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Article | Green Intelligent Systems and Applications | Volume 6 - Issue 1 - 2026 | 174−194 | https://doi.org/10.53623/gisa.v6i1.1239
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Classification of Leaf Diseases in Guava Plants Based on Images Using the MobileNetV3 Model

Author(s): Rikky , Ery Hartati
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Faculty of Computer Science and Engineering, Universitas Multi Data Palembang, South Sumatra, Indonesia

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Green Intelligent Systems and Applications

Volume 6 - Issue 1 - 2026

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Guava cultivation is often threatened by leaf diseases that disrupted plant growth and reduced agricultural productivity. Early and accurate disease identification was crucial but relied heavily on slow and subjective visual inspections by human experts. This study proposed an automated, accurate, and efficient solution by comparing lightweight deep learning models. A total of 656 augmented guava leaf images representing four classes (Algal Leaf Spot, Insects Eaten, Red Rust, and Healthy Leaf) were evaluated. Using a transfer learning approach, the hyperparameters were systematically tuned for MobileNetV3-Small as the proposed model and compared with MobileNetV2 as the baseline architecture. The experimental results demonstrated that MobileNetV3-Small achieved a superior test accuracy of 91.00%, outperforming MobileNetV2, which achieved 87.00%. The integration of Squeeze-and-Excitation (SE) modules and the h-swish activation function in MobileNetV3-Small significantly improved the identification of subtle visual symptoms, particularly for the Healthy Leaf and Insects Eaten classes. However, MobileNetV2 maintained a slight advantage in real-time processing speed (54.86 FPS versus 50.30 FPS) because of memory-bound bottlenecks associated with the SE modules. Overall, MobileNetV3-Small provided superior diagnostic accuracy, whereas MobileNetV2 remained a highly viable option for latency-critical deployment on low-end devices.

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Anam, F.S.; Muttaqin, M.R.; Ramadhan, Y.R. (2023). Klasifikasi penyakit pada daun dan buah jambu menggunakan convolutional neural network. JOINTECS (Journal of Information Technology and Computer Science), 8(3), 115. http://doi.org/10.31328/jointecs.v8i3.4823.

Badan Pusat Statistik. Produksi Jambu Biji Indonesia. (accessed on 27 June 2026) Available online: https://www.bps.go.id/id/statistics-table/3/U0dKc1owczVSalJ5VFdOMWVETnlVRVJ6YlRJMFp6MDkjMw==/produksi-tanaman-buah-buahan-dan-sayuran-tahunan-menurut-provinsi-dan-jenis-tanaman--2024.html?year=2024.

Bakara, F.K.R.D.J. (2020). Pendampingan petani dalam pengendalian hama dan penyakit jambu biji (Psidium guajava L.) di Desa Cibening, Pamijahan. Jurnal Pusat Inovasi Masyarakat, 2(1), 131–143.

Yamin, M.; Sulastri, M.A.; Damayanthy, D. (2025). Enhancing agricultural productivity and food security through Climate Smart Agriculture (CSA) adoption: The interplay of social, economic and environmental in tidal swamp farming. AGRIS On-line Papers in Economics and Informatics, 17(3), 117–131. http://doi.org/10.7160/aol.2025.170310.

Orjiakor, E.C. (2025). Securing food systems: Pathways of COVID-19 impact on food security for smallholder farmers in Nigeria. Journal of Hunger & Environmental Nutrition, 1–12. http://doi.org/10.1080/19320248.2025.2564145.

Kurniawan, D.; Apriandari, W.; Pambudi, A. (2023). Sistem pakar diagnosa hama penyakit tanaman jambu kristal penerapan metode certainty factor. Bit (Fakultas Teknologi Informasi Universitas Budi Luhur), 20(2), 168–177. http://doi.org/10.36080/bit.v20i2.2462.

