Digital Identification of Mango Leaf Diseases (Mangifera indica L.) through the Integrated Use of ImageJ, Plantix, and Agri AI Software for Accurate and Efficient Diagnosis
Downloads
Abstract
Food crops play an important role in supporting food security and the development of human civilization, including mango (Mangifera indica L.), which has high economic value but is susceptible to various leaf diseases. This study aimed to identify the types of diseases affecting mango leaves and to compare the analytical results obtained using ImageJ, Plantix, and Agri AI software. The method applied was purposive sampling by selecting leaf samples that exhibited disease symptoms. The tools used included HVS paper, a ruler, the Plantix and Agri AI applications, ImageJ software, and a smartphone, while the materials consisted of mango leaf samples and tissue paper. The research procedure began with a field survey to select symptomatic leaves, followed by disease identification using Agri AI and Plantix, and severity level analysis using ImageJ. The results showed that sample A was infected with anthracnose caused by Colletotrichum sp. and Pezizomycotina sp., whereas sample B was affected by cercospora leaf spot caused by Cercospora sp. and algal leaf spot caused by the parasitic alga Cephaleuros virescens. Quantitative analysis using ImageJ indicated that disease severity in sample A was 19.93%, while in sample B it was 16.65%. It can be concluded that the combined use of the three software applications supports more objective and accurate identification of disease types and severity levels.
Keywords:
Artificial intelligence Image analysis leaf disease mango severityReferences
Asmita, P., Pramila, P., Manju, Y., Asmita, B., & Kumar, Y. P. (2022). Insights on the mango anthracnose and its management. Journal of Plant Pathology Research, 4(1).
Berutu, L. H., Tantawi, R., A., & Wardani, D. K. (2023). Analisis perbandingan perkembangan penyakit bercak daun (Cercospora capsici) pada tanaman cabai merah (Capsicum annuum L.) di dataran tinggi dan dataran rendah selama musim hujan: Studi kasus di Kabupaten Karo dan Deli Serdang. Paspalum: Jurnal Ilmiah Pertanian, 11(2), 261–270.
Dhawan, K., Perumal, S., R., & Rk, N. (2024). Image-based Mangifera indica leaf disease detection using transfer learning for deep learning methods. ELCVIA Electronic Letters on Computer Vision and Image Analysis, 22(2), 27–40.
Dofuor, A. K., Quartey, N. K. A., Osabutey, A. F., Antwi-Agyakwa, A. K., Asante, K., Boateng, B. O., & Ninsin, K. D. (2023). Mango anthracnose disease: the current situation and direction for future research. Frontiers in Microbiology, 14, 1168203.
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
Fiani, A., & Hadiyan, Y. (2017). Respon populasi asal cendana (Santalum album L.) terhadap serangan embun jelaga. In Proceeding Biology Education Conference: Biology, Science, Environmental, and Learning, 14(1), 106–108.
Lebaka, V. R., Wee, Y.-J., Ye, W., & Korivi, M. (2021). Nutritional composition and bioactive compounds in three different parts of mango fruit. International Journal of Environmental Research and Public Health, 18(2), 741.
Liu, R., Liu, H., Wang, Y., Chen, J., Qiu, Z., Zheng, Y., Sun, B., Tan, X., Shu, C., Liu, S., & Zheng, P. (2024). Study on the Effect of Sooty Mould Disease in Tea Plants. Plants, 13(16), 2321.
Lobet, G., Draye, X., & Périlleux, C. (2013). An online database for plant image analysis software tools. Plant Methods, 9(1), 38.
Nyawose, T., Maswanganyi, C., R., & Khumalo, P. (2025). A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches. Journal of Imaging, 11(10), 326.
Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture, 161, 280–290.
Rattanakreetakul, C., Keawmanee, P., Bincader, S., Mongkolporn, O., Phuntumart, V., Chiba, S., & Pongpisutta, R. (2023). Two newly identified Colletotrichum species associated with mango anthracnose in Central Thailand. Plants, 12(5), 1130.
