Work

Attention-enhanced VGG-16 architecture for precision weed detection in wheat fields

Year: 2026

Type: article

Abstract: This research details five state of the art convolutional neural-network designs that use deep learning to classify wheat weeds. The dataset consists of greyscale photos taken in agricultural fields, enhanced with RGB lighting and cropped to 256×256×3 pixels. A VGG16-based model with further Conv2D layers and spatial attention is one of the five models suggested. Another is an architecture inspired by ResNet50 that uses residual blocks and global average pooling. For accurate wheat-weed classification, the study found that VGG16 is the best option because to its high level of generalizability and precision.

Source: International Journal of Informatics and Communication Technology (IJ-ICT)

DOI: 10.11591/ijict.v15i3.pp1303-1312

Authors: Akanksha Bodhale, Seema Verma, Aishwary Bodhale

Institutions: Banasthali University, University of the Cumberlands, National Institute of Technical Teachers’ Training and Research

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: diamond

OpenAlex ID: W4413034446

FWCI:

Cites: 48

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Related to: 10