AgriDeep-net: An advanced deep feature fusion-based technique for enhanced fine-grain image analytics in precision agriculture

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197022" target="_blank" >RIV/00216305:26220/26:0197022 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1574954125000780" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1574954125000780</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecoinf.2025.103069" target="_blank" >10.1016/j.ecoinf.2025.103069</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AgriDeep-net: An advanced deep feature fusion-based technique for enhanced fine-grain image analytics in precision agriculture

Popis výsledku v původním jazyce

With the vast diversity and rapidly evolving nature of agricultural landscapes, the need for cutting-edge technological solutions has become increasingly apparent. Addressing the complex challenges of fine-grained agricultural image classification, AgriDeep-Net is introduced as an innovative multi-model deep-learning framework, strategically incorporating advanced techniques to navigate complexities in the field. This precision-driven methodology distinguishes AgriDeep-Net, offering a strategic approach to extract salient and discriminative features from diverse deep-learning models involving highly similar agricultural images marked by low inter-class visual discrimination. Each model is characterized by unique architectural configurations, enabling strategic feature fusion that empowers AgriDeep-Net to capture nuanced semantic information within multi-class agricultural images. The framework adeptly manages the hurdles posed by uneven data distribution, intra-class diversity, and the demands of multi-class classification. Rigorous experimentation underscores AgriDeep-Net's exceptional performance, achieving a testing accuracy of 93.29 % for the ACHENY dataset and an even more impressive 98.44 % for the Indian Basmati seeds dataset. Benchmarking against state-of-the-art deep neural networks, AgriDeep-Net proves its efficacy across diverse datasets collected under real-world and controlled conditions. This framework stands out as a beacon of efficiency and accuracy, eliminating the need for extensive image pre-processing operations and showcasing its potential to empower farmers with precision tools for optimizing crop yields, resource allocation, and swift responses to emerging agricultural challenges.

Název v anglickém jazyce

AgriDeep-net: An advanced deep feature fusion-based technique for enhanced fine-grain image analytics in precision agriculture

Popis výsledku anglicky

With the vast diversity and rapidly evolving nature of agricultural landscapes, the need for cutting-edge technological solutions has become increasingly apparent. Addressing the complex challenges of fine-grained agricultural image classification, AgriDeep-Net is introduced as an innovative multi-model deep-learning framework, strategically incorporating advanced techniques to navigate complexities in the field. This precision-driven methodology distinguishes AgriDeep-Net, offering a strategic approach to extract salient and discriminative features from diverse deep-learning models involving highly similar agricultural images marked by low inter-class visual discrimination. Each model is characterized by unique architectural configurations, enabling strategic feature fusion that empowers AgriDeep-Net to capture nuanced semantic information within multi-class agricultural images. The framework adeptly manages the hurdles posed by uneven data distribution, intra-class diversity, and the demands of multi-class classification. Rigorous experimentation underscores AgriDeep-Net's exceptional performance, achieving a testing accuracy of 93.29 % for the ACHENY dataset and an even more impressive 98.44 % for the Indian Basmati seeds dataset. Benchmarking against state-of-the-art deep neural networks, AgriDeep-Net proves its efficacy across diverse datasets collected under real-world and controlled conditions. This framework stands out as a beacon of efficiency and accuracy, eliminating the need for extensive image pre-processing operations and showcasing its potential to empower farmers with precision tools for optimizing crop yields, resource allocation, and swift responses to emerging agricultural challenges.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20701 - Environmental and geological engineering, geotechnics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Ecological Informatics

ISSN

1574-9541

e-ISSN

1878-0512

Svazek periodika

86

Číslo periodika v rámci svazku

103069

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

001428390400001

EID výsledku v databázi Scopus

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