Classification of Mango Leaf Diseases Based on Convolutional Neural Network (CNN) Using Images from Smartphone Cameras
Keywords:
Keywords: Mango Leaf Disease Classification, Convolutional Neural Network (CNN), Smartphone ImagesAbstract
Mango leaf disease is one of the main factors that can reduce crop productivity and quality. Conventional detection of mango leaf disease is still largely done through visual observation, which is subjective and depends on the experience of farmers or experts. Therefore, an automated method is needed that can identify diseases quickly and accurately. This study aims to develop a Convolutional Neural Network (CNN)-based mango leaf disease classification system using images obtained from a smartphone camera. The research data consists of mango leaf images of several classes, including healthy leaves and leaves with various types of diseases. The images were obtained by taking direct shots using a smartphone camera under varying lighting conditions. The research stages include image pre-processing, application of data augmentation techniques, CNN architecture design, model training process, and model performance evaluation. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the CNN model was able to classify mango leaf diseases with a high degree of accuracy and had good generalization capabilities against image condition variations. The use of smartphone cameras as a source of image data makes this system practical and easy to implement.
This research is expected to serve as the basis for developing an artificial intelligence-based early detection system for mango diseases to support smart agriculture.
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REFERENSI
[1] F. P. E. Putra, A. B. Tamam, R. W. Efendi, and ..., “Optimasi Keamanan DNS: Eksplorasi Optimal dengan Implementasi DNS Security Extensions (DNSSEC),” REMIK Ris. dan E …, 2024, [Online]. Available: https://jurnal.polgan.ac.id/index.php/remik/article/view/13398
[2] F. P. E. Putra, F. Fauzan, S. Syirofi, M. Mursidi, D. Wahid, and A. Nuraini, “Sistem Pengendali Lingkungan Pertanian Dengan Wireless Sensor Network Untuk Mengoptimalkan Budidaya Hidroponik,” 2024. doi: 10.47709/digitech.v3i2.3461.
[3] F. P. E. Putra, M. Ghummah, M. Amrullah, and R. Hidayatullah, “Studi Kinerja Mesh Network untuk Penerapan Internet of Things (IoT) di Lingkungan Perkotaan,” 2025, researchgate.net.
[4] F. P. E. Putra, D. A. M. Putra, A. Firdaus, and A. Hamzah, “Analisis Kecepatan Dan Kinerja Jaringan 5G (generasi ke 5) Pada Wilayah Perkotaan,” INFORMATICS Educ. Prof. J. Informatics, vol. 8, no. 1, p. 47, 2023, doi: 10.51211/itbi.v8i1.2439.
[5] F. P. Eka Putra, F. Muslim, N. Hasanah, Holipah, R. Paradina, and R. Alim, “Analisis Komparasi Protokol Websocket dan MQTT Dalam Proses Push Notification,” J. Sistim Inf. dan Teknol., pp. 63–72, 2024, doi: 10.60083/jsisfotek.v5i4.325.
[6] F. P. Eka Putra, Amir Hamzah, W. Agel, and R. O. Firmansyah Kusuma, “Impelementasi Sistem Keamanan Jaringan Mikrotik Menggunakan Firewall Filtering dan Port Knocking,” J. Sistim Inf. dan Teknol., pp. 82–87, 2024, doi: 10.60083/jsisfotek.v5i4.329.
[7] F. P. Eka Putra, M. N. Arifin, K. Zulfana Imam, E. Saputra, and Sofiyullah, “Pengembangan Sistem Informasi Laboratorium Terintegerasi Sistem Akademik Menggunakan Agile Scrum,” J. Inf. dan Teknol., pp. 109–119, 2023, doi: 10.37034/jidt.v5i2.367.
