# Evaluation of Convolutional Neural Network For Image Classification with Agricultural and Urban Datasets

Source: [Evaluation of Convolutional Neural Network For Image Classification with Agricultural and Urban Datasets](<https://www.sota2.com/research/paper/695c96e353fdd5c361743414>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-07T05:00:04.582Z

Authors: Shamik Shafkat Avro, Nazira Jesmin Lina, Shahanaz Sharmin

Published: 2026-01-04T06:09:49.000Z

## Abstract

This paper presents the development and evaluation of a custom Convolutional Neural Network (CustomCNN) created to study how architectural design choices affect multi-domain image classification tasks. The network uses residual connections, Squeeze-and-Excitation attention mechanisms, progressive channel scaling, and Kaiming initialization to improve its ability to represent data and speed up training. The model is trained and tested on five publicly available datasets: unauthorized vehicle detection, footpath encroachment detection, polygon-annotated road damage and manhole detection, MangoImageBD and PaddyVarietyBD. A comparison with popular CNN architectures shows that the CustomCNN delivers competitive performance while remaining efficient in computation. The results underscore the importance of thoughtful architectural design for real-world Smart City and agricultural imaging applications.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.01393>)

[PDF](<https://arxiv.org/pdf/2601.01393.pdf>)