Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2406.07716

Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification

Md. Mahmudul Hasan·SM Shaqib·Ms. Sharmin Akter·Rabiul Alam·Afraz Ul Haque·Shahrun akter khushbu
View original source ↗Read source document ↗Export metadata ↓

Abstract

The purpose of the Insect Detection System for Crop and Plant Health is to keep an eye out for and identify insect infestations in farming areas. By utilizing cutting-edge technology like computer vision and machine learning, the system seeks to identify hazardous insects early and accurately. This would enable prompt response to save crops and maintain optimal plant health. The Method of this study includes Data Acquisition, Preprocessing, Data splitting, Model Implementation and Model evaluation. Different models like MobileNetV2, ResNet152V2, Xecption, Custom CNN was used in this study. In order to categorize insect photos, a Convolutional Neural Network (CNN) based on the ResNet152V2 architecture is constructed and evaluated in this work. Achieving 99% training accuracy and 97% testing accuracy, ResNet152V2 demonstrates superior performance among four implemented models. The results highlight its potential for real-world applications in insect classification and entomology studies, emphasizing efficiency and accuracy. To ensure food security and sustain agricultural output globally, finding insects is crucial. Cutting-edge technology, such as ResNet152V2 models, greatly influence automating and improving the accuracy of insect identification. Efficient insect detection not only minimizes crop losses but also enhances agricultural productivity, contributing to sustainable food production. This underscores the pivotal role of technology in addressing challenges related to global food security.

Explore related subjects

cs.CV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Md. Mahmudul Hasan, SM Shaqib, Ms. Sharmin Akter, Rabiul Alam, Afraz Ul Haque, Shahrun akter khushbu. 2024-06-11. Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification. https://arxiv.org/abs/2406.07716

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series

Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing methodologies often face significant challenges, particularly in their reliance on extensive manual efforts for cloud-free data curation and limited adaptability to diverse global conditions. In this paper, we introduce PTAViT3D, a deep learning architecture specifically designed for processing three-dimensional time series of satellite imagery from either Sentinel-1 (S1) or Sentinel-2 (S2). Additionally, we present PTAViT3D-CA, an extension of the PTAViT3D model incorporating cross-attention mechanisms to fuse S1 and S2 datasets, enhancing robustness in cloud-contaminated scenarios. The proposed methods leverage spatio-temporal correlations through a memory-efficient 3D Vision Transformer architecture, facilitating accurate boundary delineation directly from preprocessed, cloud-affected imagery. We comprehensively validate our models through extensive testing on various datasets, including Australia's ePaddocks - CSIRO's national, continental-scale agricultural field boundary product covering Australia's cropping regions - alongside public benchmarks Fields-of-the-World, PASTIS, and AI4SmallFarms. Our results consistently demonstrate state-of-the-art performance, highlighting excellent global transferability and robustness. Crucially, our approach significantly simplifies data preparation workflows by reliably processing cloud-affected imagery, thereby offering strong adaptability across diverse agricultural environments. Our code and models are publicly available at https://github.com/feevos/tfcl.

cs.CV↗
arXiv2026

Copy-Move Forgery Detection and Question Answering for Remote Sensing Image

Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question Answering (RSCMQA) task. Unlike traditional Remote Sensing Visual Question Answering (RSVQA), RSCMQA focuses on interpreting complex tampering scenarios and inferring relationships between objects. We present a suite of global RSCMQA datasets, comprising images from 29 different regions across 14 countries. Specifically, we propose five distinct datasets, including the basic dataset RS-CMQA, the category-balanced dataset RS-CMQA-B, the high-authenticity dataset Real-RSCM, the extended dataset RS-TQA, and the extended category-balanced dataset RS-TQA-B. These datasets fill a critical gap in the field while ensuring comprehensiveness, balance, and challenging scenarios. Furthermore, we introduce a region-discrimination-guided multimodal copy-move forgery perception framework (CMFPF), which enhances the accuracy of answering questions about tampered images by leveraging prompts about the differences and connections between the source and tampered regions. Extensive experiments demonstrate that our method establishes a stronger benchmark for RSCMQA compared to general VQA and RSVQA models. Our datasets and code are publicly available at https://github.com/shenyedepisa/RSCMQA.

cs.CV↗
arXiv2026

SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection

Traffic sign detection is a challenging visual signal processing task for advanced driver assistance, where small objects, scale variation, and occlusion limit conventional detectors with fixed receptive fields. This paper proposes State-space Modeling and Dynamic Dual Fusion Network (SMDDFNet), a deep learning detector for traffic sign images. SMDDFNet integrates a Dynamic Dual Fusion (DDF) module and a state-space modeling backbone to enhance multi-scale feature representation. DDF combines efficient multi-scale attention with content-aware dynamic filtering in the frequency domain, while the backbone captures long-range dependencies with linear computational complexity. A multi-scale feature fusion neck further aggregates pyramid features for robust localization of small signs. Experiments on TT100K, GTSDB, PASCAL VOC, and the Roboflow~100 \emph{vehicle} subset show that SMDDFNet achieves competitive accuracy against recent detectors while retaining real-time throughput. The source code is available at https://github.com/rainbowyuyu/SMDDFNet

cs.CV↗