Application of computer vision techniques to detect diseases and pests of chili plants
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Published:
Mar 30, 2024
Keywords:
Computer Vision, Convolutional Neural Networks, Farm Efficiency, Pest Classification, Plant Disease Detection
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Abstract
This research aims to develop a disease and pest detection system in chili plants using computer vision techniques. In this study, deep learning methods, especially Convolutional Neural Networks (CNN), were applied to identify and classify various types of diseases and pests that often attack chili plants. The data used included images of chili leaves infected with various diseases and pests, which were then trained in CNN models to recognize certain patterns that indicate the presence of infection. The results showed that the developed system was able to detect and classify diseases and pests in chili plants with a very high degree of accuracy. The novelty of this research lies in the use of computer vision techniques combined with sophisticated deep learning algorithms to automatically detect diseases and pests, which were previously done manually by farmers or agricultural experts. These findings make an important contribution to improving efficiency and effectiveness in chili crop health management, offering innovative solutions to support agricultural sustainability through the use of advanced technology.
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How to Cite
Nurokhman, A. ., Surorejo, S. ., Kurniawan, R. D. ., & Gunawan, G. . (2024). Application of computer vision techniques to detect diseases and pests of chili plants. Journal of Intelligent Decision Support System (IDSS), 7(1), 10-18. https://doi.org/10.35335/idss.v7i1.201
References
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Ahmed, L., Iqbal, M. M., Aldabbas, H., Khalid, S., Saleem, Y., & Saeed, S. (2023). Images data practices for semantic segmentation of breast cancer using deep neural network. Journal of Ambient Intelligence and Humanized Computing, 14(11), 15227–15243.
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Sood, S., & Singh, H. (2021). Computer vision and machine learning based approaches for food security: A review. Multimedia Tools and Applications, 80(18), 27973–27999.
Wani, J. A., Sharma, S., Muzamil, M., Ahmed, S., Sharma, S., & Singh, S. (2022). Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: Methodologies, applications, and challenges. Archives of Computational Methods in Engineering, 29(1), 641–677.
Ye, Z., Guo, S., Chen, D., Wang, H., & Li, S. (2021). Drilling formation perception by supervised learning: Model evaluation and parameter analysis. Journal of Natural Gas Science and Engineering, 90, 103923.
Zamljen, T., Zupanc, V., & Slatnar, A. (2020). Influence of irrigation on yield and primary and secondary metabolites in two chilies species, Capsicum annuum L. and Capsicum chinense Jacq. Agricultural Water Management, 234, 106104.
Abdullah, H. M., Mohana, N. T., Khan, B. M., Ahmed, S. M., Hossain, M., Islam, K. H. S., Redoy, M. H., Ferdush, J., Bhuiyan, M., & Hossain, M. M. (2023). Present and future scopes and challenges of plant pest and disease (P&D) monitoring: Remote sensing, image processing, and artificial intelligence perspectives. Remote Sensing Applications: Society and Environment, 100996.
Agussabti, A., Romano, R., Rahmaddiansyah, R., & Isa, R. M. (2020). Factors affecting risk tolerance among small-scale seasonal commodity farmers and strategies for its improvement. Heliyon, 6(12).
Ahmad Loti, N. N., Mohd Noor, M. R., & Chang, S. (2021). Integrated analysis of machine learning and deep learning in chili pest and disease identification. Journal of the Science of Food and Agriculture, 101(9), 3582–3594.
Ahmed, L., Iqbal, M. M., Aldabbas, H., Khalid, S., Saleem, Y., & Saeed, S. (2023). Images data practices for semantic segmentation of breast cancer using deep neural network. Journal of Ambient Intelligence and Humanized Computing, 14(11), 15227–15243.
Ali, M. M., Paul, B. K., Ahmed, K., Bui, F. M., Quinn, J. M. W., & Moni, M. A. (2021). Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine, 136, 104672.
Allari, R. S., Atout, M., & Hasan, A. A. (2020). The value of caring behavior and its impact on students’ self‐efficacy: Perceptions of undergraduate nursing students. Nursing Forum, 55(2), 259–266.
Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P. (2023). A review of deep learning techniques used in agriculture. Ecological Informatics, 102217.
Boulila, W., Alzahem, A., Koubaa, A., Benjdira, B., & Ammar, A. (2023). Early detection of red palm weevil infestations using deep learning classification of acoustic signals. Computers and Electronics in Agriculture, 212, 108154.
De Costa, D. M., De Costa, J. M., Weerathunga, M. T., Prasannath, K., & Bulathsinhalage, V. N. D. (2021). Assessment of management practices, awareness on safe use of pesticides and perception on integrated management of pests and diseases of chilli and tomato grown by small‐scale farmers in selected districts of Sri Lanka. Pest Management Science, 77(11), 5001–5020.
Garg, G., Gupta, S., Mishra, P., Vidyarthi, A., Singh, A., & Ali, A. (2021). CROPCARE: an intelligent real-time sustainable IoT system for crop disease detection using mobile vision. IEEE Internet of Things Journal, 10(4), 2840–2851.
Kaya, Y., & Gürsoy, E. (2023). A novel multi-head CNN design to identify plant diseases using the fusion of RGB images. Ecological Informatics, 75, 101998.
Khanramaki, M., Asli-Ardeh, E. A., & Kozegar, E. (2021). Citrus pests classification using an ensemble of deep learning models. Computers and Electronics in Agriculture, 186, 106192.
Li, Z., Guo, R., Li, M., Chen, Y., & Li, G. (2020). A review of computer vision technologies for plant phenotyping. Computers and Electronics in Agriculture, 176, 105672.
Mehmood, N., Saeed, M., Zafarullah, S., Hyder, S., Rizvi, Z. F., Gondal, A. S., Jamil, N., Iqbal, R., Ali, B., & Ercisli, S. (2023). Multifaceted impacts of plant-beneficial pseudomonas spp. in managing various plant diseases and crop yield improvement. ACS Omega, 8(25), 22296–22315.
Muflikh, Y. N., Smith, C., Brown, C., & Aziz, A. A. (2021). Analysing price volatility in agricultural value chains using systems thinking: A case study of the Indonesian chilli value chain. Agricultural Systems, 192, 103179.
Naik, B. N., Malmathanraj, R., & Palanisamy, P. (2022). Detection and classification of chilli leaf disease using a squeeze-and-excitation-based CNN model. Ecological Informatics, 69, 101663.
Ni, F., Zhang, J., & Noori, M. N. (2020). Deep learning for data anomaly detection and data compression of a long‐span suspension bridge. Computer‐Aided Civil and Infrastructure Engineering, 35(7), 685–700.
Ramzan, F., Khan, M. U. G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A., & Mehmood, Z. (2020). A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks. Journal of Medical Systems, 44, 1–16.
Shi, Z., Dang, H., Liu, Z., & Zhou, X. (2020). Detection and identification of stored-grain insects using deep learning: A more effective neural network. IEEE Access, 8, 163703–163714.
Sinha, B. B., & Dhanalakshmi, R. (2022). Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Generation Computer Systems, 126, 169–184.
Sood, S., & Singh, H. (2021). Computer vision and machine learning based approaches for food security: A review. Multimedia Tools and Applications, 80(18), 27973–27999.
Wani, J. A., Sharma, S., Muzamil, M., Ahmed, S., Sharma, S., & Singh, S. (2022). Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: Methodologies, applications, and challenges. Archives of Computational Methods in Engineering, 29(1), 641–677.
Ye, Z., Guo, S., Chen, D., Wang, H., & Li, S. (2021). Drilling formation perception by supervised learning: Model evaluation and parameter analysis. Journal of Natural Gas Science and Engineering, 90, 103923.
Zamljen, T., Zupanc, V., & Slatnar, A. (2020). Influence of irrigation on yield and primary and secondary metabolites in two chilies species, Capsicum annuum L. and Capsicum chinense Jacq. Agricultural Water Management, 234, 106104.
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