Application of MobileNetV2-Based Deep Learning in Detecting Diseases in Chili Plants
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Abstract
This study proposes a deep learning model based on MobileNetV2 architecture for the classification of chili leaf diseases using image data. The dataset was compiled from both public and private sources, covering six distinct categories of chili leaf conditions. MobileNetV2 was selected due to its efficiency and accuracy, making it ideal for real-time agricultural applications. The model was enhanced with additional layers to improve feature extraction and classification performance. Stratified 10-fold cross-validation was employed to ensure balanced evaluation across an imbalanced dataset. The experimental results showed an overall accuracy of 91.04% and an average F1-score of 0.906, indicating consistent and reliable classification performance across classes. Confusion matrix analysis highlighted strong predictive capability, particularly in detecting healthy leaves and severe disease symptoms, with minor misclassifications among visually similar categories. The findings confirm the potential of lightweight CNN architectures for practical, mobile-based agricultural diagnostics, contributing to advancements in precision farming and early disease management.
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How to Cite
Aji, N., Yudantoro, T., Safitri, Z., Kuntardjo, S., Mardiyono, M., Prayitno, P., & Santoso, K. (2025). Application of MobileNetV2-Based Deep Learning in Detecting Diseases in Chili Plants. Journal of Informatics Information System Software Engineering and Applications (INISTA), 7(2), 203-212. https://doi.org/10.20895/inista.v7i2.1825
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References
S. Mardiyati and M. Natsir, “Fluctuations and Trends in the Prices of Red Chilies and Cayenne Peppers in the Traditional Markets of Makassar City,” Iop Conf. Ser. Earth Environ. Sci., vol. 1302, no. 1, p. 12124, 2024, doi: 10.1088/1755-1315/1302/1/012124.
[2] F. Ahmad, K. Kusumiyati, M. A. Soleh, M. O. Khan, and R. S. Sundari, “Microclimates Growing and Watering Volumes Influences the Physiological Traits of Chili Pepper Cultivars in Combating Abiotic Stress,” 2024, doi: 10.21203/rs.3.rs-4916999/v1.
[3] E. K. Asamani, B. K. Maalekuu, P. Kumah, and F. Appiah, “Effect of Varieties, Solar Drying and Zeer Pot Refrigeration Lining Media on Physico – Chemical Characteristics of Chili Pepper Fruits,” Eur. J. Nutr. Food Saf., vol. 16, no. 4, pp. 139–150, 2024, doi: 10.9734/ejnfs/2024/v16i41416.
[4] A. P. W. Wibowo, “Penerapan Teknik Computer Vision Pada Bidang Fitopatologi Untuk Diteksi Penyakit Dan Hama Tanaman Cabai,” J. Inform. J. Pengemb. It, vol. 2, no. 2, pp. 102–108, 2017, doi: 10.30591/jpit.v2i2.528.
[5] E. A. Ukpoju, A. Adefemi, A. O. Adegbite, O. D. Balogun, B. O. Obaedo, and A. Abatan, “A Review of Sustainable Environmental Practices and Their Impact on U. S. Economic Sustainability,” World J. Adv. Res. Rev., vol. 21, no. 1, pp. 384–392, 2024, doi: 10.30574/wjarr.2024.21.1.2717.
[6] A. O. Adewusi, O. F. Asuzu, T. Olorunsogo, E. M. Adaga, and D. O. Daraojimba, “AI in Precision Agriculture: A Review of Technologies for Sustainable Farming Practices,” World J. Adv. Res. Rev., no. 1, pp. 2276–2285, 2024, doi: 10.30574/wjarr.2024.21.1.0314.
[7] N. B. Aji, Kurnianingsih, N. Masuyama, and Y. Nojima, “CNN-LSTM for Heartbeat Sound Classification,” Int. J. Informatics Vis., vol. 8, no. 2, pp. 735–741, 2024, doi: 10.62527/joiv.8.2.2115.
[8] N. B. Aji, L. Triyono, Kurnianingsih, W. Caesarendra, Mardiyono, and T. R. Yudantoro, “Health Protocol System: Face Mask Detection Using Deep Transfer Learning,” J. Eng. Sci. Technol., vol. 18, no. 4, pp. 17–30, 2023.
