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All Journal Agriprima : Journal of Applied Agricultural Sciences
Muhammadiyah
Bandung Institute of Technology
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology

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Effect of Nicosulfuron as Follow-Up Herbicide after Paraquat in Sweet Corn No-Tillage System Yayan Sumekar; Uum Umiyati; Rida Salsabila; Pauline Elisabeth; Muhammadiyah
Agriprima : Journal of Applied Agricultural Sciences Vol 10 No 1 (2026): MARCH
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/agriprima.v10i1.800

Abstract

Sweet corn is a high-value cereal crop whose productivity is often limited by weed infestation, especially under no-tillage (NT) systems. This study aimed to evaluate the effectiveness of Nicosulfuron as a follow-up herbicide after Paraquat application for weed control in sweet corn cultivation. The experiment was conducted at SPLPP Ciparay, Universitas Padjadjaran, from September to December 2021 using a factorial randomized block design with two factors and two replications. Paraquat was applied at doses of 0; 0.297; 0.594; and 0.891 kg ha⁻¹, followed by Nicosulfuron at 0; 0.080; 0.160; and 0.320 kg ha⁻¹. Observations included phytotoxicity, weed dry weight, crop growth, and yield. The results showed that Paraquat at 0.891 kg ha⁻¹ effectively suppressed total weed biomass, while Nicosulfuron at 0.160–0.320 kg ha⁻¹ controlled dominant weeds, particularly Paspalum conjugatum and Ageratum conyzoides. The combination of Paraquat 0.891 kg ha⁻¹ and Nicosulfuron 0.320 kg ha⁻¹ produced the lowest weed dry weight and highest sweet corn yield, indicating that sequential herbicide application is effective under NT systems.
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 Ervinda Yuliatin Galih Robby Wasesa Linda Oktavianingsih Marselina Yunita Pauline Elisabeth Rida Salsabila Siti Rahmadani Uum Umiyati Yayan Sumekar