[8] F. P. E. Putra, D. E. Arissandi, A. Rofiqi, and M. F. Hidayat, “Pemanfaatan Mikrotik Dalam Manajemen Bandwidth Pada Jaringan Sekolah,” 2025, researchgate.net. [Online]. Available: https://www.researchgate.net/profile/Fauzan-Eka-Putra-2/publication/392420575_Pemanfaatan_Mikrotik_Dalam_Manajemen_Bandwidth_Pada_Jaringan_Sekolah/links/6848fab46b5a287c304a61ca/Pemanfaatan-Mikrotik-Dalam-Manajemen-Bandwidth-Pada-Jaringan-Sekolah.pdf
[9] F. P. E. Putra, D. T. Agustina, T. S. K. Khotimah, and T. Ramadhanty, “Analisis Kinerja Jaringan 5G dalam Meningkatkan Konektivi-tas Internet of Things (IoT),” 2025, researchgate.net. [Online]. Available: https://www.researchgate.net/profile/Fauzan-Eka-Putra-2/publication/392420839_Analisis_Kinerja_Jaringan_5G_dalam_Meningkatkan_Konektivitas_Internet_of_Things_IoT/links/6848f86cdf0e3f544f5e49e9/Analisis-Kinerja-Jaringan-5G-dalam-Meningkatkan-Konektivitas-I
[10] F. P. E. Putra, A. M. U. Solichin, and ..., “Pemanfaatan Teknologi Wireless dan Mobile Network Berbasis 5G Untuk Pemerataan Akses Jaringan di Indonesia,” Infotek J. …, 2025, [Online]. Available: https://e-journal.hamzanwadi.ac.id/index.php/infotek/article/view/30559
[11] N. Muhammad Akbar, F. Prasetyo Eka Putra, K. Zulfana Imam, and M. Umar Mansyur, “Analisis Kinerja dan Interopabilitas STB Sebagai Server Penilaian Akhir Tahun,” J. Inf. dan Teknol., pp. 91–96, 2023, doi: 10.37034/jidt.v5i2.365.
[12] W. Zhang, J. Liang, and X. Liang, “Approximation Algorithms for Computing Virtual Backbones Considering Routing Costs in Wireless Networks,” IEEE/ACM Trans. Netw., vol. 32, no. 1, pp. 323–337, 2024, doi: 10.1109/TNET.2023.3284051.
[13] J. Yang et al., “Coalescence-induced jumping of droplets on superhydrophobic triangular prisms: Influence of structural parameters on dynamics and energy conversion using a three-dimensional lattice Boltzmann method,” Phys. Fluids, vol. 37, no. 5, 2025, doi: 10.1063/5.0270547.
[14] L. Calisti and E. Lattanzi, “Real-Time Energy-Efficient Sensor Libraries for Wearable Devices,” IEEE Access, vol. 12, pp. 126006–126018, 2024, doi: 10.1109/ACCESS.2024.3430049.
[15] D.-W. Park, D. R. Utomo, B. Yun, H. U. Mahmood, and S.-G. Lee, “A D-Band Power Amplifier in 65-nm CMOS by Adopting Simultaneous Output Power-and Gain-Matched Gmax-Core,” IEEE Access, vol. 9, pp. 99039–99049, 2021, doi: 10.1109/ACCESS.2021.3096423.
[16] G. Li, T. Shang, T. Tang, and Q. Li, “Maximizing effective secrecy throughput in SLIPT-based underwater wireless optical communication systems with artificial noise,” Appl. Opt., vol. 64, no. 18, pp. 5027–5036, 2025, doi: 10.1364/AO.558644.
[17] J. Li, H. Xu, and Y. Wang, “Multiresolution Fusion Convolutional Network for Open Set Human Activity Recognition,” IEEE Internet Things J., vol. 10, no. 13, pp. 11369–11382, 2023, doi: 10.1109/JIOT.2023.3243476.
[18] R. Elango and D. Maruthanayagam, “TRUXL: TRUST-BASED SECURE ROUTING AGAINST RPL ATTACKS IN IOT USING XGBOOST WITH CNN-LSTM,” J. Theor. Appl. Inf. Technol., vol. 103, no. 16, pp. 6483–6503, 2025, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105016518184&partnerID=40&md5=278e4eb19f0a3aa4c16f361e66215481
[19] S. Sakraoui, M. Makhlouf, A. Ahmim, R. Almukhlifi, M. Ahmim, and I. Ullah, “TL2AB : Trusted lightweight authentication using AI and blockchain for 6G networks,” Internet Things (The Netherlands), vol. 33, 2025, doi: 10.1016/j.iot.2025.101661.
[20] W.-T. Sung, I. G. T. Isa, and S.-J. Sung-Jung, “Value Function Mechanism in WSNs-Based Mango Plantation Monitoring System,” Comput. Mater. Contin., vol. 80, no. 3, pp. 3733–3759, 2024, doi: 10.32604/cmc.2024.053634.
[21] K. Song, H.-T. Huan, L.-C. Wei, and C.-X. Liu, “Performance analysis of a modified Archimedes spiral wind turbine having winglet on blade,” Energy, vol. 333, 2025, doi: 10.1016/j.energy.2025.137389.