[9] D. Indrajaya, H. A. Parhusip, S. Trihandaru, and D. Hartanto, “MobileNetV2-D and Multiple Cameras for Swiftlet Nest Classification Based on Feather Intensity,” Indones. J. Electr. Eng. Comput. Sci., vol. 34, no. 2, p. 1144, 2024, doi: 10.11591/ijeecs.v34.i2.pp1144-1158.
[10] E. C. Too, Y. Li, S. Njuki, and Y. Liu, “A Comparative Study of Fine-Tuning Deep Learning Models for Plant Disease Identification,” Comput. Electron. Agric., vol. 161, pp. 272–279, 2019, doi: 10.1016/j.compag.2018.03.032.
[11] K. P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Comput. Electron. Agric., vol. 145, pp. 311–318, 2018, doi: 10.1016/j.compag.2018.01.009.
[12] W. Hu, Y. Huang, L. Wei, F. Zhang, and H.-C. Li, “Deep Convolutional Neural Networks for Hyperspectral Image Classification,” J. Sensors, vol. 2015, pp. 1–12, 2015, doi: 10.1155/2015/258619.
[13] Y. Cui, C. Zhang, K. Qiao, L. Wang, B. Yan, and L. Tong, “Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation,” Brain Sci., vol. 10, no. 9, p. 602, 2020, doi: 10.3390/brainsci10090602.
[14] F. Yang, “Enhancing Concrete Crack Image Detection Using MobileNetV2 and Transfer Learning,” Front. Sci. Eng., vol. 4, no. 4, pp. 110–119, 2024, doi: 10.54691/ynk4nf60.
[15] R. Gambheer and M. S. Bhat, “Optimized Compressed Sensing for IoT: Advanced Algorithms for Efficient Sparse Signal Reconstruction in Edge Devices,” Ieee Access, vol. 12, pp. 63610–63617, 2024, doi: 10.1109/access.2024.3396494.
[16] I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, and N. Doulamis, “Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem,” Technologies, vol. 9, no. 4, p. 81, 2021, doi: 10.3390/technologies9040081.
[17] I. Markoulidakis and G. Markoulidakis, “Probabilistic Confusion Matrix: A Novel Method for Machine Learning Algorithm Generalized Performance Analysis,” Technologies, vol. 12, no. 7, p. 113, 2024, doi: 10.3390/technologies12070113.
[18] J. I. Aizpurua et al., “Probabilistic Forecasting Informed Failure Prognostics Framework for Improved RUL Prediction Under Uncertainty: A Transformer Case Study,” Reliab. Eng. Syst. Saf., vol. 226, p. 108676, 2022, doi: 10.1016/j.ress.2022.108676.
[19] F. Shehzad, M. Islam, M. I. Omar, S. I. H. Shah, R. Ahmed, and N. Sohail, “Optimizations of Modified Machine Learning Algorithms Using K-Fold Cross Validations for Wheat Productivity: A Hyper Parametric Approach,” Sarhad J. Agric., vol. 38, no. 5, 2022, doi: 10.17582/journal.sja/2022/38.5.271.278.
[20] D. S. Soper, “Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy K-Fold Cross Validation,” Electronics, vol. 10, no. 16, p. 1973, 2021, doi: 10.3390/electronics10161973.
[2] F. Ahmad, K. Kusumiyati, M. A. Soleh, M. O. Khan, and R. S. Sundari, “Microclimates Growing and Watering Volumes Influences the Physiological Traits of Chili Pepper Cultivars in Combating Abiotic Stress,” 2024, doi: 10.21203/rs.3.rs-4916999/v1.
[3] E. K. Asamani, B. K. Maalekuu, P. Kumah, and F. Appiah, “Effect of Varieties, Solar Drying and Zeer Pot Refrigeration Lining Media on Physico – Chemical Characteristics of Chili Pepper Fruits,” Eur. J. Nutr. Food Saf., vol. 16, no. 4, pp. 139–150, 2024, doi: 10.9734/ejnfs/2024/v16i41416.
[4] A. P. W. Wibowo, “Penerapan Teknik Computer Vision Pada Bidang Fitopatologi Untuk Diteksi Penyakit Dan Hama Tanaman Cabai,” J. Inform. J. Pengemb. It, vol. 2, no. 2, pp. 102–108, 2017, doi: 10.30591/jpit.v2i2.528.
[5] E. A. Ukpoju, A. Adefemi, A. O. Adegbite, O. D. Balogun, B. O. Obaedo, and A. Abatan, “A Review of Sustainable Environmental Practices and Their Impact on U. S. Economic Sustainability,” World J. Adv. Res. Rev., vol. 21, no. 1, pp. 384–392, 2024, doi: 10.30574/wjarr.2024.21.1.2717.