Lv, Q.; Zhang, S.; Wang, Y. (2022). Deep learning model of image classification using machine learning. Advances in Multimedia, 2022. http://doi.org/10.1155/2022/3351256.

Syahputra, F. (2023). Analisis arsitektur deep learning MobileNet dalam mengklasifikasi hama daun jambu madu. Bachelor's Thesis, Universitas Medan Area, Medan, Indonesia.

Maximilliano, W.; Rachmat, N. (2025). Comparative analysis of MobileNetV3-Large and small for corn leaf disease classification. Brilliance: Research of Artificial Intelligence, 5(1), 325–332. http://doi.org/10.47709/brilliance.v5i1.6259.

Yin, X.; Li, W.; Li, Z.; Yi, L. (2022). Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning. International Journal of Agricultural and Biological Engineering, 15(3), 184–194. http://doi.org/10.25165/j.ijabe.20221503.7062.

Zhang, J.; Yang, X.; Fu, X.; Wang, B.; Li, H. (2025). LDL-MobileNetV3S: An enhanced lightweight MobileNetV3-small model for potato leaf disease diagnosis through multi-module fusion. Frontiers in Plant Science, 16, 1–19. http://doi.org/10.3389/fpls.2025.1656731.

Duhan, S.; Gulia, P.; Gill, N.S.; Oliveira, T.A.; Singh, P.K. (2026). Evaluation of lightweight and efficient deep learning models for plant disease classification. Discover Internet of Things, 6(1). http://doi.org/10.1007/s43926-026-00310-0.

Xu, Y.; Li, D.; Li, C.; Yuan, Z.; Dai, Z. (2025). LiSA-MobileNetV2: An extremely lightweight deep learning model with Swish activation and attention mechanism for accurate rice disease classification. Frontiers in Plant Science, 16, 1–14. http://doi.org/10.3389/fpls.2025.1619365.

Nur Rifa'i, W.R.; Erwanto, D.; Yanuartanti, I. (2025). Evaluasi kinerja CNN dengan optimizer RMSprop, Adam dan SGD dalam klasifikasi penyakit daun anggur. JE-Unisla, 10(1), 91–104. http://doi.org/10.30736/je-unisla.v10i1.1409.

Mathew, A.; Amudha, P.; Sivakumari, S. (2021). Deep learning techniques: An overview. In Advanced Machine Learning Technologies and Applications, pp. 599–608; Springer Singapore: Singapore.

Nurhakiki, J.; Yahfizham, Y. (2024). Studi kepustakaan: Pengenalan 4 algoritma pada pembelajaran deep learning beserta implikasinya. Pendekar: Jurnal Pendidikan Berkarakter, 2(1), 270–281. http://doi.org/10.51903/pendekar.v2i1.598.

Ramadhan, M.F.; Fachrie, M. (2024). Implementasi algoritma Long Short-Term Memory (LSTM) pada sistem prediksi hasil panen sawit. Jurnal Informatika Teknologi dan Sains, 6(4), 937–944. http://doi.org/10.51401/jinteks.v6i4.4876.

Alzubaidi, L.; et al. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. http://doi.org/10.1186/s40537-021-00444-8.

Salka, T.D.; Hanafi, M.B.; Syed Ahmad Abdul Rahman, S.M.; Mohamed Zulperi, D.B.; Omar, Z. (2025). Plant leaf disease detection and classification using convolutional neural networks model: A review. Artificial Intelligence Review, 58, 322. http://doi.org/10.1007/s10462-025-11234-6.

Zhao, X.; Wang, L.; Zhang, Y.; Han, X.; Deveci, M.; Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(4), 99. http://doi.org/10.1007/s10462-024-10721-6.

Dhaka, V.S.; et al. (2021). A survey of deep convolutional neural networks applied for prediction of plant leaf diseases. Sensors, 21(14). http://doi.org/10.3390/s21144749.

Georgieva, P. (2019). Deep learning in brain computer interfaces. In ACM International Conference Proceeding Series. http://doi.org/10.1145/3351556.3351594.