[22] A. Shrotriya, A. Sharma, S. Prabhu, and A. Bairwa, “An Approach Toward Classifying Plant-Leaf Diseases and Comparisons With the Conventional Classification,” IEEE Access, vol. 12, pp. 117379–117398, 2024, doi: 10.1109/ACCESS.2024.3411013.
[23] H. Wang, J. Tao, and H. Li, “Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC,” IEEE Access, vol. 12, pp. 72117–72130, 2024, doi: 10.1109/ACCESS.2024.3403893.
[24] Z. Ren et al., “Directly Preparable self-attached triboelectric nanogenerator on living plant leaf,” Nano Energy, vol. 136, 2025, doi: 10.1016/j.nanoen.2025.110761.
[25] I. Gorņevs, V. Jurķāns, and J. Blums, “Development of Wearable Multiple Source Energy-Harvesting System for Smart Clothing,” IEEE Access, vol. 11, pp. 100284–100294, 2023, doi: 10.1109/ACCESS.2023.3313559.
[26] D. Rao, T. Xu, and X. J. Wu, “TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network,” IEEE Trans. Image Process., 2023, doi: 10.1109/TIP.2023.3273451.
[27] X. Duan, Y. Chen, M. Li, Y. Rong, R. Xie, and J. Han, “UArch: A Super-Resolution Processor with Heterogeneous Triple-Core Architecture for Workloads of U-Net Networks,” IEEE Trans. Biomed. Circuits Syst., vol. 17, no. 3, pp. 633–647, 2023, doi: 10.1109/TBCAS.2023.3261060.
[28] R. Azad et al., “Medical Image Segmentation Review: The Success of U-Net,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 12, pp. 10076–10095, 2024, doi: 10.1109/TPAMI.2024.3435571.
[29] Y. Song, Q. Zheng, B. Liu, and X. Gao, “EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 710–719, 2023, doi: 10.1109/TNSRE.2022.3230250.
[30] X. Wang, Y. Ma, J. Cammon, F. Fang, Y. Gao, and Y. Zhang, “Self-Supervised EEG Emotion Recognition Models Based on CNN,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 1952–1962, 2023, doi: 10.1109/TNSRE.2023.3263570.
[31] M. Azadimotlagh, N. Jafari, and R. Sharafdini, “Review on Architecture and Challenges in Smart Cities,” J. Inf. Syst. Telecommun., vol. 13, no. 1, pp. 33–49, 2025, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006676229&partnerID=40&md5=ddbcdcf26331529b323aae79d9e5872e
[32] F.-R. Teng et al., “Multifunctional flexible multilayer pressure sensor for pressure sensing, energy harvesting and versatile applications,” Chem. Eng. J., vol. 522, 2025, doi: 10.1016/j.cej.2025.166937.
[33] T. Pacini, E. Rapuano, and L. Fanucci, “FPG-AI: A Technology-Independent Framework for the Automation of CNN Deployment on FPGAs,” IEEE Access, vol. 11, pp. 32759–32775, 2023, doi: 10.1109/ACCESS.2023.3263392.
[34] M. A. Taher, M. Behnamfar, A. I. I Sarwat, and M. Tariq, “Wavelet and Signal Analyzer Based High- Frequency Ripple Extraction in the Context of MPPT Algorithm in Solar PV Systems,” IEEE Access, vol. 12, pp. 113726–113740, 2024, doi: 10.1109/ACCESS.2024.3426289.
[35] V.-D. Le, T.-C. Bui, and W.-S. Li, “Efficient ML Lifecycle Transferring for Large-Scale and High-Dimensional Data via Core Set-Based Dataset Similarity,” IEEE Access, vol. 11, pp. 73823–73838, 2023, doi: 10.1109/ACCESS.2023.3296136.
[36] H. Kim, H. Lim, D. J. Hwang, S. Kim, T. Kang, and S. K. Kim, “A systematic study of the evaporation performance of column-type 3D solar evaporators with variations in the surrounding temperatures,” Desalination, vol. 592, 2024, doi: 10.1016/j.desal.2024.118077.
[37] P. Guo, S. Xu, and W. Liang, “A cloud-assisted anonymous and privacy-preserving authentication scheme for internet of medical things,” Comput. Secur., vol. 157, 2025, doi: 10.1016/j.cose.2025.104614.
[38] H. Jalajamony, S. De, and R. E. Fernandez, “NFC-Enabled Batteryless AI-Integrated Sensing Network for Smart PPE System,” IEEE Sens. J., vol. 24, no. 16, pp. 26914–26925, 2024, doi: 10.1109/JSEN.2024.3419442.
