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All Journal Agriprima : Journal of Applied Agricultural Sciences Lentera Negeri
Linda Oktavianingsih
Mulawarman University
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Callus-derived silver nanoparticles from myrmecodia tuberosa: green synthesis and antioxidant properties Yanti Puspita Sari; Sandrila Sandrila; Enos Tangke Arung; Linda Oktavianingsih; Samsurianto Samsurianto; Lariman Lariman; Sus Trimurti
Lentera Negeri Vol. 7 No. 1 (2026): Lentera Negeri
Publisher : Indonesian Institute For Counseling, Education and Therapy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/992170

Abstract

Green synthesis of Silver nanoparticles (AgNPs) using plant-based materials is an environmentally friendly path for the synthesis of silver nanoparticles instead of traditional chemical methods. This study presents the first biosynthesis of AgNPs using an ethanolic extract obtained from in vitro callus cultures of Myrmecodia tuberosa Jack, an endangered medicinal epiphyte and conservation‑friendly biotechnological platform. Cotyledon explants were successfully induced high-frequency friable, green callus and the ethanolic extract acted as reducing and stabilizing agent. UV‑Vis spectroscopy revealed surface plasmon resonance peaks at 420–450 nm with a visible color change from light brown to dark brown, confirming the AgNP formation. The most intense and well-defined SPR peak indicating optimal nanoparticle synthesis was observed after 6–8 days static incubation at 35°C using 2 mM AgNO₃. In the DPPH radical scavenging assay, it could moderate antioxidant activities, with IC₅₀ = 51.80±0.035 μg mL⁻¹) which was more than that of parent callus extract (IC₅₀ = 58.91±0.097μgmL⁻¹). This improvement is probably a result of synergistic effects between silver core and phytochemical capping layer. These results indicate that callus cultures of M. tuberosa is an environmentally friendly, sustainable and conservation-friendly source of reducing metabolites for the preparation of green nanomaterials applicable in biomedical practices.
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 Ayesha Adzanni Enos Tangke Arung, Enos Tangke Ervinda Yuliatin Galih Robby Wasesa Lariman Lariman Marselina Yunita Muhammadiyah Samsurianto Sandrila Sandrila Siti Rahmadani Sus Trimurti Yanti Puspita Sari