Howard, A.; et al. (2019). Searching for MobileNetV3. arXiv preprint. http://doi.org/10.48550/arXiv.1905.02244.

Gao, Y.; Jiang, Y.; Peng, Y.; Yuan, F.; Zhang, X.; Wang, J. (2025). Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods. Tomography, 11, 52. https://doi.org/10.3390/tomography11050052.

Ekmekyapar, T.; Taşcı, B. (2023). Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis. Diagnostics, 13, 3030. https://doi.org/10.3390/diagnostics13193030.

Khan, A.T.; Jensen, S.M.; Khan, A.R.; Li, S. (2023). Plant disease detection model for edge computing devices. Frontiers in Plant Science, 14, 1–10. http://doi.org/10.3389/fpls.2023.1308528.

Dhanalaxmi, B.; Kumar, B.N.; Raju, Y.; Channapragada, R.S.R. (2025). MobileNetV3: An efficient deep learning-based feature selection and classification technique for cardiovascular disease. Journal of Engineering and Applied Science, 72(1), 1–33. http://doi.org/10.1186/s44147-025-00654-4.

Hu, J.; Shen, L.; Sun, G. (2018). Squeeze and excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132–7141.

Hosna, A.; Merry, E.; Gyalmo, J.; Alom, Z.; Aung, Z.; Azim, M.A. (2022). Transfer learning: A friendly introduction. Journal of Big Data, 9(1). http://doi.org/10.1186/s40537-022-00652-w.

Altınel, D. (2025). Development of deep learning optimizers: Approaches, concepts, and update rules. arXiv preprint. http://doi.org/10.48550/arXiv.2509.18396.

Dogo, E.M.; Afolabi, O.J.; Twala, B. (2022). On the relative impact of optimizers on convolutional neural networks with varying depth and width for image classification. Applied Sciences, 12(23). http://doi.org/10.3390/app122311976.

Yamasari, Y.; Sadewa, B.A.; Qoiriah, A.; Yohannes, E.; Putra, R.E.; Ahmad, T. (2025). Hyperparameter analysis of Adam-optimized deep transfer learning for Indonesian banknote-denomination recognition. Journal of Hunan University Natural Sciences, 52(5). http://doi.org/10.55463/issn.1674-2974.52.5.19.

Tian, Y.; Zhang, Y. (2022). A comprehensive survey on regularization strategies in machine learning. Information Fusion, 80, 146–166. http://doi.org/10.1016/j.inffus.2021.11.005.

An, C.; Park, Y.W.; Ahn, S.S.; Han, K.; Kim, H.; Lee, S.K. (2021). Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results. PLOS ONE, 16(8), 1–13. http://doi.org/10.1371/journal.pone.0256152.

Mustak Un Nobi, M.; Rifat, M.; Mridha, M.F.; Alfarhood, S.; Safran, M.; Che, D. (2023). GLD-Det: Guava leaf disease detection in real-time using lightweight deep learning approach based on MobileNet. Agronomy, 13(9), 1–21. http://doi.org/10.3390/agronomy13092240.

Pal, C.; Karmakar, S.; Mukherjee, I.; Chakrabarti, P.P. (2025). A lightweight and explainable CNN model for empowering plant disease diagnosis. Scientific Reports, 15(1), 1–14. http://doi.org/10.1038/s41598-025-94083-1.

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SUBMITTED: 08 June 2026
ACCEPTED: 29 June 2026
PUBLISHED: 30 June 2026
SUBMITTED to ACCEPTED: 21 days
DOI: https://doi.org/10.53623/gisa.v6i1.1239

Cite this article
Rikky, & Hartati, E. (2026). Classification of Leaf Diseases in Guava Plants Based on Images Using the MobileNetV3 Model. Green Intelligent Systems and Applications, 6(1), 174−194. https://doi.org/10.53623/gisa.v6i1.1239
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