[39] P. Katsikouli, D. Madariaga, A. C. Viana, A. Tarable, and M. Fiore, “DuctiLoc: Energy-Efficient Location Sampling With Configurable Accuracy,” IEEE Access, vol. 11, pp. 15375–15389, 2023, doi: 10.1109/ACCESS.2023.3243731.
[40] Q. Yan, G. Wang, D. Zhu, and J. Li, “FedIoT: Optimizing the Communication-Efficient Federated Learning Aggregation Algorithm Under Heterogeneous Data for Large-Scale IoT,” Int. J. Pattern Recognit. Artif. Intell., vol. 39, no. 11, 2025, doi: 10.1142/S0218001425520135.
[41] M. A. Al Samara, I. Bennis, A. Abouaissa, and P. Lorenz, “SA-O2DCA: Seasonal Adapted Online Outlier Detection and Classification Approach for WSN,” J. Netw. Syst. Manag., vol. 32, no. 2, 2024, doi: 10.1007/s10922-024-09801-3.
[42] Y. Dai, X. Jiang, K. Wang, and K. Li, “A phototunable self-oscillatory bistable seesaw via liquid crystal elastomer fibers,” Chaos, Solitons and Fractals, vol. 200, 2025, doi: 10.1016/j.chaos.2025.117041.
[43] I. P. Patil, S. Chandrasekaran, P. A. Meher Prasad, and S. N. A. Sarinya, “Experimental investigation of TSUSUCA DOLPHIN under regular waves,” J. Ocean Eng. Mar. Energy, vol. 11, no. 1, pp. 183–196, 2025, doi: 10.1007/s40722-024-00377-3.
[44] I. Colbert, K. Kreutz-Delgado, and S. Das, “An energy-efficient edge computing paradigm for convolution-based image upsampling,” IEEE Access, vol. 9, pp. 147967–147984, 2021, doi: 10.1109/ACCESS.2021.3123938.
[45] M. N. Bhalia and A. A. Bavarva, “Enhanced Secure and Efficient Routing Algorithm for Optimal Multimedia Data Transmission,” Serbian J. Electr. Eng., vol. 22, no. 2, pp. 147–163, 2025, doi: 10.2298/SJEE2502147B.
[46] K. Abedi, R. Ansari, and M. K. Hassanzadeh-Aghdam, “Effects of aspect ratio and arrangement of PZT-7A piezoelectric fillers on energy harvesting performance of PVDF composite cantilevers,” Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., vol. 239, no. 17 Special Issue: Materials, processes, and procedures: looking for a more sustainable world, pp. 6968–6982, 2025, doi: 10.1177/09544062251343709.
[47] O. H. Embarak and R. A. Zitar, “Securing wireless sensor networks against dos attacks in industrial 4.0,” J. Intell. Syst. Internet Things, vol. 8, no. 1, pp. 66–74, 2023, doi: 10.54216/JISIoT.080106.
[48] A. A. Al-Hamadani, M. J. Mohammed, and S. M. Tariq, “Normalized deep learning algorithms based information aggregation functions to classify motor imagery EEG signal,” Neural Comput. Appl., vol. 35, no. 30, pp. 22725–22736, 2023, doi: 10.1007/s00521-023-08944-9.
[49] C. C. Rawlins and S. Jagannathan, “An Intelligent Distributed Ledger Construction Algorithm for IoT,” IEEE Access, vol. 10, pp. 10838–10851, 2022, doi: 10.1109/ACCESS.2022.3146343.
[50] N. Gómez-Vargas, S. Maldonado, and C. Vairetti, “A predict-and-optimize approach to profit-driven churn prevention,” Eur. J. Oper. Res., vol. 324, no. 2, pp. 555–566, 2025, doi: 10.1016/j.ejor.2025.02.008.
[51] J. Shu, Y. Quan, and D. Yang, “An LSTM–AE–Bayes embedded gateway for real-time anomaly detection in agricultural wireless sensor networks,” Smart Agric. Technol., vol. 11, 2025, doi: 10.1016/j.atech.2025.100944.
[52] Y. Cao, Z. Guo, and Y. Qu, “Mechanically induced electric potential and charge redistribution in laminated composite piezoelectric semiconductor circular cylindrical thin shells,” Thin-Walled Struct., vol. 195, 2024, doi: 10.1016/j.tws.2023.111372.
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