Ristaino, J. B., Anderson, P. K., Bebber, D. P., Brauman, K. A., Cunniffe, N. J., Fedoroff, N. V, Finegold, C., Garrett, K. A., Gilligan, C. A., Jones, C. M., Martin, M. D., MacDonald, G. K., Neenan, P., Records, A., Schmale, D. G., Tateosian, L., & Wei, Q. (2021). The persistent threat of emerging plant disease pandemics to global food security. Proceedings of the National Academy of Sciences, 118(23).
Rizky, M., Wibisono, A., Nugroho, R. R. A., Syalom, D. P., Lulang, J., & Untari, L. (2023). Alga parasit Cephaleuros di lingkungan kampus Fakultas Biologi Universitas Gadjah Mada Yogyakarta. Berkala Ilmiah Biologi, 14(3), 1–9.
Rosadi, I., Ayuni, C. L. Q., Nurcahyani, I., Muhammadiyah, M., Butar-Butar, P., I. P., & Oktavianingsih, L. (2022). Analisis tingkat keparahan penyakit pada daun tanaman pangan dengan menggunakan software ImageJ dan Plantix. Bioscientist: Jurnal Ilmiah Biologi, 10(1), 100–108.
Rosadi, I., Oktavianingsih, L., Ayuni, C. L. Q., Muhammadiyah, M., Aulia, A. P., Juhri, D. A. P., Dewi, P., I., & Soviana, S. (2023). Tingkat keparahan penyakit pada daun mangga (Mangifera indica) menggunakan software ImageJ dan Plantix serta kultur bakteri pada nutrient agar (NA). BIOEDUSAINS: Jurnal Pendidikan Biologi Dan Sains, 6(2), 625–633.
Rymbai, H., Laxman, R. H., Dinesh, M. R., Sunoj, V. S. J., Ravishankar, V., K., & Jha, A. K. (2014). Diversity in leaf morphology and physiological characteristics among mango (Mangifera indica) cultivars popular in different agro-climatic regions of India. Scientia Horticulturae, 176, 189–193.
Savary, S., Willocquet, L., Pethybridge, S. J., Esker, P., McRoberts, N., & Nelson, A. (2019). The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution, 3(3), 430–439.
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: An open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682.
Shafay, M., Hassan, T., Owais, M., Hussain, I., Khawaja, S. G., Seneviratne, L., & Werghi, N. (2025). Recent advances in plant disease detection: challenges and opportunities. Plant Methods, 21(1), 140.
Springmann, M., Clark, M., Mason-D’Croz, D., Wiebe, K., Bodirsky, B. L., Lassaletta, L., Vries, W. de, Vermeulen, S. J., Herrero, M., Carlson, K. M., Jonell, M., Troell, M., DeClerck, F., Gordon, L. J., Zurayk, R., Scarborough, P., Rayner, M., Loken, B., Fanzo, Willett, W. (2018). Options for keeping the food system within environmental limits. Nature, 562(7728), 519–525.
Tilman, D., Clark, M., Williams, D. R., Kimmel, K., Polasky, S., & Packer, C. (2017). Future threats to biodiversity and pathways to their prevention. Nature, 546(7656), 73–81.
Upadhyay, A., Chandel, N. S., Singh, K. P., Chakraborty, S. K., Nandede, B. M., Kumar, M., & Elbeltagi, A. (2025). Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture. Artificial Intelligence Review, 58(3), 92.
Velásquez, A. C., Castroverde, M., C. D., & He, S. Y. (2018). Plant–pathogen warfare under changing climate conditions. Current Biology, 28(10), R619–R634.
Zakaria, L. (2021). Diversity of Colletotrichum species associated with anthracnose disease in tropical fruit crops—A review. Agriculture, 11(4), 297.
License
Copyright (c) 2026 Linda Oktavianingsih, Muhammadiyah, Ervinda Yuliatin, Ayesha Adzanni, Galih Robby Wasesa, Marselina Yunita, Siti Rahmadani (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
