology: Past and future contributions to global food security. In Phytopathology (Vol. 110, Issue 2, pp. 245–253). American Phytopathological Society. https://doi.org/10.1094/PHYTO-08-19-0300-IA
Savary, S., & Willocquet, L. (2020). Modeling the Impact of Crop Diseases on Global Food Security. Annual Review of Phytopathology. https://doi.org/10.1146/annurev-phyto-010820
Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine Learning Applications for Precision Agriculture: A Comprehensive Review. In IEEE Access (Vol. 9, pp. 4843–4873). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2020.3048415
Shrivastava, G., & Patidar, H. (2022). RICE PLANT DISEASE IDENTIFICATION DECISION SUPPORT MODEL USING MACHINE LEARNING. ICTACT JOURNAL ON SOFT COMPUTING, 3. https://doi.org/10.21917/ijsc.2022.0365
Tholl, D., Hossain, O., Weinhold, A., Röse, U. S. R., & Wei, Q. (2021). Trends and applications in plant volatile sampling and analysis. In Plant Journal (Vol. 106, Issue 2, pp. 314–325). Blackwell Publishing Ltd. https://doi.org/10.1111/tpj.15176
Unal, Z. (2020). Smart Farming Becomes even Smarter with Deep Learning - A Bibliographical Analysis. IEEE Access, 8, 105587–105609. https://doi.org/10.1109/ACCESS.2020.3000175
Valle-Torres, J., Ross, T. J., Plewa, D., Avellaneda, M. C., Check, J., Chilvers, M. I., Cruz, A. P., Dalla Lana, F., Groves, C., Gongora-Canul, C., Henriquez-Dole, L., Jamann, T., Kleczewski, N., Lipps, S., Malvick, D., McCoy, A. G., Mueller, D. S., Paul, P. A., Puerto, C., … Cruz, C. D. (2020). Tar spot: An understudied disease threatening corn production in the Americas. Plant Disease, 104(10), 2541–2550. https://doi.org/10.1094/PDIS-02-20-0449-FE
Whirl-Carrillo, M., Huddart, R., Gong, L., Sangkuhl, K., Thorn, C. F., Whaley, R., & Klein, T. E. (2021). An Evidence-Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine. Clinical Pharmacology and Therapeutics, 110(3), 563–572. https://doi.org/10.1002/cpt.2350
Yu, H., Liu, J., Chen, C., Heidari, A. A., Zhang, Q., Chen, H., Mafarja, M., & Turabieh, H. (2021). Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning. IEEE Access, 9, 143824–143835. https://doi.org/10.1109/ACCESS.2021.3120379
Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., & Zhao, C. (2020). A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. In Remote Sensing (Vol. 12, Issue 19, pp. 1–34). MDPI AG. https://doi.org/10.3390/rs12193188
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