[6] A. O. Adewusi, O. F. Asuzu, T. Olorunsogo, E. M. Adaga, and D. O. Daraojimba, “AI in Precision Agriculture: A Review of Technologies for Sustainable Farming Practices,” World J. Adv. Res. Rev., no. 1, pp. 2276–2285, 2024, doi: 10.30574/wjarr.2024.21.1.0314.
[7] N. B. Aji, Kurnianingsih, N. Masuyama, and Y. Nojima, “CNN-LSTM for Heartbeat Sound Classification,” Int. J. Informatics Vis., vol. 8, no. 2, pp. 735–741, 2024, doi: 10.62527/joiv.8.2.2115.
[8] N. B. Aji, L. Triyono, Kurnianingsih, W. Caesarendra, Mardiyono, and T. R. Yudantoro, “Health Protocol System: Face Mask Detection Using Deep Transfer Learning,” J. Eng. Sci. Technol., vol. 18, no. 4, pp. 17–30, 2023.
[9] D. Indrajaya, H. A. Parhusip, S. Trihandaru, and D. Hartanto, “MobileNetV2-D and Multiple Cameras for Swiftlet Nest Classification Based on Feather Intensity,” Indones. J. Electr. Eng. Comput. Sci., vol. 34, no. 2, p. 1144, 2024, doi: 10.11591/ijeecs.v34.i2.pp1144-1158.
[10] E. C. Too, Y. Li, S. Njuki, and Y. Liu, “A Comparative Study of Fine-Tuning Deep Learning Models for Plant Disease Identification,” Comput. Electron. Agric., vol. 161, pp. 272–279, 2019, doi: 10.1016/j.compag.2018.03.032.
[11] K. P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Comput. Electron. Agric., vol. 145, pp. 311–318, 2018, doi: 10.1016/j.compag.2018.01.009.
[12] W. Hu, Y. Huang, L. Wei, F. Zhang, and H.-C. Li, “Deep Convolutional Neural Networks for Hyperspectral Image Classification,” J. Sensors, vol. 2015, pp. 1–12, 2015, doi: 10.1155/2015/258619.
[13] Y. Cui, C. Zhang, K. Qiao, L. Wang, B. Yan, and L. Tong, “Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation,” Brain Sci., vol. 10, no. 9, p. 602, 2020, doi: 10.3390/brainsci10090602.
[14] F. Yang, “Enhancing Concrete Crack Image Detection Using MobileNetV2 and Transfer Learning,” Front. Sci. Eng., vol. 4, no. 4, pp. 110–119, 2024, doi: 10.54691/ynk4nf60.
[15] R. Gambheer and M. S. Bhat, “Optimized Compressed Sensing for IoT: Advanced Algorithms for Efficient Sparse Signal Reconstruction in Edge Devices,” Ieee Access, vol. 12, pp. 63610–63617, 2024, doi: 10.1109/access.2024.3396494.
[16] I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, and N. Doulamis, “Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem,” Technologies, vol. 9, no. 4, p. 81, 2021, doi: 10.3390/technologies9040081.
[17] I. Markoulidakis and G. Markoulidakis, “Probabilistic Confusion Matrix: A Novel Method for Machine Learning Algorithm Generalized Performance Analysis,” Technologies, vol. 12, no. 7, p. 113, 2024, doi: 10.3390/technologies12070113.
[18] J. I. Aizpurua et al., “Probabilistic Forecasting Informed Failure Prognostics Framework for Improved RUL Prediction Under Uncertainty: A Transformer Case Study,” Reliab. Eng. Syst. Saf., vol. 226, p. 108676, 2022, doi: 10.1016/j.ress.2022.108676.
[19] F. Shehzad, M. Islam, M. I. Omar, S. I. H. Shah, R. Ahmed, and N. Sohail, “Optimizations of Modified Machine Learning Algorithms Using K-Fold Cross Validations for Wheat Productivity: A Hyper Parametric Approach,” Sarhad J. Agric., vol. 38, no. 5, 2022, doi: 10.17582/journal.sja/2022/38.5.271.278.
[20] D. S. Soper, “Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy K-Fold Cross Validation,” Electronics, vol. 10, no. 16, p. 1973, 2021, doi: 10.3390/electronics10161973.