Work

Detecting Infection in Corn Leaves Using YOLO26-Based Density Peaks Clustering

Year: 2026

Type: article

Abstract: Corn is widely used as a food and renewable energy source. Due to its sustainability, corn is used to produce various chemicals and serves as a popular substitute. However, maize leaf infection can reduce corn productivity. This study presents a novel method, YOLO26-SDW-DPC, to quickly detect and classify infected leaves. The YOLO26-SDW-DPC algorithm can be considered a replacement for stride convolution, using a spatial Distance-based Density Peaks Clustering (DPC) in the backbone to improve usability and adaptability. In addition, a deformable attention–based vision transformer is used.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.19106

Authors: Sunanda Yadla, Ulligaddala Srinivasarao

Institution: GITAM University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $431