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All Journal Agriprima : Journal of Applied Agricultural Sciences
Ayesha Adzanni
Mulawarman University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology

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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 Linda Oktavianingsih; Muhammadiyah; Ervinda Yuliatin; Ayesha Adzanni; Galih Robby Wasesa; Marselina Yunita; Siti Rahmadani
Agriprima : Journal of Applied Agricultural Sciences Vol 10 No 2 (2026): SEPTEMBER
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/agriprima.v10i2.819

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.
Co-Authors Ervinda Yuliatin Galih Robby Wasesa Linda Oktavianingsih Marselina Yunita Muhammadiyah Siti Rahmadani